Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session V6: Biological Networks |
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Sponsoring Units: DBIO Chair: Edo Kussell, NYU Room: 265 |
Thursday, March 16, 2017 2:30PM - 2:42PM |
V6.00001: Adaptation, Growth, and Resilience in Biological Distribution Networks Henrik Ronellenfitsch, Eleni Katifori Highly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. We show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as plant and animal vasculature. In addition, we show how the incorporation of spatially collective fluctuating sources yields a minimal model of realistic reticulation in distribution networks and thus resilience against damage. [Preview Abstract] |
Thursday, March 16, 2017 2:42PM - 2:54PM |
V6.00002: Dynamic response of gene regulatory networks: A case study of heat shock regulation in Escherichia coli Pradeep Kumar, Venkata Krishnamurthi, Sudip Nepal, Khanh Nguyen Temporal regulation of gene expression plays important role in cellular responses to external perturbations. Gene regulatory pathways ensure that cells are able to respond to temporal environmental changes by regulating the production of various proteins in a time-dependent manner. We have experimentally and computationally studied the dynamic response of gene regulatory network underlying the heat shock regulation in Escherichia coli. Specifically, we measure the dynamics of promoter activities of two key elements in the pathway--(i) heat shock sigma factor rpoH and (ii) dnaJ/K with oscillatory temperature shocks of various frequencies. Our results suggest that the dynamic response of heat shock regulation is optimized for a wide range of temporal variations in temperature. [Preview Abstract] |
Thursday, March 16, 2017 2:54PM - 3:06PM |
V6.00003: Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks Gang Yang, Colin Campbell, Réka Albert Complex diseases can be modeled as damage to intra-cellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage [BMC system Biology 8:53]. We generalize this method to a multi-node damage scenario and to the simultaneous stabilization of multiple steady state attractors. We presents three key results. First, we use analytical and computational methods to study how network structure and regulatory logic affect the resilience of the network's steady states to single node perturbation. Second, we present an algorithm to design compensatory interventions to stabilize a steady state of the network after double node damage and evaluate it on random Boolean networks and two intra-cellular network models relevant to cancer. Third, we apply the algorithm on stabilizing two steady states simultaneously after a single node damage and discuss the emerging situations and their corresponding frequencies. We also apply the algorithm to the biological examples. [Preview Abstract] |
Thursday, March 16, 2017 3:06PM - 3:18PM |
V6.00004: Detecting Functional Structures in E. coli Gene Networks from Expression Data Tianlong Chen, Madeleine Opitz, Kevin E. Bassler The rapidly growing amount of available gene expression data for many organisms makes the development of robust systematic methods for determining the structure and function of regulatory networks from that data an important goal. Recently, methods that use the context likelihood of relatedness to infer a network and then use modularity maximizing community detection algorithms on the inferred network to find the functional structure were shown to be effective [PLoS Comput. Biol. 8, e1002391 (2012)]. Improvements of these methods will be presented and applied to systematically study Escherichia coli expression data. First robust functionally related communities of genes are identified and then the structure of the more closely related genes within those communities are determined. Results will be compared with gene ontology terms and the RegulonDB database. Predictions of a number of significant new regulatory relations are found. [Preview Abstract] |
Thursday, March 16, 2017 3:18PM - 3:30PM |
V6.00005: A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana Pramesh Singh, Tianlong Chen, Zebulun Arendsee, Eve S. Wurtele, Kevin E. Bassler Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective [PLoS Comput. Biol. 8, e1002391 (2012)]. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons [BMC Plant Biology, 8, 99 (2008)], and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. [Preview Abstract] |
Thursday, March 16, 2017 3:30PM - 3:42PM |
V6.00006: Noise-induced relations between network connectivity and dynamics Emily SC Ching Many biological systems of interest can be represented as networks of many nodes that are interacting with one another. Often these systems are subject to external influence or noise. One of the central issues is to understand the relation between dynamics and the interaction pattern of the system or the connectivity structure of the network. In particular, a challenging problem is to infer the network connectivity structure from the dynamics. In this talk, we show that for stochastic dynamical systems subjected to noise, the presence of noise gives rise to mathematical relations between the network connectivity structure and quantities that can be calculated using solely the time-series measurements of the dynamics of the nodes. We present these relations for both undirected networks with bidirectional coupling and directed networks with directional coupling and discuss how such relations can be utilized to infer the network connectivity structure of the systems. [Preview Abstract] |
Thursday, March 16, 2017 3:42PM - 3:54PM |
V6.00007: Universality, criticality and scaling in biochemical networks with feedback Tommy Byrd, Amir Erez, Andrew Mugler Feedback is ubiquitous in biological networks, stretching from gene regulation to cell-to-cell interactions and beyond. In the context of living cells, feedback and feed-forward are important mechanisms for dynamically scaling response, allowing for both sensitivity and specificity. Here we focus on feedback at the single-cell level, and its role in producing protein distributions typically observed experimentally. We introduce a generic model that, depending on a tuning parameter, can yield unimodal or bimodal steady-state protein distributions, and we examine the model's static and dynamic universality classes. We show that statically, the stochastic cell near its bifurcation point is analogous to an Ising model near its critical point, despite the inherent non-equilibrium nature of the system. We demonstrate how this abstract description of a cell as a stochastic birth/death feedback process can be equally applied to several commonly used feedback functions in biophysics. Our approach can therefore be used as a powerful tool to analyze experimental observations without restricting the analysis to a specific (and usually unknown) form of the feedback function. [Preview Abstract] |
Thursday, March 16, 2017 3:54PM - 4:06PM |
V6.00008: Temperature dependence of the multistability of lactose utilization network of Escherichia coli Sudip Nepal, Pradeep Kumar Biological systems are capable of producing multiple states out of a single set of inputs. Multistability acts like a biological switch that allows organisms to respond differently to different environmental conditions and hence plays an important role in adaptation to changing environment. One of the widely studied gene regulatory networks underlying the metabolism of bacteria is the lactose utilization network, which exhibits a multistable behavior as a function of lactose concentration. We have studied the effect of temperature on multistability of the lactose utilization network at various concentrations of thio-methylgalactoside (TMG), a synthetic lactose. We find that while the lactose utilization network exhibits a bistable behavior for temperature $T>20^{\circ}C$, a graded response arises for temperature $T\leq20^{\circ}$C. Furthermore, we construct a phase diagram of the graded and bistable response of lactose utilization network as a function of temperature and TMG concentration. Our results suggest that environmental conditions, in this case temperature, can alter the nature of cellular regulation of metabolism. [Preview Abstract] |
Thursday, March 16, 2017 4:06PM - 4:18PM |
V6.00009: Extinction Dynamics and Control in Adaptive Networks Ira Schwartz, Leah Shaw, Jason Hindes Disease control is of paramount importance in public health. Moreover, models of disease spread are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak has only recently been included in epidemic models on networks. Here we develop the mathematical machinery to describe the dynamics of extinction in finite populations that include human adaptive behavior. The formalism enables us to compute the optimal, fluctuation-induced path to extinction, and predict the average extinction time in adaptive networks as a function of the adaptation rate. We find that both observables have several unique scalings depending on the relative speed of infection and adaptivity. Finally, we discuss how the theory can be used to design optimal control programs in general networks, by coupling the effective force of noise with treatment and human behavior. [Preview Abstract] |
Thursday, March 16, 2017 4:18PM - 4:30PM |
V6.00010: Effect of Temperature on Synthetic Positive and Negative Feedback Gene Networks Daniel A. Charlebois, Sylvia Marshall, Gabor Balazsi Synthetic biological systems are built and tested under well controlled laboratory conditions. How altering the environment, such as the ambient temperature affects their function is not well understood. To address this question for synthetic gene networks with positive and negative feedback, we used mathematical modeling coupled with experiments in the budding yeast \textit{Saccharomyces cerevisiae}. We found that cellular growth rates and gene expression dose responses change significantly at temperatures above and below the physiological optimum for yeast. Gene expression distributions for the negative feedback-based circuit changed from unimodal to bimodal at high temperature, while the bifurcation point of the positive feedback circuit shifted up with temperature. These results demonstrate that synthetic gene network function is context-dependent [1]. Temperature effects should thus be tested and incorporated into their design and validation for real-world applications. [1] S. Cardinale, A.P. Arkin, Contextualizing context for synthetic biology -- identifying causes of failure of synthetic biological systems. Biotechnology Journal, 7:856-866 (2012). [Preview Abstract] |
Thursday, March 16, 2017 4:30PM - 4:42PM |
V6.00011: Stochastic dynamics of genetic broadcasting networks. Davit Potoyan, Peter Wolynes The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" of master genes that broadcast their signals to large number of binding sites. We demonstrate that this "time-scale crisis" can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying the stochastic dynamics of the genetic network of the central eukaryotic master regulator $NF\kappa B$ which broadcasts its signals to many downstream genes that regulate immune response, apoptosis etc. [Preview Abstract] |
Thursday, March 16, 2017 4:42PM - 4:54PM |
V6.00012: Luria-Delbrück Revisited: The Classic Experiment Doesn’t Rule out Lamarckian Evolution Caroline Holmes, Mahan Ghafari, Anzar Abbas, Varun Saravanan, Ilya Nemenman We re-examine data from the classic 1943 Luria-Delbruck fluctuation experiment. This experiment is often credited with establishing that phage resistance in bacteria is acquired through a Darwinian mechanism (natural selection on standing variation) rather than through a Lamarckian mechanism (environmentally induced mutations). We argue that, for the Lamarckian model of evolution to be ruled out by the experiment, the experiment must favor pure Darwinian evolution over both the Lamarckian model and a model that allows both Darwinian and Lamarckian mechanisms. Analysis of the combined model was not performed in the 1943 paper, and nor was analysis of the possibility of neither model fitting the experiment. Using Bayesian model selection, we find that: 1) all datasets from the paper favor Darwinian over purely Lamarckian evolution, 2) some of the datasets are unable to distinguish between the purely Darwinian and the combined models, and 3) the other datasets cannot be explained by any of the models considered. In summary, the classic experiment cannot rule out Lamarckian contributions to the evolutionary dynamics. [Preview Abstract] |
Thursday, March 16, 2017 4:54PM - 5:06PM |
V6.00013: Predicting genetic interactions between beneficial mutations from Fisher's geometric model Sungmin Hwang, Sijmen SCHOUSTRA, Joachim Krug, Arjan de Visser Biological evolution is modeled as a hill-climbing process fueled by genetic mutations in the fitness landscape. The complexity of this process is highly dependent on genetic interactions among different loci by shaping various valleys and peaks in the fitness landscape. The topology of fitness landscapes formed by genetic interaction is generally complicated as there can be a large number of different phenotypic traits of an organism that contribute to its fitness. \textit{Fisher's geometric model} (FGM) is a simple mathematical model that provides a geometric interpretation of the interplay between genotypic and phenotypic layers, on top of which the fitness landscape is constructed. In the framework of FGM, we discuss the statistical properties of the fitness effects when multiple mutations are combined. Experimental data for the filamentous fungus \textit{Aspergillus nidulans} are analyzed with these results to extract the biological information such as the complexities and well-adaptiveness of the organism. Finally, we discuss the geometrical interpretation of the diminishing returns pattern observed in the data in the language of FGM.\\ Reference: Proceedings of the Royal Society B 283:20161376 (2016) [Preview Abstract] |
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