Bulletin of the American Physical Society
2008 APS March Meeting
Volume 53, Number 2
Monday–Friday, March 10–14, 2008; New Orleans, Louisiana
Session W40: Focus Session: Networks, Regulation, and Pathways in Cell Biology |
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Sponsoring Units: DBP GSNP Chair: Rahul Kulkarni, Virginia Polytechnic Institute and State University Room: Morial Convention Center 232 |
Thursday, March 13, 2008 2:30PM - 3:06PM |
W40.00001: Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks Invited Speaker: The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of \textit{Yersinia pseudotuberculosis}, as well as the three \textit{Yersinia pestis} biovars \textit{Antiqua}, \textit{Mediaevalis}, and \textit{Orientalis}. While \textit{Y. pseudotuberculosis }typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related \textit{Y. pestis} strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four \textit{Yersiniae} networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility. [Preview Abstract] |
Thursday, March 13, 2008 3:06PM - 3:18PM |
W40.00002: Level architecture in genetic regulatory networks and the role of microRNAs J. M. Schwarz It is well known that genes that code for proteins regulate the expression of each other through protein-mediated interactions. With the discovery of microRNAs$^1$ (miRNAs), it has been conjectured that there are many such regulatory miRNAs in the cell that are never transcribed into proteins but are important for regulation and, hence, could explain the nature of the non-coding (or junk) DNA.$^2$ Furthermore, miRNAs are highly conserved molecules. So, just as genes that code for proteins form regulatory networks, we conjecture that miRNAs form a higher-level regulatory network amongst themselves as mediated by the genes-coding-for-proteins regulatory network to form a complex organism. We investigate this conjecture within the framework of random Boolean networks where the two-level architecture is modelled via two coupled random Boolean networks with one network taking precedence over the other for various input/output values. Aspects of the evolution of the lower-level network will also be addressed. $^1$ D. P. Bartel, Cell {\bf 116}, 281 (2004). $^2$ J. S. Mattick, Sci. Amer. {\bf 291}, 60 (2004). [Preview Abstract] |
Thursday, March 13, 2008 3:18PM - 3:30PM |
W40.00003: Tailoring the metabolism against mutations Natali Gulbahce, Adilson E. Motter, Eivind Almaas, Albert Laszlo Barabasi In the post-genomic era, organisms can be modelled at the whole-cell level in silico via steady state methods to describe their metabolic capabilities. We use two such methods, Flux Balance Analysis and Minimization of Metabolic Adjustment to explore the behavior of cells (of E. coli and S. cerevisiae) after severe mutations. We propose experimentally feasible ways of modifying the underlying biochemical reaction network of a mutant cell such that cell functionality, in particular growth rate, is significantly improved. [Preview Abstract] |
Thursday, March 13, 2008 3:30PM - 3:42PM |
W40.00004: Form, Function, and Evolvability in Biological Networks Andrew Mugler, Etay Ziv, Ilya Nemenman, Chris H. Wiggins A driving problem in systems biology for several years has been exploring the extent to which the topology of a small biological network constrains or guides its function. The absence of such constraint would allow a given network to evolve without rewiring its underlying form. We introduce a quantitative measure of this evolvability that does not rely on pre-defining the preferred function of a given topology. We then study the stochastic description of the experimental setup of Guet [1], treating chemical inducers as functional inputs and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating the highest-fidelity functions. We find that, while all networks studied are highly evolvable by our measure--meaning that the function has little dependence on location in parameter space--the evolvability is correlated with individual topological features. Certain topological attributes, then, are shown (with statistical significance) to convey evolvability to biological networks. [1] C. C. Guet et al., Science \textbf{296}, 1466 (2002). [Preview Abstract] |
Thursday, March 13, 2008 3:42PM - 4:18PM |
W40.00005: Noisy out of necessity: Probabilistic behavior during cellular differentiation Invited Speaker: Diverse organisms ranging from bacteria to mammalian stem cells undergo pluripotent differentiation where a single cell can commit to one out of several cell fates. How do underlying genetic circuits comprised of interactions between genes and proteins allow cells to ``choose'' a specific cell fate and execute the appropriate differentiation program? To address this question we investigate a simple bacterial differentiation system utilizing mathematical modeling and quantitative single cell measurements. In particular we are interested in elucidating the role of circuit dynamics and stochastic behavior in cellular differentiation. [Preview Abstract] |
Thursday, March 13, 2008 4:18PM - 4:30PM |
W40.00006: Analysis of temperature-dependent changes in the metabolism of \textit{Yersinia pestis}. Ali Navid, Eivind Almaas The gram-negative bacterium \textit{Yersinia pestis} is the aetiological agent of bubonic plague, a zoonotic infection that occurs through the bite of a flea. It has long been known that \textit{Y. pestis} has different metabolic needs upon transition from the flea gut environment (26 \r{ }C) to that of a mammalian host (37 \r{ }C). To study this and other outstanding questions about metabolic function of \textit{Y. pestis}, we used the available genomic, biochemical and physiological data to develop a constraint-based flux balance model of metabolism in the avirulent 91001 strain (biovar Mediaevalis) of this organism. Utilizing two sets of whole-genome DNA microarray expression data, we examined the system level changes that occur when \textit{Y. pestis} acclimatizes to temperature shifts. Our results point to fundamental changes in its oxidative metabolism of sugars and use of amino acids, in particular that of arginine. This behavior is indicative of an inefficient metabolism that could be caused by adaptation to life in a nutrient rich environment. [Preview Abstract] |
Thursday, March 13, 2008 4:30PM - 4:42PM |
W40.00007: Information processing in the {\it E. coli} Chemotaxis Network Lin Wang, Sima Setayeshgar Biochemical signal transduction, broadly defined as the conversion of the concentration of an input signal to an output response, is the most basic level of biological information processing. The chemosensory pathway in bacterial chemotaxis is the best-characterized signal transduction network, and as such it provides an ideal system for probing the physical principles governing complex cellular signaling and response. Using an experimentally realistic stochastic implementation of the {\it E. coli} chemotaxis network and motor response, we investigate optimality of the chemotactic response in terms of input/output information transmission. [Preview Abstract] |
Thursday, March 13, 2008 4:42PM - 4:54PM |
W40.00008: Correlated Phenotypic Transitions to Competence in Bacterial Colonies Inbal Hecht, Eshel Ben-Jacob, Herbert Levine Genetic competence is a phenotypic state of a bacterial cell in which it is capable of importing DNA, presumably to hasten its exploration of alternate genes in its quest for survival under stress. Recently, it was proposed that this transition is uncorrelated among different cells in the colony. Motivated by several discovered signaling mechanisms which create colony- level responses, we present a model for the influence of quorum- sensing signals on a colony of \emph{B. Subtilis} cells during the transition to genetic competence. Coupling to the external signal creates an effective inhibitory mechanism, which results in anti-correlation between the cycles of adjacent cells. We show that this scenario is consistent with the specific experimental measurement, which fails to detect some underlying collective signaling mechanisms. Rather, we suggest other parameters that should be used to verify the role of a quorum-sensing signal. We also study the conditions under which phenotypic spatial patterns may emerge. [Preview Abstract] |
Thursday, March 13, 2008 4:54PM - 5:06PM |
W40.00009: On the Selection of Bistability in Genetic Regulatory Circuits Cheol-Min Ghim, Eivind Almaas Bistability is a defining character of switching and memory devices. Many regulatory circuits observed in cellular reaction networks contain ``bistability motifs'' that endow a cell with efficient and reliable switching between different physiological modes of operation. One of the best characterized system, the \textit{lac} operon in \textit{E. coli}, has been shown to display a saddle-node bifurcation when induced by nonmetabolizable lactose analogue inducers, such as isopropylthio-$\beta$-D-galactoside (IPTG) and thio-methyl-galactoside (TMG). Motivated by the absence of bifurcation in the same system with its natural inducer, lactose, we studied the conditions for bistability and rationalized its fitness effects in the light of evolution. Stochastic simulations as well as mean-field approach confirm that history-dependent behavior as well as nongenetic inheritance, being realized by bistability motifs, may be beneficial in fluctuating environments. [Preview Abstract] |
Thursday, March 13, 2008 5:06PM - 5:18PM |
W40.00010: Mutual information in random Boolean models of regulatory networks Joshua Socolar, Andre Ribeiro, Bj\"orn Samuelsson, Jason Lloyd-Price, Stuart Kauffman In a large, complex network of interacting elements, such as a genetic regulatory network within a cell, the average of the mutual information over all pairs of elements is a global measure of how well the system can coordinate its internal dynamics. We study the average pairwise mutual information $\cal{I}$ in random Boolean networks (RBNs) as a function of the distribution of Boolean rules implemented at each element, assuming that the links in the network are randomly placed. As the number $N$ of network nodes approaches infinity, $N\cal{I}$ exhibits a discontinuity at parameter values corresponding to critical RBNs. For finite systems, $N\cal{I}$ peaks near the critical value, but slightly in the disordered regime for typical parameter variations. The source of high values of $N\cal{I}$ is the indirect correlations between pairs of elements from different long chains with a common starting point. The contribution from pairs that are directly linked approaches zero for critical networks and peaks deep in the disordered regime. [Preview Abstract] |
Thursday, March 13, 2008 5:18PM - 5:30PM |
W40.00011: Dynamical properties of structured Boolean networks Andrew Pomerance, Wolfgang Losert, Michelle Girvan, Edward Ott Boolean networks have been used since the 60s as a model for genetic control networks. In this model, each node takes on the value 0 or 1, modeling whether a gene is expressed or not, and updates at each time step according to a function of the value of its inputs. Random boolean networks (RBNs), where each node is randomly connected to other nodes and the function governing the dynamics is initially randomly generated, have been particularly well-studied. In particular, since these are deterministic, finite systems, the system must eventually settle into a periodic or fixed point attractor. A key question has been the scaling of the number of attractors with system size. In this talk we present results on how network structure effects the behavior of Boolean networks with randomly assigned dynamical rules. For example, we show that the number of attractors is dramatically increased by the addition of community structure to the network from the baseline RBN count with the same number of nodes. Furthermore, imposing bipartite structure on the network has little effect on the number of attractors. [Preview Abstract] |
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