Monday, March 3, 2014 - 9:00am - 9:50am
Sanjeevi Krishnan (University of Pennsylvania)
Homology on semimodule-valued sheaves naturally generalizes network flows from the setting of numerical capacity constraints to other sorts of constraints (e.g. stochastic, multicommodity). In this talk, we present new work relating the algebraic structure of flows with local network properties and algebraic properties of the ground semiring.
Wednesday, September 5, 2012 - 2:00pm - 3:00pm
Edward Ott (University of Maryland)
We consider Boolean models of the dynamics of interacting genes. Stability is defined for a large Boolean network by imagining two system states that are initially close in the sense of Hamming distance and asking whether or not their evolutions lead to subsequent divergence or convergence.
Tuesday, February 28, 2012 - 11:15am - 12:00pm
Natasha Przulj (Imperial College London)
Sequence-based computational approaches have revolutionized biological understanding. However, they can fail to explain some biological phenomena. Since proteins aggregate to perform a function instead of acting in isolation, the connectivity of a protein interaction network (PIN) will provide additional insight into the inner working on the cell, over and above sequences of individual proteins. We argue that sequence and network topology give insights into complementary slices of biological information, which sometimes corroborate each other, but sometimes do not.
Monday, February 27, 2012 - 4:00pm - 4:15pm
John Pinney (Imperial College London)
The field of systems biology has emerged from a confluence of technological advances (DNA sequencing, gene expression profiling, proteomics, metabolomics etc.) and a “systems-level” understanding of biological processes, supported by network theory. Network models are essential in the interpretation of experimental results and, increasingly, in predicting the behaviour of cellular systems subject to experimental perturbations.
Thursday, March 1, 2012 - 3:00pm - 3:45pm
Joel Bader (Johns Hopkins University)
Biological networks are dynamic, driven by processes such as development and disease that change the expressed genes and proteins and modify interactions. Understanding how networks remodel could reveal the etiology of developmental disorders and suggest new drug targets for infectious disease. We present new methods that predict how networks change over time through joint analysis of time-domain and static data.
Tuesday, February 28, 2012 - 10:15am - 11:00am
Eric Kolaczyk (Boston University)
The set of tools for thinking hard about sampling and measurement-level aspects of scientific studies is among the earliest areas of statistics to
Thursday, October 27, 2011 - 3:00pm - 4:00pm
Stuart Geman (Brown University), Matthew Harrison (Brown University)
The spiking dynamics of simultaneously recorded neurons from a small region of cortex reflect the local network structure of excitatory and inhibitory connections between observed neurons, as well as the time varying response of the neurons to their many unobserved and correlated inputs. Inference about the local network is easily contaminated by these unobserved nonstationary influences. We have been exploring conditional inference as an approach for statistically isolating local network dynamics from background nonstationarities.
Monday, October 24, 2011 - 4:15pm - 5:15pm
Preferential attachment is a powerful mechanism explaining the emergence of scaling in growing networks. If new connections are established preferentially to more popular nodes in a network, then the network is scale-free. Here we show that not only popularity but also similarity is a strong force shaping the network structure and dynamics. We develop a framework where new connections, instead of preferring popular nodes, optimize certain trade-offs between popularity and similarity.
Friday, April 25, 2008 - 9:30am - 10:10am
Anand Asthagiri (California Institute of Technology)
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
Thursday, April 24, 2008 - 10:20am - 11:00am
Christopher Myers (Cornell University)
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


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