Bulletin of the American Physical Society
2009 APS March Meeting
Volume 54, Number 1
Monday–Friday, March 16–20, 2009; Pittsburgh, Pennsylvania
Session J7: Complex Cellular Biological Networks |
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Sponsoring Units: GSNP DBP Chair: Takashi Nishikawa, Clarkson University Room: 407 |
Tuesday, March 17, 2009 11:15AM - 11:51AM |
J7.00001: Mass-action equilibrium and non-specific interactions in protein binding networks Invited Speaker: Large-scale protein binding networks serve as a paradigm of complex properties of living cells. These networks are naturally weighted with edges characterized by binding strength and protein-nodes -- by their concentrations. However, the state-of-the-art high-throughput experimental techniques generate just a binary (yes or no) information about individual interactions. As a result, most of the previous research concentrated just on topology of these networks. In a series of recent publications [1-4] my collaborators and I went beyond purely topological studies and calculated the mass-action equilibrium of a genome-wide binding network using experimentally determined protein concentrations, localizations, and reliable binding interactions in baker's yeast. We then studied how this equilibrium responds to large perturbations [1-2] and noise [3] in concentrations of proteins. We demonstrated that the change in the equilibrium concentration of a protein exponentially decays (and sign-alternates) with its network distance away from the perturbed node. This explains why, despite a globally connected topology, individual functional modules in such networks are able to operate fairly independently. In a separate study [4] we quantified the interplay between specific and non-specific binding interactions under crowded conditions inside living cells. We show how the need to limit the waste of resources constrains the number of types and concentrations of proteins that are present at the same time and at the same place in yeast cells. \newline [1] S Maslov, I. Ispolatov, PNAS 104:13655 (2007). \newline [2] S. Maslov, K. Sneppen, I. Ispolatov, New J. of Phys. 9: 273 (2007). \newline [3] K-K. Yan, D. Walker, S. Maslov, PRL accepted (2008). \newline [4] J. Zhang, S. Maslov, and E. I. Shakhnovich, Mol Syst Biol 4, 210 (2008). [Preview Abstract] |
Tuesday, March 17, 2009 11:51AM - 12:27PM |
J7.00002: Getting from Genotypes to Phenotypes through Network Reconstruction and Modeling Invited Speaker: sGenome annotations provide a detailed description of the metabolic activities an organism can carry out. A metabolic network can be reconstructed from genomic data and serves as a framework to build computational metabolic models. These models can make phenotypic predictions about the behavior of an organism given different genetic or environmental perturbations. Comparisons between model predictions and experimental data can then be used to identify missing components and interactions in biochemical networks. These comparisons provide a mechanism to improve our understanding of biological networks and genomes and in turn lead to improved models. [Preview Abstract] |
Tuesday, March 17, 2009 12:27PM - 1:03PM |
J7.00003: Synthetic rescues and spontaneous cascades in metabolic networks Invited Speaker: Using {\it in silico} experiments, I will show that organisms evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tend to significantly reduce the number of active metabolic reactions compared to typical non-optimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organisms to grow at all. I will show that this massive reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Following these observations, I will introduce a network method to recover the loss of metabolic function due to mutations and other defects, which is based on bypassing rather than correcting the defective pathways. In particular, I will present predictions of {\it synthetic recovery}, in which the knockout of one enzyme-coding gene results in a non-viable phenotype while the concurrent knockout of a second enzyme-coding gene restores viability. In addition to their potential role in metabolic engineering and medical research, these results have puzzling implications for the recently observed temporary activation of latent pathways. [Preview Abstract] |
Tuesday, March 17, 2009 1:03PM - 1:39PM |
J7.00004: Robustness of metabolic networks Invited Speaker: We investigated the robustness of cellular metabolism by simulating the system-level computational models, and also performed the corresponding experiments to validate our predictions. We address the cellular robustness from the ``metabolite''-framework by using the novel concept of ``flux-sum,'' which is the sum of all incoming or outgoing fluxes (they are the same under the pseudo-steady state assumption). By estimating the changes of the flux-sum under various genetic and environmental perturbations, we were able to clearly decipher the metabolic robustness; the flux-sum around an essential metabolite does not change much under various perturbations. We also identified the list of the metabolites essential to cell survival, and then ``acclimator'' metabolites that can control the cell growth were discovered. Furthermore, this concept of ``metabolite essentiality'' should be useful in developing new metabolic engineering strategies for improved production of various bioproducts and designing new drugs that can fight against multi-antibiotic resistant superbacteria by knocking-down the enzyme activities around an essential metabolite. Finally, we combined a regulatory network with the metabolic network to investigate its effect on dynamic properties of cellular metabolism. [Preview Abstract] |
Tuesday, March 17, 2009 1:39PM - 2:15PM |
J7.00005: Discrete dynamic modeling of T cell survival signaling networks Invited Speaker: Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: ($i)$ Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (\textit{ii}) Sphingosine kinase 1 and NF$\kappa $B are essential for the long-term survival of T cells in T-LGL leukemia. (\textit{iii}) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008). [Preview Abstract] |
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