Bulletin of the American Physical Society
Annual Meeting of the Four Corners Section of the APS
Volume 59, Number 11
Friday–Saturday, October 17–18, 2014; Orem, Utah
Session B8: Biophysics I: Mostly Computational |
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Chair: Kathrin Spendier, University of Colorado - Colorado Springs Room: Science Building 60 |
Friday, October 17, 2014 10:15AM - 10:39AM |
B8.00001: Dynamics of RNA dependent RNA polymerases during transcription: The deadly engines within Negative Sense RNA Viruses Invited Speaker: Saveez Saffarian Transcription is the process of polymerase driven synthesis of mRNA from the genome template. In viral infections, transcription is the first step for efficient replication. Many viruses, however, do not rely on cellular polymerases for transcription. Specifically non segmented negative strand (NNS) RNA viruses which include potent human pathogens e.g. Ebola, RSV and VSV, deliver special RNA dependent RNA polymerases to transcribe and replicate their genome template. Transcription initiates only at or near the 3' end of the genome which immediately poses the question of initiation and sustainability of transcription during early stages of infection. How do these polymerases initiate and sustain transcription is completely unknown. I will show the progress we have made in understanding these viruses and specifically show that transcription machinery of NNS RNA viruses is capable of function at almost infinite dilutions. [Preview Abstract] |
Friday, October 17, 2014 10:39AM - 10:51AM |
B8.00002: Fluorescence Fluctuation Analysis for Rapid Dimerization Kinetics: A Model Study James Thomas Fluorescence Correlation Spectroscopy (FCS) is a widely-used biophysical technique for characterizing the density and diffusion of fluorescently-labeled cellular constituents, such as proteins or lipids. Using two detection channels and two fluorophores with distinct emission spectra, dimerization is also readily detected and measured. The kinetics of binding and unbinding, on the other hand, present essentially no detectable signature in auto- and cross-correlation traces. Using numerical simulations of diffusion and dimerization on a lattice, we show that the use of a rotating illumination profile readily allows the separation of diffusion and reaction kinetics in FCS, provided the reaction rates are fast compared with the diffusion time (which can be increased by enlarging the illuminated area.) Dynamic illumination is thus a promising approach to determining rapid dimerization rate constants on biological specimens. [Preview Abstract] |
Friday, October 17, 2014 10:51AM - 11:03AM |
B8.00003: Limitations of Model-Based Experimental Design in Systems Biology Andrew White, Mark Transtrum Mathematical models can help us understand complex biological systems such as gene regulatory networks and signaling pathways. These models can include hundreds of unknown parameters. ~Data fitting typically leads to huge uncertainties in the inferred parameter values, a phenomenon known as sloppiness. ~It has been suggested that model-based experimental design can help overcome this challenge. However, models of complex systems, such as those in biology, never account for all of the system's details. ~Contriving experiments to make previously irrelevant model details become more important may result in the model no longer being able to fit all the data. ~If such is the case, it will require a change in the model itself. ~We test this by considering two models of a cell-signaling process, each of varying complexity. Performing experimental design guided by the simple model but using the complex model as a surrogate for the actual system, we hope to determine the limits of model-based experimental design for accurate parameter inference. [Preview Abstract] |
(Author Not Attending)
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B8.00004: Bistable Dynamics in Chaotic Flow Structures Aditya Dhumuntarao, Wenbo Tang A study of the interactions between turbulent stirring and biological processes is presented. Initial seeding of impurities in varying flow topological structures is shown to dictate the ultimate homogeneous state of the reaction scalar. In particular, hyperbolic and eddy flow structures were coupled with biological bistable dynamics to determine the ultimate state. For bistable equilibrium points, the elliptic flow structures help maintain scalar concentration and converge to one stable state. However, the hyperbolic flow structures contain high stretching regions which dilute the concentration and evolve the system to other stable state. Most importantly, the domain of attraction is critically determined by the underlying Lagrangian Coherent Structure (LCS). The domain convergence bifurcation used an underlying double gyre flow structure with varying Damk\"{o}hler numbers. [Preview Abstract] |
Friday, October 17, 2014 11:15AM - 11:27AM |
B8.00005: Automating Manifold Boundary Model Reduction in Michaelis-Menten Reaction Networks Merrill Asp, Mark Transtrum One of the major issues in understanding complex systems, such as those in systems biology, is the large number of parameters to be fit to data. Methods to approximate and reduce complex models are therefore an important problem. Recent advances in information theory have led to a new method of identifying limiting approximations in complex models known as the Manifold Boundary Approximation Method. I apply this method to systems modeled as coupled differential equations describing networks of Michaelis-Menten reactions. Such models are common in biochemical systems such as developmental biology and cancer. I discuss how this approximation method when applied to such networks can be automated. [Preview Abstract] |
Friday, October 17, 2014 11:27AM - 11:39AM |
B8.00006: Defining all networks that can achieve biological functions Malachi Tolman, Mark Transtrum In systems biology it is common to study networks of biochemical reactions in order to understand the role those reactions play in carrying out a biological function. A central question is then, how is the network topology related to the particular function. We consider the question: given a biological function, how to identify all possible topologies that can accomplish that function. Our approach leverages recent advances in model reduction. We begin with a fully connected network topology and fit it to artificial data corresponding to a particular function. We then perform model reduction to remove irrelevant edges from the network. The result is a minimal network that can carry out that function. [Preview Abstract] |
Friday, October 17, 2014 11:39AM - 11:51AM |
B8.00007: Using information theory to derive an effective model of the Wnt cell-signaling pathway Dane Bjork, Mark Transtrum Microscopically, biological signaling pathways, such as the Wnt pathway, can be very complex, involving a large number of bio-chemical reactions organized to perform specific cellular functions. This complexity is characterized by a large number of unknown parameters that remain unconstrained by experimental data. This complexity is furthermore a bottleneck to understanding the emergent mechanisms that drive the system's functionality. Recent work in information theory has shown that in spite of this complexity, most of the system's behavior is compressed into a small number of important (relevant) parameters. We use information theory to identify these parameters in a model of the Wnt signaling pathway and to derive an effective, simplified model of the system. We compare our results with other attempts to identify effective models from the literature. [Preview Abstract] |
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