Monday, February 10, 2014 - 11:00 , Location: Skiles 006 , Professor Andeas Handel , Department of Epidemiology and Biostatistics, College of Public Health, UGA , firstname.lastname@example.org , Organizer:
Inoculum dose, i.e. the number of pathogens at the beginning of an infection, often affects key aspects of pathogen and immune response dynamics. These in turn determine clinically relevant outcomes, such as morbidity and mortality. Despite the general recognition that inoculum dose is an important component of infection outcomes, we currently do not understand its impact in much detail. This study is intended to start filling this knowledge gap by analyzing inoculum dependent patterns of viral load dynamics in acute infections. Using experimental data for adenovirus and infectious bronchitis virus infections as examples, we demonstrate inoculum dose dependent patterns of virus dynamics. We analyze the data with the help of mathematical models to investigate what mechanisms can reproduce the patterns observed in experimental data. We find that models including components of both the innate and adaptive immune response are needed to reproduce the patterns found in the data. We further analyze which types of innate or adaptive immune response models agree with observed data. One interesting finding is that only models for the adaptive immune response that contain growth terms partially independent of viral load can properly reproduce observed patterns. This agrees with the idea that an antigen-independent, programmed response is part of the adaptive response. Our analysis provides useful insights into the types of model structures that are required to properly reproduce observed virus dynamics for varying inoculum doses.
Rescheduled for March 12: Spatial epidemic models: lattice differential equation analysis of wave and droplet-like behaviorFriday, January 31, 2014 - 11:00 , Location: Skiles 005 , Chi-Jen Wang , Iowa State , Organizer: Christine Heitsch
Spatially discrete stochastic models have been implemented to analyze cooperative behavior in a variety of biological, ecological, sociological, physical, and chemical systems. In these models, species of different types, or individuals in different states, reside at the sites of a periodic spatial grid. These sites change or switch state according to specific rules (reflecting birth or death, migration, infection, etc.) In this talk, we consider a spatial epidemic model where a population of sick or healthy individual resides on an infinite square lattice. Sick individuals spontaneously recover at rate *p*, and healthy individual become infected at rate O(1) if they have two or more sick neighbors. As *p* increases, the model exhibits a discontinuous transition from an infected to an all healthy state. Relative stability of the two states is assessed by exploring the propagation of planar interfaces separating them (i.e., planar waves of infection or recovery). We find that the condition for equistability or coexistence of the two states (i.e., stationarity of the interface) depends on orientation of the interface. We also explore the evolution of droplet-like configurations (e.g., an infected region embedded in an all healthy state). We analyze this stochastic model by applying truncation approximations to the exact master equations describing the evolution of spatially non-uniform states. We thereby obtain a set of discrete (or lattice) reaction-diffusion type equations amenable to numerical analysis.
Intra-Host Adaptation and Antigenic Cooperation of RNA Viruses: Modeling and Computational Analysis.Wednesday, January 22, 2014 - 11:05 , Location: Skiles Bld Room 005 , Pavel Skums , CDC , Organizer:
Understanding the mechanisms responsible for the establishment of chronic viral infections is critical to the development of efficient therapeutics and vaccines against highly mutable RNA viruses, such as Hepatitis C (HCV). The mechanism of intra-host viral evolution assumed by most models is based on immune escape via random mutations. However, continuous immune escape does not explain the recent observations of a consistent increase in negative selection during chronic infection and long-term persistence of individual viral variants, which suggests extensive intra-host viral adaptation. This talk explores the role of immune cross-reactivity of viral variants in the establishment of chronic infection and viral intra-host adaptation. Using a computational prediction model for cross-immunoreactivity of viral variants, we show that the level of HCV intra-host adaptation correlates with the rate of cross-immunoreactivity among HCV quasispecies. We analyzed cross-reactivity networks (CRNs) for HCV intra-host variants and found that the structure of CRNs correlates with the type and strength of selection in viral populations. Based on those observations, we developed a mathematical model describing the immunological interaction among RNA viral variants that involves, in addition to neutralization, a non-neutralizing cross-immunoreactivity. The model describes how viral variants escape immune responses and persist, owing to their capability to stimulate non-neutralizing immune responses developed earlier against preceding variants. The model predicts the mechanism of antigenic cooperation among viral variants, which is based on the structure of CRNs. In addition, the model allows to explain previously observed and unexplained phenomenon of reappearance of viral variants: for some chronically infected patients the variants sampled during the acute stage are phylogenetically distant from variants sampled at the earlier years of infection and intermixed with variants sampled 10-20 years later. (Joint work with Y. Khudyakov, Z.Dimitrova, D.Campo and L.Bunimovich)
Monday, January 6, 2014 - 15:00 , Location: Skiles 006 , Katherine St. John , Lehman College, CUNY , Organizer: Christine Heitsch
Evolutionary histories, or phylogenies, form an integral part of much work in biology. In addition to the intrinsic interest in the interrelationships between species, phylogenies are used for drug design, multiple sequence alignment, and even as evidence in a recent criminal trial. A simple representation for a phylogeny is a rooted, binary tree, where the leaves represent the species, and internal nodes represent their hypothetical ancestors. This talk will focus on some of the elegant mathematical and computational questions that arise from assembling, summarizing, visualizing, and searching the space of phylogenetic trees, as well as delve into the computational issues of modeling non-treelike evolution.
Wednesday, November 13, 2013 - 10:30 , Location: Skiles Bld Room 005 , Vladimir E. Bondarenko , GSU , Organizer:
A comprehensive mathematical model of β1-adrenergic signaling system for mouse ventricular myocytes is developed. The model myocyte consists of three major compartments (caveolae, extracaveolae, and cytosol) and includes several modules that describe biochemical reactions and electrical activity upon the activation of β1-adrenergic receptors. In the model, β1-adrenergic receptors are stimulated by an agonist isoproterenol, which leads to activation of Gs-protein signaling pathway to a different degree in different compartments. Gs-protein, in turn, activates adenylyl cyclases to produce cyclic AMP and to activate protein kinase A. Catalytic subunit of protein kinase A phosphorylates cardiac ion channels and intracellular proteins that regulate Ca2+ dynamics. Phosphorylation is removed by the protein phosphatases 1 and 2A. The model is extensively verified by the experimental data on β1-adrenergic regulation of cardiac function. It reproduces time behavior of a number of biochemical reactions and voltage-clamp data on ionic currents in mouse ventricular myocytes; β1-adrenergic regulation of the action potential and intracellular Ca2+ transients; and calcium and sodium fluxes during action potentials. The model also elucidates the mechanism of action potential prolongation and increase in intracellular Ca2+ transients upon stimulation of β1-adrenergic receptors.
Modeling Stochasticity and Variability in Gene Regulatory Networks with Applications to the Development of Optimal Intervention StrategiesWednesday, September 25, 2013 - 11:05 , Location: Skiles Bld Room 005 , D. Murrugarra , SoM, GaTech , Organizer:
Modeling stochasticity in gene regulation is an important and complex problem in molecular systems biology due to probabilistic nature of gene regulation. This talk will introduce a stochastic modeling framework for gene regulatory networks which is an extension of the Boolean modeling approach. This framework incorporates propensity parameters for activation and degradation and is able to capture the cell-to-cell variability. It will be presented in the context of finite dynamical systems, where each gene can take on a finite number of states, and where time is also a discrete variable. Applications using methods from control theory for Markov decision processes will be presented for the purpose of developing optimal intervention strategies. A background to stochastic modeling will be given and the methods will be applied to the p53-mdm2 complex.
Thursday, September 12, 2013 - 16:05 , Location: Skiles 005 , R.Stoop , Inst. of Neuroinformatics, ETH, Zurich , Organizer:
We study to what extent cortical columns with their particular wiring, could boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of the expected enhancement. It is on a mesoscopic?intercolumnar?scale that the wiring among the columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks and we exhibit corroborating evidence for this suggestion. Despite the current emphasis on simpler, e.g., critical, networks, networks with more than one connectivity decay behavior may be the rule rather than the exception. Ref: Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains Ralph Stoop, Victor Saase, Clemens Wagner, Britta Stoop, and Ruedi Stoop, PRL 108105 (2013)
Wednesday, August 28, 2013 - 11:05 , Location: Skiles Bld Room 005 , Mason Porter , Oxford, UK , Organizer:
I discuss "simple" dynamical systems on networks and examine how network structure affects dynamics of processes running on top of networks. I consider results based on "locally tree-like" and/or mean-field and pair approximations and examine when they seem to work well, what can cause them to fail, and when they seem to produce accurate results even though their hypotheses are violated fantastically. I'll also present a new model for multi-stage complex contagions--in which fanatics produce greater influence than mere followers--and examine dynamics on networks with hetergeneous correlations. (This talk discusses joint work with Davide Cellai, James Gleeson, Sergey Melnik, Peter Mucha, J-P Onnela, Felix Reed-Tsochas, and Jonathan Ward.)
Wednesday, April 24, 2013 - 11:05 , Location: Skiles 006 , B.W. Rink , Vrije Univ. Amsterdam , Organizer:
Abstract: Dynamical systems with a coupled cell network structure arise in applications that range from statistical mechanics and electrical circuits to neural networks, systems biology, power grids and the world wide web. A network structure can have a strong impact on the behaviour of a dynamical system. For example, it has been observed that networks can robustly exhibit (partial) synchronisation, multiple eigenvalues and degenerate bifurcations. In this talk I will explain how semigroups and their representations can be used to understand and predict these phenomena. As an application of our theory, I will discuss how a simple feed-forward motif can act as an amplifier. This is joint work with Jan Sanders.
Wednesday, April 17, 2013 - 11:05 , Location: Skiles Bldg, Room 006 , Lev Tsimring , UC San Diego, BIOCircuits Inst. , Organizer:
In this talk, I will describe our recent experimental and theoretical work on small synthetic gene networks exhibiting oscillatory behavior. Most living organisms use internal genetic "clocks" to govern fundamental cellular behavior. While the gene networks that produce oscillatory expression signals are typically quite complicated, certain recurring network motifs are often found at the core of these biological clocks. One common motif which may lead to oscillations is delayed auto-repression. We constructed a synthetic two-gene oscillator based on this design principle, and observed robust and tunable oscillations in bacteria. Computational modeling and theoretical analysis show that the key mechanism of oscillations is a small delay in the negative feedback loop. In a strongly nonlinear regime, this time delay can lead to long-period oscillations that can be characterized by "degrade and fire'' dynamics. We also achieved synchronization of synthetic gene oscillators across cell population as well as multiple populations using variants of the same design in which oscillators are synchronized by chemical signals diffusing through cell membranes and throughout the populations.