Anand R. Asthagiri (California Institute of Technology) 
Predicting quantitative diversification of multicellular
phenotype 
Abstract: Networks of biological signals guide cells to form
multicellular patterns and structures. Understanding the
design and function of these complex networks is a fundamental
challenge in developmental biology and has clear implications
for biomedical applications, such as tissue engineering and
regenerative medicine. Signaling networks are composed of
highly interconnected pathways involving numerous molecular
components. Precisely to what extent multicellular structures
are susceptible to quantitative variations in underlying
signals and to what extent Nature has utilized this mechanism
of "quantitative diversification" during evolution are unclear.
I will describe a computational framework that we have
developed to explore and to quantify the multicellular
diversity that emerges from signaling perturbations. We have
applied this method to study vulval development in C.
elegans.
The approach is not only effective in predicting the molecular
genetics of multicellular patterning, but also gauges the
capacity of this signaling network for creating phenotypic
diversity. In fact, model predictions strongly correlate to
multicellular phenotypes observed across ten species related to
C. elegans. These results suggest that systemslevel modeling
can shed insight into the evolutionary trajectories of
regulatory networks that gave rise to divergent multicellular
phenotypes.

Anand R. Asthagiri (California Institute of Technology) 
Quantitative systems analysis of multicellular morphodynamics 
Abstract: The goal of my research program is to better understand how biological circuits program multicellular phenotypes. While a major focus of the group involves phenotypes associated with human epithelial systems, we have also drawn upon more tractable model systems, such as C. elegans and yeast, to glean deeper fundamental insights. Each of these three biological systems provides unique advantages that my lab has sought to exploit using a combination of computational and quantitative experimental approaches. 
Bassam Bamieh (University of California) 
A design principle in biochemical reaction networks
based on realization theory 
Abstract: We attempt to address the question of why there are so many intermediate species in biochemical
reaction networks using an idea from realization theory. We describe how prescribed biological
function can be designed with very low dimensional models, which are however not implementable
with the physically allowable biochemical reaction mechanisms. It then becomes apparent that
the introduction of a large number of intermediate species can be interpreted as a realization technique
to enable the implementation of prescribed function with the available dynamical building blocks.
By reversing this realization scheme, we propose a model reduction paradigm for biochemical reaction networks. 
Banu Baydil (Rensselaer Polytechnic Institute) 
Parameterization for mesoscale ocean transport through random flow models 
Abstract: We describe a mathematical approach based on homogenization theory toward representing the effects of mesoscale coherent structures, waves, and turbulence on largescale transport in the ocean. We are developing a systematic parameterization strategy by building up deterministic and random subgridscale flow models in an increasing hierarchy of complexity, coupling the results from numerical simulations of cell problems with asymptotic analysis with respect to key nondimensional physical parameters such as Pecl'{e}t and Strouhal numbers. 
Robert C. Berwick (Massachusetts Institute of Technology) 
Evodevo and the syntactic and semantic 'design features' of
human language 
Abstract: Human language has long captured the imagination of biological
researchers, but the gulf separating 'computation,' 'biology', and
'language' has been equally longstanding: the classical biological
problem of how to bridge between a genotype and a phenotype,
in this case, perhaps the most complex behavioral phenotype
we know of. The aim of this talk is to show how recent developments
in linguistic theory bridge this 'abstraction gap' by
illuminating some of the modular design properties of human language,
illustrating that despite its apparent surface complexity, human language's
core seems to be far simpler than has been previously supposed,
potentially reducible to a single, simple, basic operation that derives
all the seemingly special properties of human language. Further, by
positing this modular approach, we can shed light on the computational
interfaces of human language to the systems of speech/motor production and parsing,
as well as internal systems of inference; here there seems to be
a natural, and expected kind of 'impedance matching,' with the design
seemingly forced to follow constraints imposed by considerations of
semantic interpretation, rather than considerations of computational
complexity in parsing or production. Finally, this new modular view leads naturally to
evolutionary considerations as to how these components arose in the course of
evolution, in this case, a more 'saltational' view than is generally supposed.

Sudin Bhattacharya (The Hamner Institutes for Health Sciences) 
A transcriptional regulatory switch underlying Bcell terminal
differentiation and its disruption by dioxin 
Abstract: Joint work with Qiang Zhang^{(1)}, Melvin E. Andersen^{(1)}
and Rory B.
Conolly^{(2)}.
The terminal differentiation of B cells in lymphoid organs into
antibodysecreting plasma cells upon antigen stimulation is a
crucial
step in the humoral immune response. The architecture of the
Bcell
transcriptional regulatory network consists of coupled
mutuallyrepressive feedback loops involving the three
transcription
factors Bcl6, Blimp1 and Pax5. This structure forms the basis
of an
irreversible bistable switch directing the Bcell to plasma
cell
differentiation process  i.e., the switch remains on even
after the
activating stimulus (antigen) is removed. The environmental
contaminant
2,3,7,8tetrachlorodibenzopdioxin (TCDD) is known to suppress
the
humoral immune response by interfering with this
differentiation
program. We have developed a computational model of the
biochemical
pathways that regulate Bcell differentiation, and the
molecular
mechanism by which TCDD impairs this process through the action
of the
aryl hydrocarbon receptor (AhR). Using a kinetic model and
bifurcation
analysis, we propose that TCDD regulates the proportion of
Bcells that
differentiate into plasma cells by raising the threshold dose
of antigen
lipopolysaccharide (LPS) required to trigger the
differentiation switch.
We also show that stochastic modeling of gene expression, which
allows
celltocell differences in content of signaling proteins,
introduces
distributional characteristics to the timing and probability of
differentiation among a population of Bcells. This
celltocell
variability is likely to be a key determinant of doseresponse
and
sensitivity of individual cells to differentiation.
^{(1)} Division of Computational Biology, The Hamner Institutes
for Health
Sciences, Research Triangle Park, NC 27709, USA
^{(2)}
National Center for Computational Toxicology, U.S.
Environmental
Protection Agency, Research Triangle Park, NC 27711, USA

Hans C. Breiter MD (Massachusetts General Hospital) 
Recurrent and robust patterns underlying human relative preference, and associations with brain circuitry plus genetics 
Abstract: Joint work with BW Kim.
The Phenotype Genotype Project on Addiction and Depression (PGP), with support from the Departments of Neurology, Psychiatry, and Radiology, and Center for Human Genetic Research; Massachusetts General Hospital and Harvard Medical School; Boston, MA, USA
Positive and negative preferences can be assessed by keypress procedures that quantify (i) decisionmaking regarding approach, avoidance, indifference, and ambivalence responses, and (ii) judgments that determine the magnitude of approach and avoidance. Most prior studies of relative preference have not used keypress procedures, but have used ratings of personal utility, as a composite index of approach and avoidance. Such composite ratings can be calibrated against an absolute framework such as the macroeconomic pricing of commodities, as is done with Prospect Theory. It leaves open questions of whether or not splitting composite ratings of utility into approach and avoidance measures reveals any patterns in behavior, as observed for Prospect Theory. Such patterns might include a tradeoff relationship between approach and avoidance, or a value function that might not need an absolute macroeconomic framework. We assessed these questions across multiple sets of healthy control sub
jects with a keypress procedure, and found a set of patterns for approach and avoidance are (i) recurrent across many stimulus types, and (ii) robust to the injection of noise. These patterns include: (a) a preference tradeoff plot that counterbalances approach and avoidance responses and represents biases in preference and consistency/uncertainty of preference, (b) a value function linking preference intensity to uncertainty about preference, and (c) a preference saturation plot that represents how avoidance actions are overdetermined relative to approach actions. One can consider this set of patterns to be a form of relative preference theory (RPT), since they meet the same requirements for recurrency and robustness to noise as the WeberFechnerStevens Law. As with the value function for Prospect Theory, the value function in RPT has a steeper slope for negative relative to positive preferences (i.e., loss aversion), and can be described as a power law, or a logarithmic functi
on. All of these patterns show symmetry between group and individual data in that they have similar mathematical formulations as manifolds or boundary asymptotes for group data, or as fitted functions for individual data across multiple variables. These patterns verify known biases between females and males regarding viewing beautiful and average faces. When used to evaluate cocaine dependent subjects versus healthy controls, these patterns quantify the phenotype of the restricted behavioral repertoire observed in addiction. When used as regressors in the analysis of fMRI data, RPT measures are associated with significant BOLD signal change across a set of reward/aversion brain regions. Both keypress behavior and fMRI BOLD signal can be further associated with polymorphisms is genes such as CREB1 and BDNF. Given RPT scaling between groups of subjects and individuals, further work is warranted to assess if scaling can be achieved to brain circuitry and genetics.

Dmitri Chklovskii (Cold Spring Harbor Laboratory) 
Engineering theory of neuronal shape 
Abstract: The human brain is a network containing a hundred billion neurons, each communicating with several thousand others. As the wiring for neuronal communication draws on limited space and energy resources, evolution had to optimize their use. This principle of minimizing wiring costs explains many features of brain architecture, including placement and shape of many neurons. However, the shape of some neurons and their synaptic properties remained unexplained. This led us to the principle of maximization of brain’s ability to store information, which can be expressed as maximization of entropy. Combination of the two principles, analogous to the minimization of free energy in statistical physics, provides a systematic view of brain architecture, necessary to explain brain function.

Robert Cummins (University of Illinois at UrbanaChampaign) 
Philosophy of biology: Function/design: I 
Abstract: The concept of function has a tainted history in science and philosophy, having been mated to teleology and harnessed to pull various discredited theories. This presentation begins with a short history designed to give a sense of what made the concept of function problematic. Current philosophical attention to the problem revolves ellipselike around two foci: systematic or system relative accounts, and selectionist accounts. These are briefly described, and the systematic account is defended on the grounds that it captures a ubiquitous explanatory strategy in science and engineering, and that selectionist accounts are, in contrast, limited to evolutionary biology (and, perhaps, designed artifacts), and make it difficult to articulate various important issues in evolutionary biology itself. 
Hana ElSamad (University of California) 
Response tuning through specific feedback architectures 
Abstract: Determining proper sensitivity to incoming signals is important to all regulated biological systems, but is especially crucial for responses that involve an irreversible decision. Using Xenopus oocytes maturation as an example, we illustrate how entangled feedback architectures can be used to integrate the multiple signals needed to make the switchlike, irreversible transition from interphase to meiosis. We also discuss how the specific topologies of these feedback loops modulate the switching threshold as a function of the progesterone input. 
W. Tecumseh Fitch (University of St. Andrews) 
Design principles in the evolution of animal
communication 
Abstract: The evolution of communication provides one of the few
examples in evolutionary biology where principles of physical
acoustics, interacting with developmental constraints on
physiology and motor control, have clear and predictable
effects on evolutionary outcomes. I will provide two clear
example of this, relating to basic physiological constraints
on fundamental and formant frequencies, and various
morphological "tricks" that vertebrates have evolved to evade
such constraints. Then I will explore a more speculative
hypothesis concerning the neural control of vocalization:
that the production of complex learned vocalizations requires
particular neural correlates (direct frontobulbar
connections) and behaviours ("babbling" or subsong), and
furthermore that constraints on development may mean that
there are only a few ways to achieve such vocal capabilities,
raising the interesting possibility of "deep homology" in the
evolution of communication. This hypothesis, though
speculative, is testable and is consistent with the most
recent information on genes involved in vocal learning. 
Christodoulos A. Floudas (Princeton University) 
Recent advances and challenges in deterministic global
optimization 
Abstract: In this presentation, we will provide an overview of the
research progress in global optimization. The focus will be on
important contributions during the last five years, and will
provide a perspective for future research opportunities. The
overview will cover the areas of (a) twice continuously
differentiable constrained nonlinear optimization, and (b)
mixedinteger nonlinear optimization models. Subsequently, we
will present our recent fundamental advances in (i) convex
envelope results for multilinear functions, (ii) a piecewise
quadratic convex underestimator for twice continuously
differentiable functions, (iii) the generalized alphaBB
framework, (iv) our recently improved convex underestimation
techniques for univariate and multivariate functions, and (v)
generalized pooling problems. Computational studies will
illustrate the potential of these advances. 
Daniel Forger (University of Michigan), John J. Tyson (Virginia Polytechnic Institute and State University) 
Computer Lab 1: Phase planes, vector fields, nullclines, bifurcations 
Abstract: How to use WinPP and XPP. Models of bistability and oscillations. Drawing phase plane portraits. How portraits depend on parameter values. Oneparameter bifurcation diagrams. 
Daniel Forger (University of Michigan), John J. Tyson (Virginia Polytechnic Institute and State University) 
Computer Lab 2: Modeling exercises 
Abstract: Building simple models of cell cycle, circadian rhythm, programmed cell death, glycolysis, Ca^{2+} oscillations, etc.

Daniel Forger (University of Michigan) 
Lecture 4: Stochastic modeling of molecular regulatory networks 
Abstract: Relation between stochastic and deterministic formalisms. Two discrete simulation methods proposed by Gillespie. 1/N relationship. Noise induced oscillations. Chemical Langevin equations and hybrid methods. Introduction to simulation packages. 
Daniel Forger (University of Michigan) 
Lecture 5: Models of circadian rhythms 
Abstract: Basic properties of circadian clocks. Goodwin and early models. More realistic models. Model predictions and their experimental validation. Temperature Compensation. Unanswered questions. 
Daniel Forger (University of Michigan), John J. Tyson (Virginia Polytechnic Institute and State University) 
Computer Lab 3: Stochastic Simulation 
Abstract: Simulations of simple models of genetic networks using the Gillespie Method. Comparison of behavior for small and large number of chemical events. 
Daniel Forger (University of Michigan) 
Lecture 6: Synchronization and phase resetting 
Abstract: Phase Response Curves, Phase Transition Curves and Winfree’s Type 0 vs. Type 1 distinction. Global vs. local coupling. Pulse vs. sustained coupling. Coupling induced rhythmicity. Relationship between phase resetting and coupling. 
Thomas Hillen (University of Alberta) 
A user's guide to PDE models for chemotaxis 
Abstract: The mathematical analysis of chemotaxis (the movement of biological cells or organisms in response to chemical gradients) has developed into a large and diverse discipline. A simplified version of the original KellerSegel model (called "minimal model") displays already selfaggregation properties, which are expressed, in higher dimensions, through finite time blowup solutions.
Based on this "minimal" model we study modifications and variations of chemotaxis models. These variations are partly motivated through biological realism and partly through mathematical convenience. We will present results on global existence and blowup for the modified models and discuss spatial pattern formation for those models with global solutions. Typical patterns show an interesting merging and emerging dynamics. (joint work with Kevin Painter, Heriot Watt, Edinburgh) 
Mustafa H. Khammash (University of California) 
Transient stochastic analysis of gene networks 
Abstract: Many gene regulatory networks are modeled at the mesoscopic scale, where
chemical populations change according to a discrete state (jump)
Markov process. The chemical master equation for such a process is
typically infinite dimensional and unlikely to be computationally
tractable without reduction. The recently proposed Finite State
Projection technique allows for a bulk reduction of the CME while
explicitly keeping track of its own approximation error. We show how a
projection approach can be used to directly determine the
statistical distributions for stochastic gene switch rates, escape times,
trajectory periods, and trajectory bifurcations, and to evaluate how likely
it is that a network will exhibit certain behaviors during certain intervals
of time. We illustrate these ideas through the analysis of the switching
behavior of a stochastic model of Gardner's genetic toggle switch. 
David Kleinfeld (University of California, San Diego) 
Circuitry, psychophysics, and electrophysiology of
touch in the rat vibrissa system 
Abstract: I will review the control of localization by rat vibrissa system. 
Jeff Knisley (East Tennessee State University) 
From neurons to neural networks 
Abstract: Artificial Neural Networks (ANN's) are machinelearning algorithms that are often used as classifiers in molecular and computational biology. Originally, ANN's were inspired by in vivo models of axonal and dendritic neuroelectric activity, especially the classical models of Hodkgin, Huxley, and others. Much of the successive development of ANN's, as well as the parallel development of other approaches such as Support Vector Machines, has been as a means of addressing issues such as overfitting and hard margins which arise in machine learning applications. To address these issues, ANN's have borrowed from a variety of sources in computer science, physics, and cognitive psychology, but not so much from the everimproving neuronal models which provided their initial inspiration. We will revisit much of the historical and algorithmic development of ANN's, with the goal being that of suggesting the types of ANN's that might be inspired by more recent developments in dendritic electrotonic models. 
Peter R. Kramer (Rensselaer Polytechnic Institute) 
Informal working seminar on stochastic techniques in microbiology 
Abstract: Based on the response I received, I propose to do the following:
I'll begin by briefly advertising the various projects in which I am
actively interested since
several new people have arrived since I did this before.
For most of the hour,
I will again discuss the stochastic immersed boundary method for simulating
microbiological systems, but the emphasis
will not be so much on the method itself, but on more general issues raised in the
design and analysis of the method. Possible topics for discussion include:
1) How do you add noise in a "correct" way to a physical system?
2) How can you check that a stochastic numerical method is simulating the
correct statisical physics?
3) How can you use the method of stochastic mode reduction to analyze the
effective coarsegrained behavior of a complex stochastic systems when
a separation of time scales can be exploited. If desired, I can illustrate the
stochastic mode reduction approach on the simple equation I described
last week (showing that, on longtime scales, thermally driven Newton's law
for particle motion (second order stochastic differential equation) can
be approximated by a first order driftdiffusion model).
I'll direct the discussion as best I can toward the interests of those who choose to
attend, and pitch the technical level accordingly. 
Peter R. Kramer (Rensselaer Polytechnic Institute) 
Analysis of physical fidelity of stochastic simulation method for
microbiological system 
Abstract: One approach to accelerating biomolecular simulations is to
simulate explicitly
only certain slow degrees of freedom of interest, incorporating
the effects of
the remaining “fast” variables through effective stochastic
models. We illustrate
a systematic multiscale stochastic mode reduction procedure on
a simple model
problem with metastability – a high potential energy barrier
separating different
conformational states. Metastability is a prevalent feature in
biomolecular systems.
We show in particular how the metastability can lead to various
effective
stochastic equations for the slow degrees of freedom depending
on the relations
between the physical parameters and properties of the potential
energy landscape.
This work is in collaboration with Jessika Walter at Ecole
Polytechnique
Federale de Lausanne and Christof Schuette at the Free
University of Berlin.
Time permitting, I will also discuss some general observations
concerning the
application of multiple scale asymptotics to problems with
three (or more) active
time scales (joint work with Adnan Khan (Lahore) and Robert E.
Lee DeVille
(University of Illinois)). 
Oluwole Daniel Makinde (University of Limpopo) 
Computational hemodynamics analysis in large blood
vessels: Effects of hematocrit variation on flow stability 
Abstract: Understanding the effects of blood viscosity
variation plays
a very crucial role in hemodynamics, thrombosis and
inflammation and
could provide useful information for diagnostics and therapy of
(cardio)
vascular disease. Blood viscosity, which arises from frictional
interactions between all major blood constituents, i.e. plasma,
plasma
proteins and red blood cells, constitutes blood inherent
resistance to
flow in the blood vessel. Because red blood cells (RBCs) are
the main
constituent of the cellular phase of blood, white blood cells
and
platelets normally do not have a great influence on whole blood
viscosity. When blood flows through a vessel, it tends to
separate in
two different phases. In direct contact with the wall a low
viscosity
phase exists, which is deficient in cells and rich in plasma
and acts as
a lubricant for the blood transport. In the central core region
of the
vessel a high viscosity phase exists, which depends on the
hematocrit.
In this paper, the nature and stability of blood flow in a
large artery
is investigated numerically using a spectral collocation
technique with
expansions in Chebyshev polynomials. The study reveals that a
rise in
hematocrit concentration in the central core region of a large
artery
has a stabilizing effect on the flow.
Keywords: Arterial blood flow; Hematocrit; Variable viscosity;
Temporal
stability; Chebyshev spectral collocation technique
References:
1.) Pedley T. J.: The Fluid Mechanics of Large Blood Vessels.
Cambridge
University Press, London, 1980.
2.) Makinde O. D.: MagnetoHydromagnetic Stability of plane
Poiseuille
flow using MultiDeck asymptotic technique. Mathematical &
Computer
Modelling Vol. 37, No. 34, 251259, 2003.
3.) Makinde O. D. and Mhone P. Y.: Temporal stability of small
disturbances in MHD JefferyHamel flows. Computers and
Mathematics with
Applications, Vol. 53, 128–136, 2007.
4.) Makinde O. D.: Entropygeneration analysis for
variableviscosity
channel flow with nonuniform wall temperature. Applied Energy,
Vol. 85,
384393, 2008. 
Ezio Marchi (Instituto de Matemática Aplicada) 
The advantage of two step transport problem 
Abstract: Here, we present a transport model which indroduce a variant in
the transport theory. Here the mass or merchandize in order to
go from a port to a destination must pass through a deposit,
without accumulation. It is possible to consider deposit
constraint. We obtain the corresponding linear program
associated to it, as well as the dual. The application of this
model to biological sciences, specially in human body, lungs, blood and biological systems seems promising.
We obtain the dual, which appear in a natural way from the
incidence matrix, which has many interesting properties.
The rank is the number of the ports, deposits plus the
destination minus on.
We study and characterize all the extremals extending the ideas
of JurkarRyser, and von Neumann in the case of the classical
transpot model. A relation with the material in the papers by
E. Marchi:Z.Wahrscheinlichtheorie verww. geb,12,220230
(1969) and 23,717, (1972), is underway.
We were succesful to extend this theory to mixed model
considering that in a deposit, there exists the possibility to
stay or passing. This rich study permits more potentiality to
the tools. An extension to several steps has been performed and
its potentiality for applications is vast.

Ezio Marchi (Instituto de Matemática Aplicada) 
Biological project: The construction of the cycle in LotkaVolterra of nspecies 
Abstract: LotkaVolterra equations are very famous by two biological
spices. Generally the most common presentations are in ecology
science. We study this models without considering the
antisymmetric condition among the parameters.(see Marchi and
Velasco Revista Mexicana de Fisica,36,No4(1990)665679).We
obtain a movement constant for the systems which satisfies the
Hamilton equation. For more than two interacting species the
result the Volterra result are biologically improper. This is
due to the antisymetric conditions. Following Volterra original
methods combined with the variation of Montroll et. all, who
applied such equation to maser and laser, we obtain some
powerful personal procedure that we applied to the three and
four species obtaining and general condition and build the
cycle. Moreover this can be extended for an arbitrary number of
biological species obtaining constructively the cycle. The
background idea is to delete one variable at that time using
partial differentials equations of second power of the first
order. Furthermore we consider the conservation of the density
of point in the fase space (LIoville theorem) an we postulate
that the same distributions following the Gibbs Canonical Law.
By the way Prigogyne at all applied LotkaVolterra system in
nonequilibrium thermodynamics. Moreover, there are several
applications of LotkaVolterra to the theory of membrane. We
can show you some of them. Finally, we point out that
there are about four hundred papers in the subject in the last
ten years, and non of them apply our methods. The potentiality
for real application is very important even if the system of
LotkaVolterra is asymptotically unstable.

Richard M. Murray (California Institute of Technology) 
Tutorial on feedback control theory 
Abstract: This talk will present an introduction to some of the key principles and tools from feedback control theory. The two main design principles that will be explored is the role of feedback as a tool for managing uncertainty, and the use of feedback to design the dynamics of a system. Examples from engineering and nature will be used to illustrate some of the key concepts and techniques. 
Richard M. Murray (California Institute of Technology) 
Architecture: Human engineered systems 
Abstract: This talk will present an engineering perspective on "architecture" in complex engineered systems. The role of protocols and interfaces will be emphasized, along with other architectural concepts such as modularity, evolvability and reusability. Examples of architecture as applied to autonomous vehicles will be used to illustrate modern engineering design tools. 
Chad L. Myers (University of Minnesota) 
Discovering biological networks by integrating diverse genomic data 
Abstract: Understanding protein function and modeling biological networks is a key challenge in modern systems biology. Recent developments in biotechnology have enabled highthroughput measurement of several cellular phenomena including gene expression, proteinprotein interactions, protein localization and sequence. The wealth of data generated by such technology promises to support computational prediction of network models, but so far, successful approaches that translate these data into accurate, experimentally testable hypotheses have been limited. I will discuss key insights into why we face this imbalance between genomic data and established knowledge and present computational approaches for addressing these challenges. Specifically, I will present a Bayesian framework for integrating diverse genomic data to predict biological networks. I will describe the machine learning methods as well as important data visualization features that support a public, webbased system for userdriven search of network predictions from genomic data. Using our approach, we correctly predicted new functions for approximately 100 genes in yeast. I will discuss experimental validation for these predictions as well as our recent efforts to use predicted network models for directing largescale genetic interaction screens. 
Chris J. Myers (University of Utah) 
Design principles in synthetic biology 
Abstract: Recently, numerous engineers have demonstrated that genetic circuits can
be effectively modeled and analyzed utilizing methods originally
developed for electrical circuits leading to new understanding of their
behavior. If this is possible, then it may also be possible to design
synthetic genetic circuits that behave like particular electrical
circuits such as switches, oscillators, and communication networks.
Synthetic genetic circuits have the potential to help us better
understand how microorganisms function, produce drugs more economically,
metabolize toxic chemicals, and even modify bacteria to hunt and kill
tumors. There are, however, numerous challenges to design with genetic
material. For example, genetic circuits are composed of very noisy
components making their behavior more asynchronous, analog, and
nondeterministic in nature. Therefore, design methods must be adapted
to consider these issues. Interestingly, future electrical circuits may
soon also face these challenges which opens up the very intriguing idea
that these new design methods may in the future be utilized to produce
more robust and power efficient electrical circuits. 
Christopher R. Myers (Cornell University) 
Cellular information processing in the face of promiscuity and sloppiness 
Abstract: Cellular information processing is carried out by complex biomolecular networks
that are able to function reliably despite environmental noise and genetic mutations.
The robustness and evolvability of biological systems is supported in part by neutral
networks and neutral spaces that allow for the preservation of phenotype despite underlying
genotypic variation. This talk will describe two such spaces. The first are sequence niches
that emerge in the process of satisfying constraints needed to avoid crosstalk among sets
of promiscuous and paralogous proteins. The second are highly anistropic, sloppy parameter
spaces that arise in multiparameter models of biomolecular networks. Implications of
the structure of these spaces for cellular information processing will be discussed. 
Theoden Netoff (University of Minnesota) 
Dynamical approaches to understanding epilepsy 
Abstract: Epilepsy is a dynamical disease of networks of neurons. I will talk about the different mathematical analysis tools used in our lab to help us understand the basic mechanisms of epilepsy. To understand this disease we need to bridge cellular activity to network activity. We use phase response curves to bridge changes at the cellular level to changes in behavior at the network level. To measure from hundreds of neurons simultaneously, we image slices of brain stained with calcium dyes. Processing the movies into a format that can be easily interpreted is another daunting task. We use independent component analysis and diffusion maps to process our data. 
Donald Pfaff (Rockefeller University) 
Reverse engineering the lordosis behavior neuronal circuit 
Abstract: For the simple reproductive behavior exhibited by female quadrupeds, the neural circuit and several genomic modules have been worked out (Drive, MIT Press, 1999). Feedback is most prominent in the hormonal mechanisms that support the behavior, whereas most of the behavioral dynamics exhibit feedforward relations. Emboldened by the success of the analysis of this simple behavior, we've enlarged the focus of our lab's work to encompass sexual arousal and generalized CNS arousal (Brain Arousal, Harvard Univ. Press, 2006). Shannon's information theoretical calculations probably apply to CNS arousal and its (universal) complementary phenomenon, habituation. We have speculated (BioEssays, August 2007) that in an animal or human at rest, CNS arousal systems live in a chaotic domain and quickly go through a phase transition to orderly dynamics as the animal or human responds. We are testing this theoretical idea with simulations of neural nets and with deep brain stimulation that raises the arousal level of a brain damaged animal.

Aravind R. Rammohan (Corning) 
Interaction of lipid bilayers with inorganic surfaces: experiments to modeling 
Abstract: Joint work with David R. Heine and Jitendra Balakrishnan.
Lipid bilayers typically serve as the scaffold for trans membrane protein receptors. These membrane protein receptors are targets for atleast 50% of the drug molecules developed by pharmaceutical companies. Drug development and testing typically is carried out in an invitro environment with these lipid membranes deposited on some synthetic surface organic or inorganic. Here we present details of the interactions between lipid bilayers and inorganic surfaces. The approach adopted to develop an understanding of these interactions has been a combination of a multiscale modeling framework in conjunction with specific experimental effort to complement the modeling effort. The multiscale modeling effort involves modeling the membrane surface interactions in detail at an atomistic level to looking at macroscopic membrane dynamics on surfaces with specific topologies. The experimental effort involves a combination of Surface Force Apparatus (SFA) and Atomic Force Microscopic (AFM) measurements to generate these insights. The talk will focus specifically on the role of surface topology in modulating membrane surface interactions. 
Eric J. Rawdon (University of St. Thomas) 
Size and shape of polymers 
Abstract: We use numerical simulations to investigate how chain length and
knotting in freely fluctuating knotted polymer rings affect their size
and shape. In particular, we find smallest containers, such as
rectangular boxes, spheres, and polyhedra, which contain the simulated
polymers. Ideal knots, and their relationship to the containers, also
will be discussed. 
Michael A. Savageau (University of California) 
Generic approach to construction of system design space 
Abstract: Determining quality of performance for a biological system is critical to identifying and elucidation its design principles. This important task is greatly facilitated by enumeration of regions within the system's design space that exhibit qualitatively distinct function. First, I will review a few examples of design spaces that have proved useful in revealing design principles for elementary gene circuits. Then I will present a recently developed approach to the generic construction of design spaces, illustrate its application to common classes of biochemical network motifs, and test predictions for a specific class with experimental data from human erythrocytes. 
Michael Schendel (Medtronic) 
Challenges for numerical analysis in medical device industries 
Abstract: This presentation will provide an overview of how numerical analyses are currently being used in the development and reliability investigations for implantable medical devices. Specific emphasis will be given on the current methods being used and additionally the barriers to the usefulness of these numerical predictions. 
Peter H. Schwartz (Indiana University Center for Bioethics and Indiana University School of Medicine) 
Pluralism about function 
Abstract: There is no question that "systematic" functional analysis, which explains the capacities or features of a system based on interactions among its parts, plays an important role in biology. But an important role is also played by "selectionist" functional analysis, where engineering or design principles carry explanatory weight based on the action of natural selection. While some have questioned the legitimacy of selectionist functional analysis, asserting that it involves illegitimate teleological notions or an incorrect understanding of evolutionary theory, careful work by philosophers of biology over the last 35 years has helped explicate and defend this approach. Uses of the concept of function in biology may be interpreted, based on context, in terms of one or the other type of functional analysis, and sometimes in terms of both, and thus pluralism about function should be embraced.

Anirvan Sengupta (Rutgers University) 
Modeling epigenetic silencing 
Abstract: We formulate a mathematical version of the conventional model
of maintenance of silencing in S. cerevisiae and analyze the conditions
for bistability as well as for formation of stationary boundaries.
Although the model is perhaps too simple, the structure of the
bifurcation diagram, describing parameter regions with different kinds
of qualitative behavior, is likely to be more robust. We can place some
of the known mutants in different regions of this diagram. One
interesting finding of this study is that, under some conditions, the
lowering of acetylation rates might have nonobvious consequences for
silencing. Possible improvements of the model as well as experimental
tests are discussed at the end. 
Eduardo D. Sontag (Rutgers University) 
Monotone input/output systems as a technique for modular analysis of biomolecular network dynamics

Abstract: This talk present the basic ideas underlying a method developed by the speaker
and his collaborators, for the analysis of the dynamics of certain biomolecular
systems.

John J. Tyson (Virginia Polytechnic Institute and State University) 
Lecture 1: Cell physiology, molecular biology and mathematical modeling 
Abstract: An introduction to cell growth and division, programmed cell death, cell differentiation, motility, and signaling. Basic molecular mechanisms governing these processes. Modeling molecular mechanisms with ordinary differential equations. 
John J. Tyson (Virginia Polytechnic Institute and State University) 
Lecture 2: Network motifs: sniffers, buzzers, toggles and blinkers 
Abstract: Simple models of regulatory motifs. Positive and negative feedback. Signalresponse curves and bifurcation diagrams. Adaptation. Ultrasensitivity. Bistability and oscillations. Simple bifurcations: saddlenode and Hopf. Homoclinic bifurcations. 
John J. Tyson (Virginia Polytechnic Institute and State University) 
Lecture 3: Cell cycle regulation 
Abstract: Physiological characteristics of the cell division cycle. Molecular biology of cyclindependent kinases. Simple model of bistability and oscillations in the CDK control system of frog eggs. More complex models of yeast cell cycles. Mammalian cell cycle and cancer. 
Dan Valente (Cold Spring Harbor Laboratory) 
Trajectory measures describing the locomotor behavior of Drosophila melanogaster in a circular arena 
Abstract: Measuring locomotor behavior of Drosophila in enclosed arenas is a powerful way to obtain a quantitative behavioral phenotype. The measures previously used for this purpose are relatively coarse, thus ignoring fine scale movements of the fly. More importantly, they do not explicitly take into account how the arena shapes the dynamics of the locomotion. We acquired data using a videotracking system to measure the trajectory of a fly over extended periods as it explores a circular arena. Based on this data, we present some metrics that take into consideration the dynamics of the trajectory, as well as the interactions of the trajectory with the geometry of the arena. The basic idea is to treat the locomotor trajectory of the fly as a stochastic process, and then estimate a set of marginal distributions of the probability measure describing this process. The measures include joint or individual distributions of position, speed, path curvature, interevent times and reorientation angles after stopping. These probability measures can be worked into other behavioral setups, are relatively easy to calculate using robust statistical estimation procedures, and can account for environmental effects on the behavior. They also serve as foundation for a quantitative stochastic process model of the walking behavior. 
Glenn Vinnicombe (University of Cambridge) 
Fundamental limitations on noise reduction in the cell 
Abstract: It is well known that, for a molecule being produced in an unregulated manner and degraded exponentially (constitutive gene expression for example) the intrinsic noise in the molecule number will be such that variance equals the mean. This talk will establish fundamental limits on the amount of noise reduction that can be obtained by regulation when delays in the mechanism (due to transport and the finite time required to synthesize intermediate molecules) and when that mechanism has limited information carrying capacity in the sense of Shannon (due to itself involving finite numbers of molecules). 
Haibin Wang (Cold Spring Harbor Laboratory) 
Evolution of song culture in the zebra finch 
Abstract: A number of Oscine songbirds are vocal learners. In these species, song shares a remarkable characteristic with human language: both are acquired through imitative learning. When reared in social and acoustic isolation, the Zebra Finch (the species of songbird under study here) can still sing, revealing the genetically encoded aspect of song. However, the isolate songs usually have longer syllables, and appear to be more variable than the wild type (WT) songs found in the wild or in laboratory colonies.
In order to understand how the isolate song might evolve over multiple generations, we successively trained naïve juveniles starting with an isolate founding father. The first generation learners are in turn used as tutors to train the next generation, and so on. Thus, tutoring lineages are established through a recursive training processes. We found that small, yet systematic variations accumulate over generations of training. Remarkably, the descendants’ song structures are gradually transformed towards WT songs. 