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| Friday, January 5 — Session 9: Genes |
| 4:20-5 pm |
Reka Albert
Pennsylvania State University
Dynamics of biological regulatory networks
Interaction between gene products forms the basis of
essential processes like signal transduction, cell metabolism or
embryonic development. Recent experimental advances helped uncover the structure of many cellular networks, creating a surge of interest in the dynamical description of gene regulation. Traditionally genetic and protein interactions are modeled by differential equations based on reaction kinetics, but these studies are greatly hampered by the sparsity of known kinetic detail. As an alternative, qualitative models assuming a small set of discrete states for gene products, or combinations of discrete and continuous dynamics, are gaining acceptance. Many results also suggest that the interaction topology plays a determining role in the dynamics of regulatory networks and there is significant robustness to changes in kinetic parameters. In this presentation I will explore models of the gene regulatory network governing the segmentation of fruit fly embryos, and of the signal transduction network regulating drought response in plants. Each model is able to give predictions and insights into its respective biological process, and illuminates the emergent (network-level) functional robustness of cellular regulatory networks.
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| 5-5:20 pm |
James Lu
Johann Radon Institute for Computational and Applied Mathematics
(RICAM)
Email: james.lu@oeaw.ac.at
authors: Heinz W. Engl, RICAM; Peter Schuster, University of Vienna
Inverse analysis for uncovering roles of network components in gene regulation
Given a large, highly-nonlinear ODE model of a gene regulatory network, relating aspects of its dynamical properties back to the network structure is a highly challenging task. However, questions of this type are prevalent in the study of biological systems: how is the control mechanism of cell cycle encoded in the topology of the regulatory network? what are the possible causes for an observed mutant phenotype that loses a certain dynamical property?
In this talk, we propose a method for carrying out such (nonlinear) inverse dynamical/bifurcation analyses. To study the causes of a certain physiological property, a sequence of bifurcation diagrams is generated, each of which is 'sparsely' mapped to the parameter space via the use of sparsity-promoting regularization functionals. In combination with hierarchical identification strategies, the roles of network components can be elucidated. We demonstrate the methodology in studying cell cycle and circadian rhythm models.
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| 5:20-6 pm |
James Collins
Boston University
Center for BioDynamics and Department of Biomedical Engineering
Boston University
Engineering Gene Networks: Integrating Synthetic Biology &
Systems Biology
Many fundamental cellular processes are governed by genetic
programs which employ protein-DNA interactions in regulating function.
Owing to recent technological advances, it is now possible to design
synthetic gene regulatory networks, and the stage is set for the
notion of engineered cellular control at the DNA level.
Theoretically, the biochemistry of the feedback loops associated with
protein-DNA interactions often leads to nonlinear equations, and the
tools of nonlinear analysis become invaluable. In this talk, we
describe how techniques from nonlinear dynamics and molecular biology can be utilized to model, design and construct synthetic gene regulatory networks. We present examples in which we integrate the development of a theoretical model with the construction of an experimental system. We also discuss the implications of synthetic gene networks for biotechnology, biomedicine and biocomputing. In addition, we present integrated computational-experimental approaches that enable construction of first-order quantitative models of gene-protein regulatory networks using only steady-state expression measurements and no prior information on the network structure or function. We discuss how the reverse-engineered network models, coupled to experiments, can be used: (1) to gain insight into the regulatory role of individual genes and proteins in the network, (2) to identify the pathways and gene products targeted by pharmaceutical compounds, and (3) to identify the genetic mediators of different diseases.
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