Institute for Mathematics and its Applications University of Minnesota 114 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20072008 Program
See http://www.ima.umn.edu/20072008 for a full description of the 20072008 program on Mathematics of Molecular and Cellular Biology.
This two week workshop at the IMA, organized by L. Mahadevan, Engineering and Applied Sciences, Harvard University, Edward A. Spiegel, Astronomy, Columbia University, Thomas A. Witten, Physics, University of Chicago, and Wendy Zhang, Physics, University of Chicago, will focus on what we know and what we would like to know about these types of singular structures; with the first week focusing on fluid singularities, and the second on elastic singularities. We also expect to have an intense midweek minitutorial on singularities in geometric field theories: examples from gravitation to condensed matter.
Complex Fluids and Complex Flows: The preliminary program for the IMA Thematic year 20092010 on Complex Fluids and Complex Flows is now available on line.
2:00p3:00p  Prescribing thermal forcing for physical systems  Peter R. Kramer (Rensselaer Polytechnic Institute)  Lind Hall 409  WSSTM 
11:15a12:15p  A model for transfer phenomena in structured populations  Peter Hinow (University of Minnesota)  Lind Hall 409  PS 
11:15a12:15p  Modeling the genomewide transient response to stimuli in yeast: adaptation through integral feedback  Claudio Altafini (International School for Advanced Studies (SISSA/ISAS))  Lind Hall 409  MMCB 
All Day  Introductory lectures  SW5.1113.08  
12:00p1:00p  Registration and coffee  EE/CS 3176  SW5.1113.08  
1:00p1:15p  Welcome and introduction  Chehrzad Shakiban (University of Minnesota)  EE/CS 3180  SW5.1113.08 
1:15p2:15p  Opening plenary talk: Stochastic analysis is the fundamental tool for understanding biological function  Michael C. Reed (Duke University)  EE/CS 3180  SW5.1113.08 
2:15p2:30p  Coffee  EE/CS 3176  SW5.1113.08  
2:30p3:30p  Models and measures of virus growth and infection spread  John Yin (University of Wisconsin)  EE/CS 3180  SW5.1113.08 
3:30p4:00p  Coffee  EE/CS 3176  SW5.1113.08  
4:00p5:00p  Analyzing stochastic models  Thomas G. Kurtz (University of Wisconsin)  EE/CS 3180  SW5.1113.08 
5:00p5:30p  Second chances  EE/CS 3180  SW5.1113.08  
6:30p8:30p  Workshop dinner  Caspian Bistro 2418 University Ave SE Minneapolis, MN 55414 6126231133 
SW5.1113.08 
8:30a9:00a  Coffee  EE/CS 3176  SW5.1113.08  
9:00a10:00a  Subdiffusion and reaction networks in biophysics  Samuel Kou (Harvard University)  EE/CS 3180  SW5.1113.08 
10:00a10:30a  Coffee  EE/CS 3176  SW5.1113.08  
10:30a11:30a  Fundamental limits on the suppression of randomness in biology  Johan Paulsson (Harvard Medical School)  EE/CS 3180  SW5.1113.08 
11:30a1:15p  Lunch  EE/CS 3176  SW5.1113.08  
1:15p2:45p  An introduction to discreteevent simulation  Peter W. Glynn (Stanford University) Peter J. Haas (IBM Research Division)  EE/CS 3180  SW5.1113.08 
2:45p3:15p  Coffee  EE/CS 3176  SW5.1113.08  
3:15p3:45p  Part I: Local and global stability of biochemical reaction network dynamics  Gheorghe Craciun (University of Wisconsin)  EE/CS 3180  SW5.1113.08 
3:50p4:20p  Part II: Homotopy methods for counting reaction network equilibria  Ruth J. Williams (University of California, San Diego)  EE/CS 3180  SW5.1113.08 
4:20p4:50p  Second chances  EE/CS 3180  SW5.1113.08  
4:50p5:00p  Group photo  SW5.1113.08  
5:00p6:30p  Reception and Poster Session  Lind Hall 400  SW5.1113.08  
Product form stationary distributions for deficiency zero chemical reaction networks  David F. Anderson (University of Wisconsin)  
Solutions to inverse problems of biochemical networks using stochastic methods  Mónica F. Bugallo (SUNY) Petar M. Djuric (SUNY)  
Interlaced Euler scheme for stiff stochastic differential equations  Ioana Cipcigan (University of Maryland Baltimore County) Muruhan Rathinam (University of Maryland Baltimore County)  
Stochastic simulations of reactiondiffusion models  Anilkumar Devarapu (University of Louisville)  
Simulating discrete biochemical reaction systems  Arnab Ganguly (University of Wisconsin)  
Multiscale methods in a heat shock response model  HyeWon Kang (University of Wisconsin)  
Stochastic models for intracellular reaction networks  Yiannis N. Kaznessis (University of Minnesota)  
Stochastic control analysis for biological reaction systems  Kyung Hyuk Kim (University of Washington)  
Predicting translation rate from sequence  Howard Salis (University of California)  
Stochastic modeling of vesicular stomatitis virus(VSV) growth in cells: An application of order statistics to predict replication delays  Rishi Srivastava (University of Wisconsin)  
Stochastic modeling of bistable chemical systems: Schlogl's model  Melissa Vellela (University of Washington)  
Biochemical and network modeling of the mammalian suprachiasmatic nucleus  Richard Yamada (University of Michigan) 
8:15a8:45a  Coffee  EE/CS 3176  SW5.1113.08  
8:45a9:45a  Stochastic oscillations in small genetic networks  Lev S. Tsimring (University of California, San Diego)  EE/CS 3180  SW5.1113.08 
9:45a10:45a  Multiscale models for synthetic biology  Yiannis N. Kaznessis (University of Minnesota)  EE/CS 3180  SW5.1113.08 
10:45a11:15a  Coffee  EE/CS 3176  SW5.1113.08  
11:15a12:15p  Stochastic aspects of actin filament dynamics  Hans G. Othmer (University of Minnesota)  EE/CS 3180  SW5.1113.08 
12:15p1:45p  Lunch  EE/CS 3180  SW5.1113.08  
1:45p2:45p  Bayesian inference for stochastic intracellular reaction network models  Darren James Wilkinson (University of Newcastle upon Tyne)  EE/CS 3180  SW5.1113.08 
2:45p3:45p  Inferring an underlying reaction network from the data  Grzegorz A. Rempala (University of Louisville)  EE/CS 3180  SW5.1113.08 
3:45p4:15p  Coffee  EE/CS 3176  SW5.1113.08  
4:15p4:45p  Second chances  EE/CS 3180  SW5.1113.08  
4:45p5:45p  Closing plenary talk: Metabolic engineering and metabolic modeling: where do we go from here?  James C. Liao (University of California)  EE/CS 3180  SW5.1113.08 
11:15a12:15p  Some mathematical issues arising in single and multiple target SELEX  Howard A. Levine (Iowa State University)  Lind Hall 409  MMCB 
2:00p3:00p  Theoretical framework for microscopic osmotic phenomena  Peter R. Kramer (Rensselaer Polytechnic Institute)  Lind Hall 409  WSSTM 
11:15a12:15p  The cohesin ring is required for centrosome integrity  Duncan J. Clarke (University of Minnesota)  Lind Hall 409  PS 
11:15a12:15p  Anomalous diffusion, tumor growth and random walk models  Sergei Fedotov (University of Manchester)  Lind Hall 409  MMCB 
2:00p3:00p  Classification of solution to a nonlinear biharmonic equation with negative exponent  YungSze Choi (University of Connecticut)  Lind Hall 409 
All Day  Memorial Day. The IMA is closed. 
8:15a9:00a  Registration and coffee  EE/CS 3176  W5.2730.08  
9:00a9:15a  Welcome to the IMA  Douglas N. Arnold (University of Minnesota)  EE/CS 3180  W5.2730.08 
9:15a10:05a  Actin and aactinin orchestrate the assembly and maturation of nascent adhesions in a myosin II motor independent manner  Rick Horwitz (University of Virginia)  EE/CS 3180  W5.2730.08 
10:05a10:35a  Coffee  EE/CS 3176  W5.2730.08  
10:35a11:25a  Modular control model for endothelial sheet migration  Tobias Meyer (Stanford University)  EE/CS 3180  W5.2730.08 
11:25a2:00a  Lunch  W5.2730.08  
2:00p2:50p  New methods to study motile phenomena  Ken Jacobson (University of North Carolina)  EE/CS 3180  W5.2730.08 
2:50p3:05p  Group Photo  W5.2730.08  
3:05p3:35p  Coffee  EE/CS 3176  W5.2730.08  
3:35p4:25p  Actin organization at the cell edge: mechanism for formation of lamellipodium lamellum interface  Michael M. Kozlov (Tel Aviv University)  EE/CS 3180  W5.2730.08 
4:30p6:30p  Reception and Poster Session  Lind Hall 400  W5.2730.08  
A robustly wrong Listeria motility model, and its redemption  Jonathan B. Alberts (University of Washington)  
A stochastic immersed boundary method incorporating thermal fluctuations: Coarsegrained micromechanics  Paul Atzberger (University of California)  
Dynamic contractile Factin cortex during cell shape change and morphogenesis  Lance Davidson (University of Pittsburgh)  
Intracellular polarization of motile cells  Adriana Dawes (University of Washington)  
Cell motility as persistent random motion: theories from experiments  Henrik Flyvbjerg (Technical University of Denmark)  
Factin dynamics regulate force transmission at focal adhesions  Margaret Gardel (University of Chicago)  
Microtubule assembly dynamics at the nanoscale  Melissa K. Gardner (University of Minnesota)  
Coupling the cytoskeleton to the membrane: driving dynamic cellular shape transitions  Nir Shachna Gov (Weizmann Institute of Science)  
Dynamic multivariate analysis of single cell motility  Mark Harris (Vanderbilt University) Eric Kim (Vanderbilt University)  
Mathematical and computational tools for cellmechanobiology  Dirk Hartmann (RuprechtKarlsUniversität Heidelberg)  
Simulation of concentrationdependent contraction of cell  Pilhwa Lee (University of Colorado)  
Model for protein concentration gradients in the cytoplasm  Karen Lipkow (University of Cambridge) David Odde (University of Minnesota)  
Smoldyn, a novel simulator for cellular systems biology  Karen Lipkow (University of Cambridge)  
Quantitative analysis of stream formation during dictyostelium chemotaxis  Wolfgang Losert (University of Maryland)  
Cellular dynamics simulations of MCF10A cell random migration in 2D  Alka A. Potdar (Vanderbilt University)  
Modeling cell rheology: microrheology of viscoelastic networks  Sebastian Ambrose Sandersius (Arizona State University)  
A continuum model of the cytoskeleton dynamics in lamellipodia  Christian Schmeiser (Universität Wien)  
TenascinC is upregulated at the end of the cell cycle in proliferating NIH 3T3 fibroblasts  Benjamin L. Stottrup (Augsburg College)  
Spatiotemporal modelling of intracellular signalling in Rhodobacter sphaeroidess  Marcus John Tindall (University of Oxford)  
Chemotaxis of E. coli towards glucose  Rajitha Vuppula (Indian Institute of TechnologyBombay)  
Automated characterization of cell shape changes during amoeboid motility  Yuan Xiong (Johns Hopkins University)  
The minimal molecular surface  Shan Zhao (University of Alabama)  
A Multiscale model of endothelial cell migration, proliferation and maturation in angiogenesis  Xiaoming Zheng (University of Michigan)  
From the curve straightening flow in the plane to a model for the Lamellipodial Actincytoskeleton  Dietmar Bernhard Ölz (Universität Wien) 
8:30a9:00a  Coffee  EE/CS 3176  W5.2730.08  
9:00a9:50a  Computational modeling of invadopodiaECM interactions  Alissa Weaver (Vanderbilt University)  EE/CS 3180  W5.2730.08 
9:55a10:45a  Effects of nonequilibrium processes on actin dynamics and force generation  Anders E. Carlsson (Washington University)  EE/CS 3180  W5.2730.08 
10:45a11:15a  Coffee  EE/CS 3176  W5.2730.08  
11:15a12:05p  Developmental regulation of cell motility  Denise Montell (Johns Hopkins University)  EE/CS 3180  W5.2730.08 
12:05p2:00p  Lunch  W5.2730.08  
2:00p2:50p  Adherent cells as a mechanical device  probing the forces and understanding the regulatory circuit  Yuli Wang (University of Massachusetts Medical School, Worcester Campus)  EE/CS 3180  W5.2730.08 
2:55p3:45p  Analysis of actin FLAP dynamics in the Leading lamella  Micah Dembo (Boston University)  EE/CS 3180  W5.2730.08 
3:45p4:15p  Coffee  EE/CS 3176  W5.2730.08  
4:15p4:45p  Second Chances  EE/CS 3180  W5.2730.08  
7:00p8:00p  Informal sessions (for people who have energy and will to present and discuss some narrower topics. That would be selforganized, like 2nd chances).  EE/CS 3180  W5.2730.08 
8:30a9:00a  Coffee  EE/CS 3176  W5.2730.08  
9:00a9:50a  Signal propagation during chemotaxis  Erin Rericha (University of Maryland)  EE/CS 3180  W5.2730.08 
9:55a10:45a  Modeling chemotactic gradient sensing, polarization and motility in Dictyostelium discoideum  Pablo A. Iglesias (Johns Hopkins University)  EE/CS 3180  W5.2730.08 
10:45a11:15a  Coffee  EE/CS 3176  W5.2730.08  
11:15a12:05p  Novel signal pathways in tumor cell chemotaxis  John S. Condeelis (Albert Einstein College of Medicine)  EE/CS 3180  W5.2730.08 
12:05p2:00p  Lunch  W5.2730.08  
2:00p2:50p  Chemotactic cell movement during Dictyostelium development and chick gastrulation  Cornelius Weijer (University of Dundee)  EE/CS 3180  W5.2730.08 
2:55p3:45p  Biochemical regulation of cell polarization and actinbased cell motility  Leah EdelsteinKeshet (University of British Columbia)  EE/CS 3180  W5.2730.08 
3:45p4:15p  Coffee  EE/CS 3176  W5.2730.08  
4:15p4:45p  Second Chances  EE/CS 3180  W5.2730.08  
6:30p8:30p  Workshop dinner at Pagoda in Dinkytown  Pagoda 1417 4th St. SE Minneapolis, MN 6123784710 
W5.2730.08 
8:30a9:00a  Coffee  EE/CS 3176  W5.2730.08  
9:00a9:50a  Bacterial chemotaxis: Sophisticated behavior from simple circuitry  John Sandy Parkinson (University of Utah)  EE/CS 3180  W5.2730.08 
9:55a10:45a  The chemotaxis receptor cluster revisited  Dennis Bray (University of Cambridge)  EE/CS 3180  W5.2730.08 
10:45a11:15a  Coffee  EE/CS 3176  W5.2730.08  
11:15a12:05p  Inferring cellular response to a small stimulus from noise measurements in nonstimulated cells  Philippe Cluzel (University of Chicago)  EE/CS 3180  W5.2730.08 
12:05p2:00p  Lunch  W5.2730.08  
2:00p2:50p  From molecule to behavior: E. coli’s memory, computation and taxis  Yuhai Tu (IBM)  EE/CS 3180  W5.2730.08 
2:55p3:25p  Second Chances + Closing Discussion  EE/CS 3180  W5.2730.08 
Jonathan B. Alberts (University of Washington)  A robustly wrong Listeria motility model, and its redemption 
Abstract: Our experimental study of the effect of ActA distribution on the motility of L. monocytogenes indicates that speed is positively correlated with both ActA intensity (amount) and polarity. The agentbased model we used to explore this behavior (from Alberts and Odell 2004) robustly demonstrates the opposite (i.e wrong) polarity relationship with speed. We initially judged this failure as an indication that model parameters, such as the those governing the ActAactin filament bonds, were "out of tune". Extensive (though not exhaustive) searches in parameter space failed to yield solutions in agreement with experiment. We were ultimately led to the conclusion that our model was topologically incorrect a mechanistic behavior was improperly represented, or missing altogether. The model is redeemed by inclusion of a cooperative (rather than linearly superposed) restraining force, specifically the explicit frictional drag of filaments on the bacterial surface. The redeemed model robustly captures target experimental behaviors. In light of this experience we reflect on the likely nature of complex dynamical systems in biology and our attempts to represent them in silico. Additionally, we present a onedimensional partialdifferential model (state variables: barbedends, actin density, speed of motion) that is instructive as a mathematical synopsis of the agentbased simulation, but fails to demonstrate sensitivity to the details that make all the difference in the complex model (and presumably in the biology).  
Claudio Altafini (International School for Advanced Studies (SISSA/ISAS))  Modeling the genomewide transient response to stimuli in yeast: adaptation through integral feedback 
Abstract: At the level of gene expression, the response of yeast to various types of stresses/perturbations is characterized by a shortterm transient followed by a return to the basal level (adaptation). A thorough investigation of the transient response to several different stimuli shows a common modulation, functionally and dynamically similar. The adaptation that follows the transient excursion is modeled by means of an integral feedback, with the gene product representing the variable that is fed back. The resulting linear system with input explains sufficiently well the different time constants observable in the transient response while being in agreement with the known experimental degradation rates measurements.  
David F. Anderson (University of Wisconsin)  Product form stationary distributions for deficiency zero chemical reaction networks 
Abstract: The dynamics of chemical reaction networks can be modeled either deterministically or stochastically. The deficiency zero theorem for deterministically modeled systems gives conditions under which a unique equilibrium value with strictly positive components exists within each stoichiometric compatibility class (linear subset of Euclidean space in which trajectories are bounded). The conditions of the theorem actually imply the stronger result that there exist concentrations for which the network is "complex balanced." That observation in turn implies that the standard stochastic model for the reaction network has a product form stationary distribution.  
Paul Atzberger (University of California)  A stochastic immersed boundary method incorporating thermal fluctuations: Coarsegrained micromechanics 
Abstract: The immersed boundary method is a modeling approach traditionally applied to macroscopic systems involving flexible elastic structures which interact with a fluid flow. To use such methods for microscopic systems requires at sufficiently small length scales that thermal fluctuations be taken into account. In this poster we discuss an extension of the immersed boundary method to incorporate thermal fluctuations of both the mechanical structures and the surrounding fluid. The formalism gives a set of stiff SPDE's for which standard numerical approaches perform poorly. We present alternative stochastic numerical methods to integrate the systems of equations. We then discuss how the methods can be used in practice and present results for simulations of a coarse grained model of a lipid bilayer membrane and a molecular motor protein.  
Dennis Bray (University of Cambridge)  The chemotaxis receptor cluster revisited 
Abstract: The cluster of receptors and associated proteins at the 'front end' of the E. coli chemotaxis pathway is a paradigm for membrane complexes in cells. Like focal adhesions and synapses, it acts as a solidstate computational device that amplifies, integrates, and parses chemical signals from the environment and relays the output to the rest of the cell. Ten years ago we proposed a structure for this receptor cluster and suggested how it might provide a basis for the very high sensitivity of cells to certain attractants. In this talk I will revisit the lattice architecture in the light of recent findings and present a radically different mechanism for its amplification. The new model is more firmly based on known molecular events and gives a better understanding of how the cell responds to mixed signals.  
Mónica F. Bugallo (SUNY), Petar M. Djuric (SUNY)  Solutions to inverse problems of biochemical networks using stochastic methods 
Abstract: Advances in the development of models that can satisfactorily describe biochemical networks are extremely valuable for understanding life processes. In order to get full description of such networks, one has to solve the inverse problem, that is, estimate unknowns (rates and populations of various species) or choose models from a set of hypothesized models using experimental data. In this work we discuss signal processing techniques for resolving the inverse problem of biochemical networks using a stochastic approach based on Bayesian theory. The proposed methods are tested in simple scenarios and the results are promising and suggest application of these methods to more complex networks.  
Anders E. Carlsson (Washington University)  Effects of nonequilibrium processes on actin dynamics and force generation 
Abstract: The talk will address two aspects of the connection between nonequilibrium molecularlevel processes and properties of actin related to cell motility. 1) The effects of ATP hydrolysis on force generation and polymerization dynamics. Using an extended Brownianratchet methodology, it is shown that hydrolysis of bound nucleotide can reduce the stall force of ensembles of actin filaments by a factor of three or more relative to the thermodynamic stall force. The force reduction occurs because the interaction with the obstacle induces hydrolysis at the tip, which converts filament tips temporarily to a depolymerizing state. It is also shown that hydrolysis can lead to overshoots in actin polymerization traces. The overshoots occur when the characteristic time of polymerization is shorter than the nucleotide exchange time on monomers. This effect may be present in cells with sufficiently high concentrations of free barbed ends. 2) The effects of network structure on the contractile stress generated by active myosin II interacting with actin filaments. Using elasticity theory combined with an effectivemedium theory, it is shown that the stress generated per myosin is proportional to the average filament length. This leads to estimates of the cytokinesis tension that are much lower than existing estimates. unless the filaments are bundled into inextensible units longer than a filament. Continued contraction requires treadmilling of actin filaments, and the rate of contraction is limited by the filament treadmilling rate.  
YungSze Choi (University of Connecticut)  Classification of solution to a nonlinear biharmonic equation with negative exponent 
Abstract: We study global positive smooth solutions of the geometrically
interesting
equation: Δ^{2}u + u^{−q} = 0 with q > 0 in R^{3}. When q = 7, it corresponds to some kind of critical index.
When 0 < q ≥ 1, there is no solution, while when q > 1, there is solution with superlinear growth as x → ∞. Restricted to linear growth solution, there is no solution for 1 < q < 7. When q = 7, all solutions are radially symmetric. Up to a translation and a dilation, such solution is unique. 

Ioana Cipcigan (University of Maryland Baltimore County), Muruhan Rathinam (University of Maryland Baltimore County)  Interlaced Euler scheme for stiff stochastic differential equations 
Abstract: In deterministic as well as stochastic models of chemical kinetics, stiff systems, i.e. systems with vastly different time scales where the fast scales are stable, are very common. It is well known that the implicit Euler method is well suited for stiff deterministic equations (modeled by ODEs) while the explicit Euler is not. In particular once the fast transients are over, the implicit Euler allows for the choice of times steps comparable to the slowest time scale of the system. In stochastic systems (modeled by SDEs) the picture is more complex. While the implicit Euler has better stability properties over the explicit Euler, it underestimates the stationary variance. In general one may not expect any method to work successfully by taking time steps of the order of the slowest time scale. We explore the idea of interlacing large implicit Euler steps with a sequence of small explicit Euler steps. In particular we present our study of a linear test system of SDEs and demonstrate that such interlacing could effectively deal with stiffness. We also show uniform convergence of mean and variance.  
Duncan J. Clarke (University of Minnesota)  The cohesin ring is required for centrosome integrity 
Abstract: Cohesin is a protein complex which forms a ring structure involved in tethering newly replicated DNA molecules until chromosome segregation in mitosis. We present the unexpected finding that Cohesin Rings are, in addition, needed for the integrity of the centrosome, an organelle that organizes the nucleation of microtubules to form the mitotic spindle. Time lapse microscopy of centrosomes and spindles in living cells revealed that in cells lacking Cohesin, the centrosomes become split during mitosis forming supernumerary spindle poles. This causes the chromosomes to segregate unevenly, leading to aneuploidy. We conclude that the Cohesin Ring is needed for the integrity of the centrosome during mitosis and therefore for proper chromosome segregation.  
Philippe Cluzel (University of Chicago)  Inferring cellular response to a small stimulus from noise measurements in nonstimulated cells 
Abstract: In recent years, there have been significant efforts to characterize the noise associated with the regulation of intracellular processes. Intrinsic fluctuations are often due to the small number of molecules that govern these processes. However, noise is not always caused by a low number of molecules; the regulatory network itself can cause large fluctuations in the number of signaling molecules. For example, systems that have an ultrasensitive inputoutput relationship can be effective sources of noise because they not only amplify the input signal but also the associated noise itself. In this picture, systems that are more sensitive to intrinsic spontaneous noise also would be more sensitive to small external perturbations. In linear theory, it is common to analyze, at the equilibrium, spontaneous fluctuations (i.e. noise) in the system in order to predict the dynamical response to a small external perturbation. Here, we extend this simple mathematical framework to infer the cellular response to a small external stimulus by analyzing the noise of nonstimulated cells. We present a combination of experiments on and models of bacterial chemotaxis in E. coli that demonstrate the existence of such a relationship in living cells.  
Gheorghe Craciun (University of Wisconsin)  Part I: Local and global stability of biochemical reaction network dynamics 
Abstract: Local and global stability of biochemical reaction network Dynamics Gheorghe Craciun Modern biological research provides countless examples of biochemical interaction networks. For example, at the intracellular level, the nodes of these interaction networks could be signaling molecules, genes, and gene products. In order to understand the role played by some of these interactions one often faces great difficulties in trying to interpret the effect of positive and negative feedbacks, nonlinear interactions, and other complex signaling between the nodes of the network. We will analyze the connections between reaction network structure and the capacity for complex dynamic behavior. These connections may play an important role in facilitating the understanding of experimental data.  
Lance Davidson (University of Pittsburgh)  Dynamic contractile Factin cortex during cell shape change and morphogenesis 
Abstract: Joint work with Hye Young Kim and Robert Weaver (University of Pittsburgh, Department of Bioengineering, Pittsburgh PA 15260). Three distinct tissues from the early frog embryo contain either cells that exhibit either no translational movement, monopolar directed cell migration, or mediolateral cell intercalation. Interestingly, while each cell type exhibits different patterns of cell protrusions they manifest the same stereotypical periodic pattern of Factin contractions within the pericellular cortex. Contractions form in less than a minute as cortex covering a few square micrometers appears to contract toward a focal point. As contraction progresses the Factin network increases in density at which point the network appears to depolymerize back to the initial background state. The timecourse of contraction followed by depolymerization takes less than two minutes. In order to understand the formation and disassembly of these Factin contractions we are combining experiments to disrupt or activate the actomyosin cortex with a simple model that captures the basic dynamics of twodimensional actomyosin networks in the cell cortex. Our model is based on a mechanical representation of myosin II minifibrils distributed within a network of polarized actin filaments and appears to capture the initial phase of contraction but not the later disassembly phase of Factin contractions seen in vivo. Combined analysis of actin dynamics and modeling efforts are needed to understand the complex connections between the molecular mechanisms controlling subcellular mechanics and how these processes shape cells and tissues during development.  
Adriana Dawes (University of Washington)  Intracellular polarization of motile cells 
Abstract: To move in a persistent, directed manner, motile cells must break symmetry and initiate movement that is maintained even after the stimulus is removed. In this poster, I will present a model of intracellular polarization in crawling cells that couples biochemical dynamics with spatially directed force generation. This biologically based model is able to reproduce a number of experimentally observed features including consistent responses to stimuli of varying strength and spontaneous polarization. I will discuss how polarization in other cell types will inform future directions of this model.  
Micah Dembo (Boston University)  Analysis of actin FLAP dynamics in the Leading lamella 
Abstract: Joint work with Igor R. Kuznetsov and Marc Herant. The transport of labeled Gactin from the midlamella region to the leading edge in a highly motile malignant rat fibroblast line has recently been studied using fluorescence localization after photo bleaching or FLAP (see Zicha et al.[Zicha2003]). The transit times recorded in these experiments were so fast, that simple diffusion was deemed an insufficient explanation. Since this conclusion has been controversial we here we reexamine the ZichaFLAP experiments using a twophase reactive interpenetrating flow formalism to model the cytoplasm and the transport dynamics of bleached and unbleached actin in a moving cell. This new analysis reveals a mechanism that can realistically explain the timing and the amplitude of all the observed FLAP signals in the Zichaexperiments without invoking special transport modalities. The proposed mechanism requires the existence of a small compartment at the leading edge of the lamella where actin polymerization is very fast and where this production is balanced by equally fast mechanical dilatation of Factin caused by retrograde flow away from the leading edge. If our dilatation hypothesis is correct, the FLAP technique constitutes a novel and very sensitive probe of actin dynamics in a crucial leading edge environment which is otherwise very difficult to interrogate.  
Anilkumar Devarapu (University of Louisville)  Stochastic simulations of reactiondiffusion models 
Abstract: ReactionDiffusion models are key components of models in developmental biology. These reaction diffusion processes can be mathematically modelled using either deterministic partial differential equations or stochastic simulation algorithms. Here we discuss the stochastic simulations on both linear and nonlinear Reactiondiffusion models  
Leah EdelsteinKeshet (University of British Columbia)  Biochemical regulation of cell polarization and actinbased cell motility 
Abstract: I survey our recent work on assembling the modular function and dynamics of signaling casettes that regulate actinbased motility. Arp2/3mediated branching of actin filaments is regulated by small GTPases of the Rho family. These are modulated by phosphoinositides. Together, such signalling agents determine "front vs rear" in a stimulated cell, where new actin filament ends are nucleated or uncapped inside the cell, and where protrusion or myosin contractility will occur. I describe how we explored such biochemical dynamics in both 1D and 2D cell motility models, and how we analysed their essential features in reduced mathematical caricatures. This work is joint with AFM Maree, AT Dawes, A Jilkine, Y Mori, and V Grieneisen. It is supported by NSERC, NSF, and MITACS.  
Sergei Fedotov (University of Manchester)  Anomalous diffusion, tumor growth and random walk models 
Abstract: The theory of anomalous diffusion is wellestablished and leads to the integral equations or the alternative fractional diffusion equations for number densities. Despite the progress in understanding the anomalous transport most work has been concentrated on the passive density of the particles, and comparatively little is known about the interaction of non standard transport with reactions. This work is intended to address this issue by utilising the random walk techniques in order to model the anomalous diffusion with reactions. Example is the tumor's cells migration and proliferation (Phys. Rev. Lett. 98, 118101 (2007)).  
Henrik Flyvbjerg (Technical University of Denmark)  Cell motility as persistent random motion: theories from experiments 
Abstract: Experimental time series for trajectories of motile cells may contain so much information that a systematic analysis will yield celltypespecific motility models. We show how here, using human keratinocytes and fibroblasts as examples. The two models that result, seem to reflect the cells' different roles in the organism, and show that a cell has a memory of past velocities. The models distinguish quantitatively between various surfaces' compatibility with the two cell types.  
Arnab Ganguly (University of Wisconsin)  Simulating discrete biochemical reaction systems 
Abstract: The stochastic simulation algorithm, or Gillespie Algorithm, is a tool used to simulate discrete biochemical reaction systems when there are a small to moderate number of molecules. The Gillespie Algorithm has been adapted to handle systems with delays (such as with gene transcription and translation) and approximations (such as tau leaping) have been developed in order to increase computation speed. In this poster we will model discrete biochemical systems via a random time change representation. We will then demonstrate how natural and efficient modifications of the stochastic simulation algorithm arise from such a representation. Also, we will use this representation to show how to incorporate postleap checks in the tauleaping algorithm.  
Margaret Gardel (University of Chicago)  Factin dynamics regulate force transmission at focal adhesions 
Abstract: The ability of cells to spatially and temporally regulate traction forces on their extracellular matrix is fundamental to tissue morphogenesis and directed cell migration. Forces generated in the actin filament (Factin) cytoskeleton are transmitted through the cell plasma membrane to the extracellular matrix via mechanosensitive focal adhesions1. In migrating cells, Factin and focal adhesions exhibit stereotypical patterns of assembly, disassembly and motion28. It is well appreciated that an intact Factin cytoskeleton is required for cellular force generation; however, the role of Factin motion dynamics in force generation is unknown. We show here that Factin motion spatially correlates with traction stresses on the extracellular matrix. Near the cell edge, traction stress and Factin speed are inversely correlated, suggesting that focal adhesions strengthen by slowing Factin and engaging it to the stationary extracellular matrix. However, instead of observing maximal traction stress when Factin motion is minimized, we find that an intermediate speed of Factin motion marks a switch to focal adhesion weakening. The switch from focal adhesion strengthening to weakening is not correlated with focal adhesion protein density, age, stress magnitude, assembly/disassembly status or subcellular position. In contrast, the Factin speed associated with maximal traction stress and the transition to adhesion weakening is strikingly robust. Thus, we identify Factin motion dynamics as an important regulator of focal adhesionmediated traction forces at the leading edge of migrating cells.  
Melissa K. Gardner (University of Minnesota)  Microtubule assembly dynamics at the nanoscale 
Abstract: Joint work with Henry T. Schek^{2}, Alan
J. Hunt^{2}, and David J.
Odde^{1}.
Microtubule ends are intrinsically unstable, and although this
property is critical for establishing cellular morphology and
motility, the molecular basis of assembly remains unclear. Our
3D mechanochemical model for microtubule dynamic instability
predicts that a spatially extended GTP cap allows for highly
variable growth dynamics, and specifically that substantial
nanoscale shortening of the microtubule tip could occur during
a microtubule growth phase. Here we use optical tweezers to
track microtubule polymerization against microfabricated
barriers, permitting unprecedented spatial resolution. We find
that microtubules exhibit extensive nanometerscale variability
in growth rate, and often undergo shortening excursions, in
some cases exceeding 5 tubulin layers, during periods of
overall net growth. This result indicates that the GTP cap
does not exist as a single layer as previously proposed.
Rather, simulations that rely on an exponentially distributed
GTPCap qualitatively reproduce the experimentally observed
variability in microtubule growth.
^{1}Department of Biomedical Engineering, University of Minnesota,
Minneapolis, MN ^{2}Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 

Peter W. Glynn (Stanford University), Peter J. Haas (IBM Research Division)  An introduction to discreteevent simulation 
Abstract: Biochemical systems can often be viewed as discreteevent systems, i.e., as systems that make stochastic state transitions at a strictly increasing sequence of random times. We survey a number of topics pertinent to modeling and simulation of such systems. We first describe several basic models for discreteevent systems, such as generalized semiMarkov processes, stochastic Petri nets, and continuous time Markov chains, and discuss the interplay between the choice of modeling formalism, the compactness of the model representation, and the computational complexity of the resulting simulation algorithm. We then outline a collection of techniques for increasing the efficiency of a simulation, as well as for efficiently estimating the sensitivity of a discreteevent system model with respect to one or more model parameters.  
Nir Shachna Gov (Weizmann Institute of Science)  Coupling the cytoskeleton to the membrane: driving dynamic cellular shape transitions 
Abstract: The outer membrane of a living cell is very different from an inert lipid bilayer or a vesicle. Most notably, there exists a strong coupling between the membrane and the underlying cytoskeletal network of the cell. The cytoskeleton is a dynamic formation of proteins that is continuously reshaping itself, and in the process applies forces to the membrane. The membrane of a living cell is therefore constantly deformed by the forces of the underlying cytoskeleton. The energy produced by the cell's metabolism, in the form of ATP, drives the motion of the cytoskeleton and membrane through the polymerization of cytoskeletal filaments (actin and microtubules), the action of cytoskeletonbound molecular motors and membranebound ions pumps. The cytoskeleton modifies the physical properties, such as the tension, fluctuation amplitude and effective bending modulus of the lipid membrane. In turn, the cytoskeletal organization can be influenced by the membrane shape (curvature) and tension. We describe several theoretical models of the physics of an active cytoskeleton and membrane and their coupling with the cell metabolism. The cytoskeletonmembrane coupling can lead to dynamic instabilities which are manifested as shape transitions of the membrane from the uniform flat configuration to one with fingerlike protrusions. These transitions are coupled with composition phase separation inside the membrane, and are triggered by the active forces of the cytoskeleton. A similar instability is proposed as a model for the initiation of the contractile ring in dividing cells. Furthermore, traveling (propagating) waves can also arise when there are opposing forces of protrusion and contraction, or due to the effect of calcium ion influx. We compare these results to the observed behavior of living cell membranes, which exhibit a variety of dynamic behaviors.  
Dirk Hartmann (RuprechtKarlsUniversität Heidelberg)  Mathematical and computational tools for cellmechanobiology 
Abstract: Mechanical properties are a key feature in a wide rage of animal cell functions, including growth, motility, and gene expression. On the poster, we outline different mathematical and computational tools for theoretical investigations of the mechanobiology of cells. Modelling mechanics of cytoskeletal networks often discrete microscopic models in terms of energy functionals are employed. However macroscopic continuum models are usually preferable form a computational point of view. But finding such macroscopic descriptions is often a nontrivial task. Considering microscopic models given in terms of free energies Gammaconvergence is the ideal framework for rigorously bridging the gap between discrete microscopic and continuous macroscopic models. On this poster mechanics of red blood cells are considered to show how such a multiscale framework can be employed to derive appropriate constitutive laws. The second focus of this poster are computational tools for studying the mechanobiology of cells. We outline two approaches: a Lagrangian approach, which represents evolving domains via transformations of a fixed grid, and an Eulerian approach, which characterises evolving domains implicitly using levelset methods. Both approaches are capable of coupling surface and bulk mechanics, as well as chemical and mechanical processes within one implicit timestepping scheme.  
Peter Hinow (University of Minnesota)  A model for transfer phenomena in structured populations 
Abstract: Pglycoprotein (Pgp) is known to cause resistance to cancer chemotherapy as it is able to remove toxic substances from the cytoplasm of a cell. Recent experimental work by Levchenko et al. shows that cells rich in Pgp are able to transfer part of their Pgp to other cells and thereby may confer resistance. We propose a mathematical model for the transfer of a scalar quantity in a structured population. It is shown that the solutions of this 'pure transfer' model converge to a Radon measure. We add to this model production of Pgp and random fluctuations in Pgp content. For the amended model we show the existence of a globally asymptotically stable steady state, provided that the rate of Pgp transfer is sufficiently small. This is joint work with Pierre Magal (University of Le Havre, France) and Glenn F. Webb (Vanderbilt University).  
Rick Horwitz (University of Virginia)  Actin and aactinin orchestrate the assembly and maturation of nascent adhesions in a myosin II motor independent manner 
Abstract: Joint work with Colin K. Choi ^{1,2}, Miguel VicenteManzanares^{2}, Jessica Zareno^{2}, Leanna A. Whitmore^{2}, and Alex Mogilner^{3}. We have developed a new model for the assembly and maturation of nascent adhesions. Using dual label imaging and high resolution TIRF microscopy, we show that nascent adhesions assemble and are stable only within the lamellipodium. The assembly is myosin IIindependent and requires actin polymerization. A computational model has been developed that accommodates the quantitative data on the dynamics of the nascent adhesions and the linkage of their assembly to actin polymerization. As the back of the lamellipodium moves past the nascent adhesions, they either disassemble as the protrusion continues to advance or begin to grow and elongate and mature as the protrusion pauses. Rescue of a myosin IIA knockdown with a motorinhibited mutant of myosin IIA shows that the crosslinking function of myosin II is sufficient to promote adhesion maturation. Using an RNAi knockdown of ?actinin, we demonstrate that aactininactin filaments serve as a template that guides the centripetal elongation of the nascent adhesion. Measurements of paxillin dynamics and clustering using correlation microscopy show that adhesions tend to assemble by addition of monomers or small aggregates while they disassemble as large aggregates. From these studies, a new model emerges for adhesion assembly and maturation that clarifies the relative contributions of actin polymerization, stabilization and myosin II motor activity to adhesion dynamics. ^{1} Department of Biomedical Engineering and ^{2} Cell Biology, University of Virginia, Charlottesville, Virginia 22908; ^{3}Department of Neurobiology, Physiology and Behavior and Department of Mathematics, University of California, Davis, California 95618.  
Pablo A. Iglesias (Johns Hopkins University)  Modeling chemotactic gradient sensing, polarization and motility in Dictyostelium discoideum 
Abstract: In this talk I will discuss our group's efforts at elucidating the mechanisms underlying chemotaxis. Using known biochemical data, we have developed mathematical models that can account for many of the observed chemotactic behavior of the model organism Dictyostelium. I will discuss experiments used to test these models. Finally, I will describe how informationtheoretic methods can be used to evaluate the optimality of the gradient sensing mechanisms.  
Ken Jacobson (University of North Carolina)  New methods to study motile phenomena 
Abstract: We are currently attempting to understand how the basic processes of contractility, protrusion and adhesion are integrated to produce cell migration. This is an intellectually challenging problem that requires new approaches. The overall plan is to test an in silico model developed by A. Mogilner and colleagues for migration and check this model by perturbing these basic processes locally using photomanipulative techniques to see if the experimentally determined changes in migration can be accounted for by the model. The feasibility of two types of perturbations has now been demonstrated. Caged actin binding proteins and peptides derived from Focal Adhesion Kinase (FAK) can be uncaged and produce dramatic phenotypes. A complementary loss of function technique, EGFPchromophoreassisted laser inactivation (EGFPCALI), has been applied locally to several actin binding proteins including EGFPaactinin, EGFPMena and EGFPcapping protein, again with strong phenotypes. Photochemical mechanisms mediating CALI will be discussed.
I will also describe a graph theoretic approach to modeling motile phenomena that has been developed in collaboration with Gabriel Weinreb, Maryna Kapustina and Tim Elston. The causal map (CMAP) is a coursegrained biological network tool that permits description of causal interactions between the elements of the network which leads to overall system dynamics. On one hand, the CM CMAP is an intermediate between experiments and physical modeling, describing major requisite elements, their interactions and paths of causality propagation. On the other hand, the CMAP is an in independent tool to explore the hierarchical organization of cell and the role of uncertainties in the system. It appears to be a promising easytouse technique for cell biologists to systematically probe v verbally formulated, qualitative hypotheses. We apply the CMAP to study the phenomenon of contractility oscillations in spreading cells in which microtubules have been depolymerized. Supported by the NIH Cell Migration Consortium, IK54GM64346. 

HyeWon Kang (University of Wisconsin)  Multiscale methods in a heat shock response model 
Abstract: We analyze a heat shock response model developed by Srivastava, Peterson, and Bently (2001) to find appropriate scaling for the abundance of molecules of chemical species and for chemical reaction rates. Extending the method introduced in the paper by Ball, Kurtz, Popovic, and Rempala (2006), we apply the multiple scaling method to the various range of the abundance of chemical species and the chemical reaction rate constants. In this method, the complicated original system is split into several subsystems with separate fast, intermediate, and slow processes by using the approximated scaled processes with appropriate time change. Then, we can approximate the state of the system from the state of the reduced subsystems. In the slow time scale, we identify behavior of fast processes since they already start to move while the slow species remain mostly constant. In the fast time scale, we identify behavior of the slow processes driven by averaged behavior of fast processes. Our goal is to find an appropriate scaling that makes the abundance of molecules of each chemical species wellbalanced. To make each chemical species balanced, the maximal order of magnitude of the reaction rates of production for each chemical species must be the same as the maximal order of magnitude of the reaction rates of consumption. We also need a balance condition between the maximal order of magnitude of collective reaction rates of inflow and outflow in each atom graph. In case any of balance conditions are not satisfied, we put restriction on the range of time change exponent. In this poster, I will introduce multiscale method briefly and give a simulation result for one set of exponents meeting our balance conditions.  
Yiannis N. Kaznessis (University of Minnesota)  Multiscale models for synthetic biology 
Abstract: The nascent field of synthetic biology offers the promise of engineered gene networks with novel biological phenotypes. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates. Despite a booming field and although recently developed designs of regulatable gene networks are ingenious, there are limitations in routinely engineering synthetic biological systems. Indeed, there is a need for rationalizing the design of novel regulatable gene networks that can be used in useful applications. We are developing multiscale mathematical tools to assist synthetic biology efforts. Why are multiscale models necessary to assist synthetic biology, and not simply apply the mathematics developed to model kinetic and thermodynamic processes in living organisms? Because, although the principles of thermodynamics, kinetics and transport phenomena apply to biological systems, these systems differ from industrialscale chemical systems in an important, fundamental way: they are occasionally far from the thermodynamic limit. Indeed, using ordinary differential equations for simulating the reaction kinetics of these systems can be distinctly false. The need arises then for stochastic models that account for inherent, thermal noise, which is manifest as phenotypic distributions at the population growth/interaction levels. In the presentation, we will discuss synthetic biological systems, the development of multiscale models to capture the phenotypic behavior of these systems and we will present examples of modeldriven synthetic bioengineering.  
Yiannis N. Kaznessis (University of Minnesota)  Stochastic models for intracellular reaction networks 
Abstract: In the presented work we will describe how to rationalize synthetic biology using modeldriven, molecularlevel engineering principles. In the poster we will focus on the theoretical effort to develop an algorithm for simulating biomolecular systems across all relevant time and length scales; from stochasticdiscrete to stochasticcontinuous and deterministiccontinuous models, we are developing the theoretical foundation for accurately simulating all biomolecular interactions in transcription, translation, regulation and induction and how these result in phenotypic probability distributions at the population level.  
Kyung Hyuk Kim (University of Washington)  Stochastic control analysis for biological reaction systems 
Abstract: We have investigated how noise propagation in biological reaction networks affects system sensitivities. We have shown that the sensitivities can be enhanced in one region of system parameter values by reducing sensitivities in another region. We have applied this sensitivity compensation effect to enhance the amplification of a concentration detector designed by using an incoherent feedforward reaction network. We have also shown that metabolic control analysis can be extended for stochastic reaction systems by providing new versions of the summation and connectivity theorems.  
Samuel Kou (Harvard University)  Subdiffusion and reaction networks in biophysics 
Abstract: In recent years, singlemolecule experiments have emerged as a powerful means to study biophysical/biochemical processes; many new insights are obtained from this singlemolecule perspective. One phenomenon recently observed in singlemolecule biophysics experiments is subdiffusion, which largely departs from the classical Brownian diffusion theory. In this talk, by introducing fractional Gaussian noise into the generalized Langevin equation, we propose a model to describe the subdiffusion. We will study the analytical properties of the model, compare the model predictions with experiments, look at its connection with reaction networks, and explore the implications of the model on enzyme reactions.  
Michael M. Kozlov (Tel Aviv University)  Actin organization at the cell edge: mechanism for formation of lamellipodium lamellum interface 
Abstract: The complex system of actin filaments spanning the volume of a moving cell can be subdivided into distinct zones differing in their dynamic behaviour, structure and function and ordered in space sequentially beginning from the cell leading edge towards the cell interior. The first two zones are the lamellipodium, which underlies the cell membrane at the leading edge, and the lamellum adjacent to the lamellipodium and propagating further into the cell volume. The lamellipodium and lamellar actin networks do not overlap; they are separated by a distinct interface marked by an abrupt change of the velocity of the retrograde actin flow, and by a sharp change of the actin network density and structure. Revealing the physical forces responsible for the generation and dynamics of the lamellipodiumlamellum interface is of a primary importance for understanding the factors which govern organization of actin at the cell front into the spatially segregated and essentially distinct subsystems. The goal of the present work is to propose a physical mechanism for this phenomenon. Based on the existing knowledge on the mechanical properties of actin gels, we consider the lamellipodium actin network as a twodimensional elastic medium, which slides towards the cell centre over a row of focal adhesions and exerts a frictionlike interaction with the latter. We show that the frictionlike interaction between the actin gel and the focal adhesions results in formation of a lamellipodium boundary passing through the row of the FAs and having a shape similar to that observed within cells. This boundary is suggested to represent the lamellipodiumlamellum interface. We further consider advancing of the lamellipodiumlamellum interface to a new row of focal adhesions.  
Peter R. Kramer (Rensselaer Polytechnic Institute)  Prescribing thermal forcing for physical systems 
Abstract: I will describe the principles, particularly the fluctuationdissipation theorem, involved in adding the appropriate stochastic driving terms to represent the effects of finite temperature on a given physical system. The concepts will be described both for an elementary example as well as for the more complicated immersed boundary method simulation scheme for flexible structures coupled to an ambient fluid.  
Peter R. Kramer (Rensselaer Polytechnic Institute)  Theoretical framework for microscopic osmotic phenomena 
Abstract: My main intention in this seminar is to think through, in simple physical terms, how osmotic pressure (for a semipermeable membrane confining a solute) manifests itself microscopically. That is, I will approach the question simply in terms of mechanical forces between the solute and membrane, and the pressure in the fluid. Somehow I am a little concerned that I may be naive about some aspects, and I would appreciate the current IMA visitors pointing out to me where my reasoning may be inadequate! The reason for this investigation was the recognition that the classical statistical mechanical formula for osmotic pressure (van't Hoff's law) requires corrections when some of the idealized assumptions (infinitesmal particles, hardwall membrane) break down. In the physical modeling and numerical simulation of submicron scale systems, these deviations from the idealized limits appear to be relevant. I'll explain the change to the classical formulas in terms of simple examples; in particular the osmotic pressure registered in the fluid must be distinguished from that exerted on the membrane. This discussion will primarily involve elementary statistical mechanics and fluid mechanics. Probability arises only in the prescription of the random configurations of the solute particles subject to the confining potential of the membrane.  
Thomas G. Kurtz (University of Wisconsin)  Analyzing stochastic models 
Abstract: Classical stochastic models for chemical reaction networks are given by continuous time Markov chains. Methods for characterizing these models will be reviewed focusing primarily on obtaining the models as solutions of stochastic equations. The relationship between these equations and standard simulation methods will be described. The primary focus of the talk will be on employing stochastic analytic methods for these equations to understand the multiscale nature of complex networks and to exploit the multiscale properties to simplify the network models.  
Pilhwa Lee (University of Colorado)  Simulation of concentrationdependent contraction of cell 
Abstract: A mathematical modeling and simulation for concentrationdependent contraction of cell is shown in an illustrative way. The cell is supposed to have actomyosin fibers parallelly in cytoplasm. Those fibers are sensitive to local calcium concentration regarding elastic stiffness and resting length by Hilltype curves. Initially the calcium is low in intracellular domain, and high in extracellular domain. With the membrane permeable to calcium, actomyosin network gets activated and contracted by increased calcium inside. By the water semipermeability of the membrane and the osmotic effect, the volume change is available in the course of contraction. Membrane and fibers being moving in a viscous fluid, the spatiotemporal dynamics of ion concentration are possible by the computational framework of the immersed boundary method with advectionelectrodiffusion.  
James C. Liao (University of California)  Closing plenary talk: Metabolic engineering and metabolic modeling: where do we go from here? 
Abstract: Engineering intracellular metabolism has a long history, dated back to the late 1950s. The field has enjoyed significant attention in recent years, particularly because of the energy and environmental concerns. Many success stories have highlighted the feasibility of engineering intracellular metabolism to achieve a designed function. In almost all cases, the bottleneck of the problem resides on the biological side. With some exceptions, mathematical modeling has rarely played a predictive role in this development, mainly because of the lack of appropriate model structure and useful parameters. This talk will discuss a few examples of metabolic engineering and examine the role of mathematical modeling in these developments. In particular, we will present an en ensemble modeling technique that aims to narrow the gap been experiment and model prediction.  
Karen Lipkow (University of Cambridge), David Odde (University of Minnesota)  Model for protein concentration gradients in the cytoplasm 
Abstract: Intracellular protein concentration gradients are generally thought to be unsustainable at steadystate due to diffusion. Here we show how protein concentration gradients can theoretically be sustained indefinitely through a relatively simple mechanism that couples diffusion to a spatially segregated kinase–phosphatase system. Although it is appreciated that such systems can theoretically give rise to phosphostate gradients, it has been assumed that they do not give rise to gradients in the total protein concentration. Here we show that this assumption does not hold if the two forms of protein have different diffusion coefficients. If, for example, the phosphorylated state binds selectively to a second larger protein or protein complex, then a steadystate gradient in total protein concentration will be created. We illustrate the principle with an analytical solution to the diffusionreaction problem and by stochastic individualbased simulations using the Smoldyn program. Looking at the example of bacterial chemotaxis, our model accounts for gradients of total CheY and CheZ, which were observed, but not explained, previously. We argue that protein gradients created in this way need to be considered in experiments using fluorescent probes and could in principle encode spatial information in the cytoplasm.  
Karen Lipkow (University of Cambridge)  Smoldyn, a novel simulator for cellular systems biology 
Abstract: Joint work with Steven S. Andrews
(The Molecular Sciences Institute, Berkeley, MA). Smoldyn is a computer program for performing detailed simulations of cell biology. Proteins and other molecules of interest are represented by individual pointlike particles that diffuse within a continuous (nonlattice) space. Smoldyn accurately and efficiently simulates diffusion, first and second order chemical reactions, allostery, and a wide variety of moleculemembrane interactions. As in the eponymous Smoluchowski theory, simulated bimolecular reactions occur when reactants diffuse closer together than a socalled binding radius. Smoldyn was written on OS X in C and OpenGL. It is open source, multiplatform, and easy to use, running on any machine from laptop to computer cluster. Smoldyn is freely available, designed for both the research and teaching communities. We present the algorithm and its speed performance, and its application in modelling various aspects of the bacterial chemotaxis system. 

Wolfgang Losert (University of Maryland)  Quantitative analysis of stream formation during dictyostelium chemotaxis 
Abstract: Joint work with C.P. McCann^{1,2},
P.W. Kriebel^{1},
E.C. Rericha^{2},
C.A. Parent^{1}.
Upon nutrient deprivation, the social amoeba Dictyostelium
discoideum enter a developmental program allowing them to
aggregate and differentiate into a multicellular organism made
of spores atop a stalk of vacuolated cells. During the
aggregation process individual cells sense and migrate up a
chemoattractant gradient of cyclic adenosine monophosphate
(cAMP), relaying the signal by synthesizing and releasing
additional cAMP. This process leads to a characteristic chain
migration where cells align in a headtotail fashion and
migrate in streams. We have quantitatively analyzed timelapse
images of cells as they migrate in streams. We find that the
average cell migration speed and the formation of streams are
sensitive to cell plating density. At a density of 7x104
cells/cm2 cells stream with an average speed of ~10 micron/min
outside steams, while at 2x104 cells/cm2 cells do not stream
and move with an average speed of ~5 micron/min. In addition,
for a given plating density, the volume of fluid above the
cells has a strong influence on the formation of streams,
suggesting that excess extracelluar fluid effectively dilutes
celltocell signals. We are currently performing cell mixing
experiments with fluorescently labeled cells to assess the
speed of cells both inside and outside streams. Our findings
provide insights into the requirements for signal relay during
chemotaxis and highlight the importance of environmental
conditions for cell migration.
^{1}Laboratory of Molecular and Cellular
Biology, CCR, NCI, NIH, Bethesda, MD; ^{2}Dept. of Physics, University of Maryland, College Park, MD 

Denise Montell (Johns Hopkins University)  Developmental regulation of cell motility 
Abstract: In normal development and tumor metastasis, epithelial cells can acquire migratory and invasive properties. Border cells in the Drosophila ovary provide a genetic model for such behaviors. Earlier work has shown that JAK/STAT signaling is critical to specify the migratory population and sustain their motility. In a genetic screen for new mutations that affect border cell motility, we identified the gene apontic. Apontic, a nuclear protein, converts an initially graded pattern of STAT activity into an allornothing response. This defines and limits the invasive border cell population. Apt functions as a feedback inhibitor of STAT, providing a molecular mechanism to explain a classic problem in embryology: how a graded signal can generate discrete cellular responses. This work, which includes a mathematical model, elucidates one mechanism to limit cell invasion in vivo.  
Hans G. Othmer (University of Minnesota)  Stochastic aspects of actin filament dynamics 
Abstract: Actin polymerization and network formation are key processes in cell motility. Numerous actin binding proteins that control the dynamic properties of actin networks have been studied and models such as the dendritic nucleation scheme have been proposed for the functional integration of at least a minimal set of such regulatory proteins. In this talk we will describe recent work on the evolution of the distribution of filament lengths and nucleotide profiles of actin filaments from both the deterministic and stochastic viewpoints. For the latter we develop a master equation for the biochemical processes involved at the individual filament level and simulate the dynamics by generating numerical realizations using a Monte Carlo scheme. A new computational algorithm that is far more efficient than standard methods will also be described.  
John Sandy Parkinson (University of Utah)  Bacterial chemotaxis: Sophisticated behavior from simple circuitry 
Abstract: Motile bacteria seek optimal living habitats by following gradients of attractant and repellent chemicals in their environment. The signaling machinery for these chemotactic behaviors, although assembled from just a few protein components, has extraordinary informationprocessing capabilities. Escherichia coli, the beststudied model, employs a networked cluster of transmembrane receptors to detect minute chemical stimuli, to integrate multiple and conþicting inputs, and to generate an ampliÞed output signal that controls the cell's þagellar motors. Signal gain arises through cooperative action of chemoreceptors of different types. The signaling teams within a receptor cluster may be built from trimers of receptor dimers that communicate through shared connections to their partner signaling proteins.  
Johan Paulsson (Harvard Medical School)  Fundamental limits on the suppression of randomness in biology 
Abstract: Living cells contain such low numbers of active genes, RNAs and proteins that random fluctuations in concentrations arise spontaneously. Because many biological processes require reliability and precision, cells are thought to deal with this problem by using negative feedback control loops, correcting perturbations by increasing synthesis at high concentrations and decreasing it at low concentrations. Negative feedback has been systematically studied in control theory, but focus has been on macroscopically large systems subject to external perturbations  like airplanes in random gusts of wind  while other principles arise in discrete molecularscale probabilistic processes. Many biological control loops are also strongly nonlinear, and crucial aspects are often unknown. Such systems are typically considered impossible to analyze: how can we make quantitative statements about strongly nonlinear stochastic systems if we do not even know the nature of the nonlinearities? By combining exact analytical tools from functional analysis, information theory and statistical physics, I will show that seemingly mild constraints, such as short delays or finite numbers of control molecules, can place fundamental limits on noise suppression that no arbitrarily elaborate feedback system could ever overcome  hard physical bounds that can be derived explicitly. Using approximate methods for families of feedback systems, I will also show how the limits arise because different types of noise are suppressed according to incompatible principles, generating frustration tradeoffs where reducing one type of variation inevitably amplifies another. Finally I discuss partial loopholes in the general laws where counterintuitive mechanisms can circumvent the tradeoffs to some extent. The general results are illustrated by bacterial plasmids, where large fluctuations promote extinction and where numerous mechanisms have evolved to approach the physical limits. I will emphasize intuitive reasoning and physical observables throughout, and show some preliminary experiments.  
Alka A. Potdar (Vanderbilt University)  Cellular dynamics simulations of MCF10A cell random migration in 2D 
Abstract: Coauthors: Junhwan Jeon ^{[1]}, Alissa
M. Weaver ^{[2]} and Peter T.
Cummings ^{[1,3]}.
We have implemented the cellular dynamics simulation
methodology (originally developed for bacterial migration) to
describe the random migration of MCF10A pbabe, neuN and neuT
cells using the experimental parameters extracted for each cell
type, by the application of bimodal analysis technique
developed by us. The Bimodal framework inspired from the
bacterial runtumble scheme segregates mammalian cell tracks
into alternating directional and reorientation modes. We find
from simulations that the HER2 transformed neuT cells have
higher random motility coefficient compared to the control
pbabes.
^{[1]}Department of Chemical Engineering, Vanderbilt University,
Nashville TN ^{[2]}Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN ^{[3]}Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 

Michael C. Reed (Duke University)  Opening plenary talk: Stochastic analysis is the fundamental tool for understanding biological function 
Abstract: Several deterministic models will be presented to illustrate the special types of mechanisms that regulate and control cell metabolism. Stochastic analysis can be used to investigate such highly nonlinear deterministic dynamical systems that typically operate far from equilibrium. Examples will be given that show how specific biological system properties depend on the natural stochasticity inherent in diffusion processes and in the making and breaking of chemical bonds. On many different space and time scales, evolution has selected stochastic mechanisms for accomplishing specific biological functions. Finding, characterizing, and explaining these mechanisms requires cooperative research by biologists and stochastic analysts. The tasks are daunting because biology is really hard.  
Grzegorz A. Rempala (University of Louisville)  Inferring an underlying reaction network from the data 
Abstract: Consider the system of reaction rate equations (RRE) describing a chemical network with the reaction rate constants considered to be unknown parameters. The talk shall describe a statistical approach to identifying the "most likely network" from a given set of RRE coefficient estimates. The idea relies on mapping the estimated reaction constants into an appropriate convex region of a network stochiometric space in order to identify the reactions which are most likely to span that region. This approach reduces the original problem to inferring parameters of a certain multinomial distribution which may be solved using the general methods of algebraic statistics.  
Erin Rericha (University of Maryland)  Signal propagation during chemotaxis 
Abstract: The social amoebae Dictyostelium and human leukocytes possess the ability to polarize and move in a directed fashion in response to chemical signals. We are interested in understanding how cells transduce shallow external chemotactic signals into highly polarized cellular responses, which are required for directed cell migration. By tagging various signaling proteins with the green fluorescent protein (GFP) we have been able to visualize in live cells where and when various cascades are activated. This has led us to propose a novel mechanism that explains how chemotactic gradients are amplified by signal relay. Our research projects are designed to provide insight on the role of various signaling cascades in chemotaxis and have direct bearing on the understanding of clinically important processes as such leukocyte migration to sites of inflammation as well as cancer metastasis.  
Howard Salis (University of California)  Predicting translation rate from sequence 
Abstract: The reliable genetic engineering of bacterial systems would be greatly aided by a more quantitative and predictive understanding of gene expression. In many synthetic biology applications, the production rate of a protein needs to be precisely tuned to optimize some performance characteristic of the genetic system. A typical application might require selecting the optimal expression level of enzymes in a metabolic pathway. The production rate of a protein can be precisely tuned by varying the mRNA sequence of the 5' UTR (untranslated region), including the ribosome binding site (RBS), that precedes the protein's coding sequence. However, there is currently no quantitative model that can predict an RNA sequence that yields a desired production rate of protein. We have developed and experimentally validated a statistical thermodynamic model of translation initiation in Escherichia coli. The model quantifies the effects of the important RNA, RNARNA, and RNAprotein interactions and determines the resulting translation initiation rate of a specified mRNA transcript. We combine the quantitative model with Monte Carlo sampling to create a design method that generates synthetic 5' UTRs. The user inputs a protein coding sequence and a desired translation initiation rate and the design method will generate the corresponding RNA sequence. We measure the accuracy of the design method by generating numerous synthetic 5' UTRs that drive the expression of the fluorescent protein RFP in a simplified test system. We then compare the design method's predictions with flow cytometry data from a test system. The design method is capable of accurately generating RNA sequences that yield the desired translation rate, with rates varying across three orders of magnitude. The presented model and method is a key foundational technology that will enable us to more rationally engineer synthetic biological systems. With the emergence of lower cost largescale DNA synthesis and the rapid assembly of genetic systems, the development bottleneck now shifts towards identifying the DNA sequence that ultimately yields a desired system behavior. These and other quantitative and predictive models aim to provide that muchneeded missing link.  
Sebastian Ambrose Sandersius (Arizona State University)  Modeling cell rheology: microrheology of viscoelastic networks 
Abstract: Recently, a computational model addressing cell biomechanics has been introduced, called the Subcellular Element Model (SEM). The model is primarily designed to represent deformable cells in multicellular systems, but can also be used to represent a single cell in more detail. Within the model framework, a cell is represented by a collection of elastically coupled elements, interacting with one another via shortrange potentials, and dynamically updated using overdamped Langevin dynamics. We have tested whether the model yields viscoelastic properties consistent with those measured on single living cells and reconstituted cytoskeletal networks. Employing methods of microrheology we find that weak power law rheology emerges. With further phenomenology of treating the cytoskeleton as a viscoelastic network, we show here a novel onedimensional model, which as well displays intermediate time regime/weak power law rheology.  
Christian Schmeiser (Universität Wien)  A continuum model of the cytoskeleton dynamics in lamellipodia 
Abstract: The lamellipodium is the movement organ of several types of crawling cells. Motility is driven by the dynamics of the actin cytoskeleton, a flat network of polymers. The dynamics is influenced by actin (de)polymerization, by crosslinking of polymers, by their stiffness, by adhesion of the network to the substrate through transmembrane proteins, by active contraction of the network by motor proteins, and by the mechanical properties of the cell membrane. In a recent modelling effort, these effects are modelled on a microscopic scale, where the elastic response of individual filaments and the stochastic properties of protein reactions are taken into account. Then, by a homogenization procedure, continuum models for the cytoplasm in a lamellipodium are derived. The derivation of this new class of models, their mathematical properties, as well as results of numerical simulations will be presented.  
Rishi Srivastava (University of Wisconsin)  Stochastic modeling of vesicular stomatitis virus(VSV) growth in cells: An application of order statistics to predict replication delays 
Abstract: Although viruses are the smallest organisms with the shortest genomes, they have major impacts on human health, causing deadly diseases (e.g., influenza, AIDS, cancer) on a global scale. To reproduce, a virus particle must infect a living cell and divert biosynthetic resources toward production of virus components. A better mechanistic understanding the infection cycle could lead to insights for more effective antiviral strategies. However, simulating the virus infection cycle is a computationally hard problem because some species fluctuate rapidly while others change gradually in number. We study vesicular stomatitis virus, an experimentally accessible virus for which we have developed a deterministic kinetic model of growth (Lim et al, PLoS Comp Bio, 2006). Stochastic simulation of VSV genome encapsidation is a computationallyintensive chain reaction that produces rapid fluctuations in the nucleocapsid(N) protein that associates with the genome. Analytical results from order statistics enable us to avoid explicit tracking of all intermediate species, with significant reduction in computational burden. This approach can be generalized to a broad class of stochastic polymerization reactions where multistep chain reactions are modeled as a single reaction with timedelay.  
Benjamin L. Stottrup (Augsburg College)  TenascinC is upregulated at the end of the cell cycle in proliferating NIH 3T3 fibroblasts 
Abstract: Joint work with Michael Halter^{1}, Kurt J.
Langenbach^{2},
Alex Tona^{3},
Anne L. Plant^{1}, John T.
Elliott^{1}
TenascinC expression is frequently upregulated during wound
healing, inflammation, and tumorigenesis. Using live cell
automated microscopy, we quantified the fluorescence intensity
from individual NIH3T3 fibroblasts stably transfected with a
tenascinC promoter driving a destabilized eGFP reporter.
Hundreds of individual cells were followed throughout the cell
cycle during live cell imaging experiments lasting 62 hours.
We will describe techniques used to track these cells as they
migrate. We observed that the GFP production in individual
cells increased as they approached mitosis. On average the
increase began when cells were approximately 60% through the
cell cycle, suggesting that the tenascinC promoter is more
active at the end of the cell cycle. We conclude that the
increase in GFP within individual cells was unlikely due to a
systematic change in the degradation of GFP because we found no
correlation between the GFP degradation kinetics and GFP
fluorescence intensity when measured across a large number of
individual cells. This work illustrates the application of
quantitative, live cell microscopy using a promoterdriven GFP
reporter cell line to measure the dynamics of single cell gene
expression activity. A large number of signaling pathways have
been implicated in the activation of tenascinC gene
transcription. Our results suggest that tenascinC expression
is, at least in part, directly coupled to proliferation and
cell cycle progression.
^{1} National Institute of Standards and Technology, Gaithersburg
MD ^{2}ATCC, Manassas, VA ^{3}SAIC, Arlington, VA 

Marcus John Tindall (University of Oxford)  Spatiotemporal modelling of intracellular signalling in Rhodobacter sphaeroidess 
Abstract: Joint work with S.L. Porter^{2},
P.K. Maini^{1,3},
J.P. Armitage^{2,3}.
The role that spatial protein localisation plays in altering
the expression of flagellar motor driving proteins in bacterial
chemotaxis has to date largely been ignored. The work presented
here focuses on a mathematical spatiotemporal
reactiondiffusion model of signal transduction developed to
describe phosphotransfer in Rhodobacter sphaeroides. The
mathematical model is used to understand the role that spatial
protein localisation has on affecting the motor protein
expression and the connection between the cytoplasmic and
receptor clusters in describing the overall bacterial response.
^{1} Centre for Mathematical Biology, Mathematical
Institute, 2429 St Giles', Oxford, OX1 3LB. ^{2}Department of Biochemistry, Microbiology Unit, University of Oxford, South Parks Road, Oxford, OX1 3QU. ^{3}Oxford Centre for Integrative Systems Biology, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU. 

Lev S. Tsimring (University of California, San Diego)  Stochastic oscillations in small genetic networks 
Abstract: One of the defining characteristics of life is the ability to keep time, which organisms often achieve by using internal genetic "clocks" to govern fundamental cellular behavior. While the gene networks that produce oscillatory expression signals are typically quite elaborate, certain recurring network motifs are often found at the core of these biological clocks. In this lecture, I will describe our recent experimental and theoretical work on small genetic networks exhibiting oscillatory behavior. One common motif which may lead to oscillations is delayed autorepression. We constructed synthetic oscillators based on this design principle, and observed robust and tunable oscillations both in bacteria and yeast. Since genetic systems typically involve small number of reacting components, intrinsic fluctuations play an important role in the dynamics. I will introduce theoretical and computational tools which can be used for modeling stochastic dynamics of small genetic networks.  
Yuhai Tu (IBM)  From molecule to behavior: E. coli’s memory, computation and taxis 
Abstract: In this talk, I will present our recent work in trying to understand bacterial chemotaxis (bacteria’s ability to sense and track chemical gradient) by using a quantitative modeling approach. Based on molecular level knowledge of the E. coli chemotaxis pathway, we propose a simple model for bacterial chemotaxis and use it to address several interesting, important systemlevel questions: 1) What kind of computation does a E. coli cell perform in response to complex timevarying stimuli? 2) What kind of memory does E. coli have? How long does it take the cell to forget? 3) How does the cell use its memory and computation capability to sense and respond to a minute chemical gradient (food or poison) among a wide range of background.  
Melissa Vellela (University of Washington)  Stochastic modeling of bistable chemical systems: Schlogl's model 
Abstract: When describing chemical systems in a nonequilibrium steady state, striking differences arise in the predictions between deterministic and stochastic models. Using a classic model first introduced by Schlogl, we study the steady state behaviors of a one dimensional, open system that exhibits bistability. The definitions of thermodynamic quantities, such as flux, chemical potential, and entropy production rate are compared across the two types of models. The stochastic model allows for jumping between the two stable steady states which are separated by an unstable steady state in the deterministic model. The transition rates and exponentially small eigenvalue associated with this jumping are investigated and calculated numerically. Understanding the behavior of the stochastic steady state directly from the master equation is important because even widely used approximations such as FokkerPlanck can be incorrect, as is shown for this example.  
Rajitha Vuppula (Indian Institute of TechnologyBombay)  Chemotaxis of E. coli towards glucose 
Abstract: Joint work with Mahesh S. Tirumkudulu and K.V. Venkatesh We describe a technique to create stable glucose gradients without requiring any fluid flow. The gradients are measured in both, space and time, using fluorescent glucose. We quantify the chemotaxis of E. coli by measuring the diffusivity and drift velocity of individual bacteria at varying glucose gradients.  
Yuli Wang (University of Massachusetts Medical School, Worcester Campus)  Adherent cells as a mechanical device  probing the forces and understanding the regulatory circuit 
Abstract: We have applied new experimental and computational approaches to understand the mechanical events of adherent cells. While the extensive use of flexible substrates has lead to excellent understanding of traction forces, introduction of long polymers into the cytoplasm has started to shed light on weak gradients of forces generated by the cortex. In addition, a topdown mathematical model has been developed to simulate the control circuit that coordinates protrusions and retractions. The model is capable of generating experimentally measurable parameters, which in turn allows the use of optimization algorithms to match the model with observations, and to provide insights into how a unified feedback mechanism might generate a wide range of behaviors.  
Alissa Weaver (Vanderbilt University)  Computational modeling of invadopodiaECM interactions 
Abstract: Invadopodia are subcellular protrusive structures associated with sites of extracellular matrix (ECM) degradation. Little is known about the regulation of invadopodia functions by ECM properties. I will discuss a joint experimentalcomputational study in which we have modeled and tested the regulation of invadopodia formation and function by crosslinked gelatin and collagen substrates. We find that crosslinking inhibits degradation and penetration of invadopodia in dense substrates, such as gelatin, but not in loose collagen matrices. These results provide a framework for consideration of diverse types of matrices that invasive cells may experience in vivo.  
Cornelius Weijer (University of Dundee)  Chemotactic cell movement during Dictyostelium development and chick gastrulation 
Abstract: We investigate the molecular mechanisms by which cells produce and detect chemotactic signals and translate this information in directed coordinated movement up or down chemical gradients in the social amoebae Dictyostelium discoideum, and during gastrulation in the chick embryo. In Dictyostelium starvation for food induces the aggregation of up to hundreds of thousands of individual amoebae into a multicellular aggregate. During aggregation the cells differentiate into several distinct celltypes, which sort out to form a migrating slug, which after a variable period of migration transforms into a fruiting body consisting of a stalk supporting a mass of spores. Experiments show that chemotactic cell migration in all stages of development is controlled by propagating waves of the chemoattractant cAMP. At present we concentrate on investigation of the mechanisms that drive chemotactic cell sorting which results in slug formation. We use quantitative imaging techniques to investigate cell type specific differences in signal transduction dynamics, polarised activation of the actinmyosin cytoskeleton and force generation that drive cell sorting. Gastrulation in the chick embryo starts with the occurring of extensive cell flows that result in the formation of the primitive streak, a structure through which the mesoderm and endoderm precursor cells ingress to take up their correct positions in the embryo. We are tracking the invivo migration of these cells during streak formation and after their ingression through the primitive streak to gain insight in the mechanisms that drive these cell movements. Our current hypothesis is that formation of the primitive streak as well as the movement of the mesoderm cells after their ingression through the streak is controlled by a combination of attractive and repulsive guidance cues, delivered at least in part by members of the FGF, VEGF, PDGF and Wnt families of signalling molecules.  
Darren James Wilkinson (University of Newcastle upon Tyne)  Bayesian inference for stochastic intracellular reaction network models 
Abstract: This talk will provide an overview of computationally intensive methods for conducting Bayesian inference for the rate constants of stochastic kinetic intracellular reaction network models using singlecell time course data. Inference for the true Markov jump process is extremely challenging in realistic scenarios, so the true model will be replaced by a diffusion approximation, known in this context as the Chemical Langevin Equation (CLE). Inference for the CLE is also challenging, but the development of effective algorithms is possible, and turns out to be extremely effective, even in scenarios where one would expect the diffusion approximation to break down.  
Ruth J. Williams (University of California, San Diego)  Part II: Homotopy methods for counting reaction network equilibria 
Abstract: Based on joint work with G. Craciun and J. W. Helton. Dynamical system models of complex biochemical reaction networks are usually highdimensional, nonlinear, and contain many unknown parameters. In some cases the reaction network structure dictates that positive equilibria must be unique for all values of the parameters in the model. In other cases multiple equilibria exist if and only if special relationships between these parameters are satisfied. We describe methods based on homotopy invariance of degree which allow us to determine the number of equilibria for complex biochemical reaction networks and how this number depends on parameters in the model.  
Yuan Xiong (Johns Hopkins University)  Automated characterization of cell shape changes during amoeboid motility 
Abstract: The ability of a cell to change shape is crucial for the proper function of many cellular processes, such as chemotaxis. Traditionally, cell motility has been characterized by a number of different parameters, determined either by the position of the cell's centroid as it migrates, or by limited aspects of the cell shape such as perimeter, area roundness and body orientation. These parameters primarily provide global information about chemotaxis and chemoattractantinduced cell shape changes. They are insufficient to distinguish cell strains based on local morphological information, such as pseudopodial protrusions, that typify amoeboid motility. When the activity of pseudopods has been described, the protrusions and retractions have been identified and outlined mostly manually. In addition to being highly time consuming, these manual methods have the drawback that they are based on subjective judgments. Here we describe a series of automated methods used to characterize amoeboid locomotion based on the skeleton of a planar shape. We demonstrate that the skeleton can be used to identify pseudopods in microscopic images of moving cells. Moreover, the timevarying evolution of the skeleton can be used to capture the dynamic nature of the pseudopodial protrusions and retractions  
Richard Yamada (University of Michigan)  Biochemical and network modeling of the mammalian suprachiasmatic nucleus 
Abstract: The SupraChiasmatic Nucleus (SCN) is the central oscillator that keeps time in all mammalian organisms. Central to understanding the circadian rhythms that these oscillators generate are the multitude of interacting genes and proteins, in addition to the complex interactions of coupled SCN cells. In recent year, quantitative modelling has emerged as an additional tool complimenting experimental techniques in the study of circadian rhythms. In this poster, we discuss a detailed mathematical model for circadian timekeeping within the SCN. Our proposed model consists of a large population of SCN neurons, with each neuron containing a network of biochemical reactions involving the core circadian components. Central to our work is the determination of the model’s unkown parameters, which were obtained from comparing the model’s output to experimental data. From these estimated parameters, additional experimental test of the model are proposed. Our studies highlight the importance of low numbers of molecules of clock proteins, and how this fact addects the accuracy of circadian timekeeping.  
John Yin (University of Wisconsin)  Models and measures of virus growth and infection spread 
Abstract: When a virus infects a living cell it directs the biosynthetic resources of the cell toward transcription and translation of viral proteins, replication of viral genomes, assembly of virus particles, and release of hundreds to thousands of progeny virus particles to the extracellular environment. For wellcharacterized viruses one may begin to build kinetic models that predict virus growth behavior based on the dynamics of the underlying molecular processes within the infected cell. Recent experimental study of single BHK cells infected by single particles of vesicular stomatitis virus, a virus that encodes only five genes, reveal a broad distribution of virus productivity. Viral genetic variation and hostcell heterogeneity are unable to fully account for the broad growth behavior. A better understanding of these experiments may follow from stochastic modeling of virus growth.  
Shan Zhao (University of Alabama)  The minimal molecular surface 
Abstract: We introduce a novel concept, the minimal molecular surface (MMS), as a new paradigm for the theoretical modeling of biomoleculesolvent interfaces. When a less polar macromolecule is immersed in a polar environment, the surface free energy minimization occurs naturally to stabilize the system, and leads to an MMS separating the macromolecule from the solvent. For a given set of atomic constraints (as obstacles), the MMS is defined as one whose mean curvature vanishes away from the obstacles. Based on the theory of differential geometry, an iterative procedure is proposed to compute the MMS via the mean curvature minimization of molecular hypersurface functions. Extensive numerical experiments, including those with internal and open cavities, are carried out to demonstrate the proposed concept and algorithms. Comparison is given to the molecular surface. Unlike the molecular surface, the proposed MMS is typically free of singularities. The application of the MMS to the electrostatic analysis is considered for a set of twenty six proteins.  
Xiaoming Zheng (University of Michigan)  A Multiscale model of endothelial cell migration, proliferation and maturation in angiogenesis 
Abstract: Joint work with Trachette Jackson (Mathematics Department, University of Michigan). A new multiscale mathematical model of angiogenesis is developed to quantify the relative roles of cell quiescence, proliferation and migration. Migration is assumed to be the primary event of sprouting, and the governing equation is derived based on cellular mechanics. The extension of the vessel, however, is restricted by the supply of endothelial cells, which is dominated by the proliferation. Although all cells are capable of proliferating, experimental observations often conclude that the proliferative activity is concentrated in the region proximal to the leading edge. In order to capture this phenomenon, we introduce the concept of maturation level, which is modulated by angiopoietins, to describe a quiescent phenotype of endothelial cells. All of these features are combined into a multiscale model of vessel extension in angiogenesis, including vessel anastomosis and branching. Relations between extension and proliferation are investigated, and comparisons to rat cornea experiments are made.  
Dietmar Bernhard Ölz (Universität Wien)  From the curve straightening flow in the plane to a model for the Lamellipodial Actincytoskeleton 
Abstract: The presentation deals with the modelling approach on the Actincytoskeleton presented too in the talk of C. Schmeiser. In the first part of the talk we derive the model in a step by step way starting form the curve straightening flow of planar open curves generated by the Kirchhoff bending energy. We show numerical simulations and present a local in time existence result of solutions and regularity results. In the second part we go back to the curve straightening flow and formulate the governing equations in terms of the indicatrix and a scalar Lagrange multiplier function. We prove existence and (improved) regularity of solutions. We compute the energy dissipation, prove its coercivity and conclude the exponential decay of the energy. 
Jonathan B. Alberts  University of Washington  5/26/2008  5/30/2008 
Claudio Altafini  International School for Advanced Studies (SISSA/ISAS)  4/15/2008  5/15/2008 
David F. Anderson  University of Wisconsin  5/10/2008  5/14/2008 
Ping Ao  University of Washington  5/10/2008  5/13/2008 
Douglas N. Arnold  University of Minnesota  7/15/2001  6/30/2008 
Donald G. Aronson  University of Minnesota  9/1/2002  8/31/2009 
Paul Atzberger  University of California  5/26/2008  5/30/2008 
Daniel J. Bates  University of Minnesota  9/1/2006  8/31/2008 
John Baxter  University of Minnesota  8/1/2007  4/1/2009 
Banu Baydil  Rensselaer Polytechnic Institute  3/1/2008  6/30/2008 
Yermal Sujeet Bhat  University of Minnesota  9/1/2006  8/31/2008 
Tracy Bibelnieks  Augsburg College  5/27/2008  5/30/2008 
Lisa Bishop  University of Washington  5/10/2008  5/13/2008 
Khalid Boushaba  Iowa State University  1/15/2008  6/30/2008 
Maury Bramson  University of Minnesota  5/10/2008  5/13/2008 
Dennis Bray  University of Cambridge  5/1/2008  5/30/2008 
Mónica F. Bugallo  SUNY  5/10/2008  5/13/2008 
Don Button  University of Alaska  5/11/2008  5/12/2008 
MariaCarme T. Calderer  University of Minnesota  5/11/2008  5/13/2008 
MariaCarme T. Calderer  University of Minnesota  5/27/2008  5/30/2008 
Hannah Callender  University of Minnesota  9/1/2007  8/31/2009 
Yang Cao  Virginia Polytechnic Institute and State University  5/10/2008  5/13/2008 
Yang Cao  Virginia Polytechnic Institute and State University  5/26/2008  5/30/2008 
Anders E. Carlsson  Washington University  5/26/2008  5/30/2008 
Teng Chen  University of Central Florida  5/10/2008  5/14/2008 
Lauren Maressa Childs  Cornell University  5/10/2008  5/14/2008 
Gregory S. Chirikjian  Johns Hopkins University  5/25/2008  5/30/2008 
YungSze Choi  University of Connecticut  4/1/2008  5/31/2008 
Ioana Cipcigan  University of Maryland Baltimore County  5/10/2008  5/13/2008 
Duncan J. Clarke  University of Minnesota  5/20/2008  5/20/2008 
Philippe Cluzel  University of Chicago  5/29/2008  5/30/2008 
John S. Condeelis  Albert Einstein College of Medicine  5/26/2008  5/30/2008 
Ludovica Cecilia CottaRamusino  University of Minnesota  10/1/2007  8/30/2009 
Gheorghe Craciun  University of Wisconsin  5/10/2008  5/13/2008 
Gaudenz Danuser  Scripps Research Institute  5/26/2008  5/31/2008 
Lance Davidson  University of Pittsburgh  5/26/2008  5/30/2008 
Adriana Dawes  University of Washington  5/26/2008  5/30/2008 
Micah Dembo  Boston University  5/26/2008  5/30/2008 
Anilkumar Devarapu  University of Louisville  5/11/2008  5/14/2008 
Lee DeVille  University of Illinois at UrbanaChampaign  5/10/2008  5/13/2008 
Rich Dickinson  University of Florida  5/26/2008  5/30/2008 
Ton Dieker  IBM  5/11/2008  5/13/2008 
Kequan Ding  Chinese Academy of Sciences  4/15/2008  5/31/2008 
Petar M. Djuric  SUNY  5/10/2008  5/13/2008 
Olivier Dubois  University of Minnesota  9/3/2007  8/31/2009 
Leah EdelsteinKeshet  University of British Columbia  5/29/2008  5/30/2008 
Walid Fakhouri  Michigan State University  5/10/2008  5/14/2008 
Martin Falcke  HahnMeitnerInstitut für Kernforschung (Nuclear Research) Berlin GmbH  5/26/2008  5/31/2008 
Sergei Fedotov  University of Manchester  5/5/2008  6/4/2008 
Daniel Fletcher  University of California  5/26/2008  5/30/2008 
Christodoulos A. Floudas  Princeton University  4/1/2008  6/30/2008 
Henrik Flyvbjerg  Technical University of Denmark  5/26/2008  5/30/2008 
John Fricks  Pennsylvania State University  5/10/2008  5/14/2008 
Arnab Ganguly  University of Wisconsin  5/10/2008  5/13/2008 
Margaret Gardel  University of Chicago  5/26/2008  5/30/2008 
Melissa K. Gardner  University of Minnesota  5/27/2008  5/30/2008 
Peter W. Glynn  Stanford University  5/11/2008  5/13/2008 
Nir Shachna Gov  Weizmann Institute of Science  5/26/2008  5/30/2008 
Jason E. Gower  University of Minnesota  9/1/2006  8/31/2008 
Paula Grajdeanu  Ohio State University  5/10/2008  5/14/2008 
Robert Guy  University of California  3/24/2008  6/24/2008 
Peter J. Haas  IBM Research Division  5/11/2008  5/13/2008 
Esfandiar Haghverdi  Indiana University  1/2/2008  6/30/2008 
Adam Miklos Halasz  University of Pennsylvania  5/10/2008  5/14/2008 
Mark Harris  Vanderbilt University  5/26/2008  5/31/2008 
Dirk Hartmann  RuprechtKarlsUniversität Heidelberg  5/22/2008  6/21/2008 
Thomas Hays  University of Minnesota  5/27/2008  5/30/2008 
Gerald L. Hazelbauer  University of Missouri  5/26/2008  5/30/2008 
David Heine  Corning  5/26/2008  5/30/2008 
Milena Hering  University of Minnesota  9/1/2006  8/31/2008 
Anthony Hill  University of Minnesota  5/11/2008  5/13/2008 
Thomas Hillen  University of Alberta  4/27/2008  5/10/2008 
Peter Hinow  University of Minnesota  9/1/2007  8/31/2009 
Rick Horwitz  University of Virginia  5/26/2008  5/29/2008 
Martin Howard  John Innes Centre  5/24/2008  5/30/2008 
Bo Hu  Center for Theoretical Biological Physics, UCSD  5/26/2008  5/31/2008 
DiAnna Lynn Hynds  Texas Woman's University  5/26/2008  5/31/2008 
Pablo A. Iglesias  Johns Hopkins University  5/26/2008  5/30/2008 
Ken Jacobson  University of North Carolina  5/26/2008  5/30/2008 
Richard D. James  University of Minnesota  9/4/2007  6/30/2008 
Imre M. Jánosi  Eötvös Loránd University (ELTE)  2/1/2008  6/30/2008 
Junhwan Jeon  Vanderbilt University  5/26/2008  5/30/2008 
Tiefeng Jiang  University of Minnesota  9/1/2007  6/30/2008 
HyeWon Kang  University of Wisconsin  5/11/2008  5/14/2008 
Yiannis N. Kaznessis  University of Minnesota  5/10/2008  5/13/2008 
Eric Kim  Vanderbilt University  5/26/2008  5/31/2008 
Kyung Hyuk Kim  University of Washington  5/10/2008  5/13/2008 
Debra Knisley  East Tennessee State University  8/17/2007  6/1/2008 
Dmitry A. Kondrashov  University of Chicago  5/9/2008  5/13/2008 
Samuel Kou  Harvard University  5/10/2008  5/13/2008 
Youri A. Koutoyants  Université du Maine  5/10/2008  5/14/2008 
Michael M. Kozlov  Tel Aviv University  5/26/2008  5/31/2008 
Peter R. Kramer  Rensselaer Polytechnic Institute  1/8/2008  6/30/2008 
Daniel Kroll  North Dakota State University  5/27/2008  5/31/2008 
Thomas G. Kurtz  University of Wisconsin  5/10/2008  5/13/2008 
Yueheng Lan  University of California  5/10/2008  5/13/2008 
Lorene Lanier  University of Minnesota  5/27/2008  5/30/2008 
Juan Latorre  Rensselaer Polytechnic Institute  1/10/2008  6/30/2008 
Chang Hyeong Lee  Worcester Polytechnic Institute  5/6/2008  5/13/2008 
Juliet Lee  University of Connecticut  5/26/2008  5/30/2008 
Pilhwa Lee  University of Colorado  5/26/2008  5/30/2008 
Howard A. Levine  Iowa State University  5/13/2008  5/16/2008 
Anton Leykin  University of Minnesota  8/16/2006  8/15/2008 
James C. Liao  University of California  5/12/2008  5/13/2008 
Karen Lipkow  University of Cambridge  5/24/2008  5/31/2008 
Chun Liu  Pennsylvania State University  4/28/2008  5/3/2008 
Di Liu  Michigan State University  5/10/2008  5/13/2008 
Hailiang Liu  Iowa State University  5/11/2008  5/13/2008 
Wolfgang Losert  University of Maryland  5/27/2008  5/29/2008 
Roger Y. Lui  Worcester Polytechnic Institute  9/1/2007  6/30/2008 
Laura Lurati  University of Minnesota  9/1/2006  8/31/2008 
Dionisios Margetis  University of Maryland  5/26/2008  5/31/2008 
M. David Marks  University of Minnesota  5/11/2008  5/13/2008 
Bonnie Marsick  University of Minnesota  5/27/2008  5/30/2008 
Scott McKinley  Duke University  5/10/2008  5/14/2008 
Bence Melykuti  University of Oxford  5/10/2008  5/14/2008 
Vicenc Mendez  Autonomous University of Barcelona  5/1/2008  5/31/2008 
Tobias Meyer  Stanford University  5/26/2008  5/29/2008 
Paul Milewski  University of Wisconsin  5/26/2008  5/30/2008 
Ezra Miller  University of Minnesota  9/1/2007  6/30/2008 
Hitesh Mistry  University of Dundee  5/10/2008  5/14/2008 
Alex Mogilner  University of California  5/26/2008  6/1/2008 
Michael Monine  Los Alamos National Laboratory  5/10/2008  5/13/2008 
Denise Montell  Johns Hopkins University  5/26/2008  5/30/2008 
Yoichiro Mori  University of British Columbia  5/26/2008  6/1/2008 
Ambarish Nag  Los Alamos National Laboratory  5/10/2008  5/14/2008 
Timothy Newman  Arizona State University  9/1/2007  6/30/2008 
Duane Nykamp  University of Minnesota  9/1/2007  6/30/2008 
David Odde  University of Minnesota  1/9/2008  6/30/2008 
James Oliver  University of Oxford  5/24/2008  5/31/2008 
Dietmar Bernhard Ölz  Universität Wien  5/25/2008  6/8/2008 
Hans G. Othmer  University of Minnesota  9/1/2007  6/30/2008 
John Sandy Parkinson  University of Utah  5/27/2008  5/30/2008 
Johan Paulsson  Harvard Medical School  5/11/2008  5/13/2008 
Bobby Philip  Los Alamos National Laboratory  4/16/2008  5/30/2008 
Jonathan Popko  University of Minnesota  5/27/2008  5/30/2008 
Lea Popovic  Concordia University  5/10/2008  5/13/2008 
Mary Porter  University of Minnesota  5/27/2008  5/30/2008 
Alka A. Potdar  Vanderbilt University  5/26/2008  5/30/2008 
Aravind R. Rammohan  Corning  5/26/2008  5/30/2008 
Chris Rao  University of Illinois at UrbanaChampaign  5/26/2008  5/30/2008 
Muruhan Rathinam  University of Maryland Baltimore County  5/10/2008  5/13/2008 
Eric J. Rawdon  University of St. Thomas  1/10/2008  6/30/2008 
Michael C. Reed  Duke University  5/11/2008  5/13/2008 
Grzegorz A. Rempala  University of Louisville  5/11/2008  5/14/2008 
Erin Rericha  University of Maryland  5/26/2008  5/30/2008 
Alexander Roitershtein  University of British Columbia  5/10/2008  5/14/2008 
Howard Salis  University of California  5/10/2008  5/13/2008 
Sebastian Ambrose Sandersius  Arizona State University  5/1/2008  6/14/2008 
Fadil Santosa  University of Minnesota  4/23/2008  5/2/2008 
Jeffery G. Saven  University of Pennsylvania  3/19/2008  6/10/2008 
Christian Schmeiser  Universität Wien  5/26/2008  5/31/2008 
Deena Schmidt  University of Minnesota  9/1/2007  8/31/2009 
Casey SchneiderMizell  University of Michigan  5/26/2008  5/31/2008 
Sven A. Sewitz  University of Cambridge  5/24/2008  5/31/2008 
Chehrzad Shakiban  University of Minnesota  9/1/2006  8/31/2008 
ShagiDi Shih  University of Wyoming  5/26/2008  5/31/2008 
Julie Simons  University of Wisconsin  5/26/2008  5/30/2008 
Vassilios Sotiropoulos  University of Minnesota  5/11/2008  5/13/2008 
Rishi Srivastava  University of Wisconsin  5/10/2008  5/14/2008 
Andrew Stein  University of Minnesota  9/1/2007  8/31/2009 
Magdalena Stolarska  University of St. Thomas  5/27/2008  5/30/2008 
Sean Sun  Johns Hopkins University  5/26/2008  5/30/2008 
Vladimir Sverak  University of Minnesota  9/1/2007  6/30/2008 
Tatyana Svitkina  University of Pennsylvania  5/26/2008  5/30/2008 
Csilla Szabo  Rensselaer Polytechnic Institute  5/26/2008  5/31/2008 
David Tello  Arizona State University  5/10/2008  5/14/2008 
Julie A. Theriot  Stanford University  5/26/2008  5/30/2008 
Marcus John Tindall  University of Oxford  5/26/2008  5/31/2008 
Mahesh Tirumkudulu  Indian Institute of TechnologyBombay  5/25/2008  5/31/2008 
Margaret A Titus  University of Minnesota  5/27/2008  5/30/2008 
Lev S. Tsimring  University of California, San Diego  5/10/2008  5/13/2008 
Yuhai Tu  IBM  5/27/2008  5/30/2008 
Erkan Tüzel  University of Minnesota  9/1/2007  8/31/2009 
Melissa Vellela  University of Washington  5/10/2008  5/13/2008 
K.V. Venkatesh  Indian Institute of TechnologyBombay  5/25/2008  5/31/2008 
Jorge Vinals  McGill University  5/8/2008  5/14/2008 
Rajitha Vuppula  Indian Institute of TechnologyBombay  5/26/2008  5/30/2008 
Jin Wang  SUNY  5/11/2008  5/14/2008 
Jin Wang  SUNY  5/26/2008  5/30/2008 
Yuli Wang  University of Massachusetts Medical School, Worcester Campus  5/26/2008  5/30/2008 
Zhian Wang  University of Minnesota  9/1/2007  8/31/2009 
Alissa Weaver  Vanderbilt University  5/26/2008  5/30/2008 
Cornelius Weijer  University of Dundee  5/26/2008  5/31/2008 
Hans Weinberger  University of Minnesota  2/13/2008  6/30/2008 
Darren James Wilkinson  University of Newcastle upon Tyne  5/10/2008  5/13/2008 
Ruth J. Williams  University of California, San Diego  5/10/2008  5/13/2008 
Charles Wolgemuth  University of Connecticut Health Center  5/26/2008  5/30/2008 
Zhijun Wu  Iowa State University  9/4/2007  6/1/2008 
Yuan Xiong  Johns Hopkins University  5/26/2008  5/30/2008 
Chuan Xue  University of Minnesota  5/27/2008  5/30/2008 
Richard Yamada  University of Michigan  5/10/2008  5/14/2008 
Richard Yamada  University of Michigan  5/25/2008  5/30/2008 
John Yin  University of Wisconsin  5/10/2008  5/13/2008 
Hongchao Zhang  University of Minnesota  9/1/2006  8/31/2008 
Zhigang Zhang  University of HoustonDowntown  5/10/2008  5/14/2008 
Shan Zhao  University of Alabama  5/26/2008  5/29/2008 
Xiaoming Zheng  University of Michigan  5/26/2008  5/30/2008 