Georgia Institute of Technology College of Sciences

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)

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.

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)

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.