Bulletin of the American Physical Society
APS March Meeting 2011
Volume 56, Number 1
Monday–Friday, March 21–25, 2011; Dallas, Texas
Session X40: Biological Networks and Systems Biology |
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Sponsoring Units: DBP Chair: Luis Cruz Cruz, Drexel University Room: A122/123 |
Thursday, March 24, 2011 2:30PM - 2:42PM |
X40.00001: Computer Simulations of Loss of Organization of Neurons as a Model for Age-related Cognitive Decline Luis Cruz, Elene Fengometidis, Frank Jones, Srinivas Jampani In normal aging, brains suffer from progressive cognitive decline not linked with loss of neurons common in neurodegenerative disorders such as Alzheimer's disease. However, in some brain areas neurons have lost positional organization specifically within microcolumns: arrays of interconnected neurons which may constitute fundamental computational units in the brain. This age-related loss of organization, likely a result of micron-sized random displacements in neuronal positions, is hypothesized to be a by-product of the loss of support from the surrounding medium, including dendrites. Using a dynamical model applied to virtual 3D representation of neuronal arrangements, that previously showed loss of organization in brains of cognitively tested rhesus monkeys, the relationship between these displacements and changes to the surrounding dendrite network are presented. The consequences of these displacements on the structure of the dendritic network, with possible disruptions in signal synchrony important to cognitive function, are discussed. [Preview Abstract] |
Thursday, March 24, 2011 2:42PM - 2:54PM |
X40.00002: Coupled feedback loops govern bistability properties in gene networks Abhinav Tiwari, Oleg Igoshin Positive feedback is a necessary component for network bistability - the simplest design being a positive autoregulatory circuit. Then why some biological systems have multiple feedback loops? We hypothesize that the presence of multiple additively or multiplicatively coupled feedback loops affects the net cooperativity of the system, thereby influencing the possibility of bistability. We find that additively coupled feedback loops in the MprAB-SigE-RseA network in mycobacteria do not lead to bistability. Only the inclusion of post-translational regulation of SigE by RseA makes the system robustly bistable. In general we find that if two one-feedback networks are individually monostable, then only multiplicative coupling can generate bistability in the combined circuit. We analytically perform pair-wise controlled comparisons between the autoregulation circuit, additively and multiplicatively coupled two-gene circuits that reveal neither of the circuits has an advantage with regards to bistability range. We numerically validate our results by employing Monte Carlo parameter sampling for the comparisons. [Preview Abstract] |
Thursday, March 24, 2011 2:54PM - 3:06PM |
X40.00003: Better Bet-Hedging with coupled positive and negative feedback loops Jatin Narula, Oleg Igoshin Bacteria use the phenotypic heterogeneity associated with bistable switches to distribute the risk of activating stress response strategies like sporulation and persistence. However bistable switches offer little control over the timing of phenotype switching and first passage times (FPT) for individual cells are found to be exponentially distributed. We show that a genetic circuit consisting of interlinked positive and negative feedback loops allows cells to control the timing of phenotypic switching. Using a mathematical model we find that in this system a stable high expression state and stable low expression limit cycle coexist and the FPT distribution for stochastic transitions between them shows multiple peaks at regular intervals. A multimodal FPT distribution allows cells to detect the persistence of stress and control the rate of phenotype transition of the population. We further show that extracellular signals from cell-cell communication that change the strength of the feedback loops can modulate the FPT distribution and allow cells even greater control in a bet-hedging strategy. [Preview Abstract] |
Thursday, March 24, 2011 3:06PM - 3:18PM |
X40.00004: Attractor Distribution in Random Biological Networks Described by ODEs and Diminished Order-Chaos Transition Zhiyuan Li, Chao Tang Ordinary Differential Equations (ODEs) are widely used to model biological network in a continuous manner. The state of an ODE system after infinitely long time is called attractor, which indicates the ultimate fate of the corresponding biological system. Even though the attractor behaviors of many biological systems have been understood, yet the distribution of attractors for networks followings biological reaction rules is in general unknown. In our work, we study the final state for all 3 nodes networks that follow transcriptional regulation or enzymatic reaction rules, under random parameter sets. Surprisingly, mono-stable behavior appears most frequently, while bi-stable and tri-stable behavior is less frequently observed. Oscillations are rarely seen, and chaos is almost never observed. We extend the study to random networks with a large number of nodes, and the outcome does not change qualitatively. Furthermore, with increased connectivity, the transition from order to chaos predicted by discrete models is not observed. Our results provide a null-distribution for attractors in bio-networks, and have important implication for cell fate decision. [Preview Abstract] |
Thursday, March 24, 2011 3:18PM - 3:30PM |
X40.00005: Beyond Critical Exponents in Neuronal Avalanches Nir Friedman, Tom Butler, Robert DeVille, John Beggs, Karin Dahmen Neurons form a complex network in the brain, where they interact with one another by firing electrical signals. Neurons firing can trigger other neurons to fire, potentially causing avalanches of activity in the network. In many cases these avalanches have been found to be scale independent, similar to critical phenomena in diverse systems such as magnets and earthquakes. We discuss models for neuronal activity that allow for the extraction of testable, statistical predictions. We compare these models to experimental results, and go beyond critical exponents. [Preview Abstract] |
Thursday, March 24, 2011 3:30PM - 3:42PM |
X40.00006: Stochastic Modeling of Regulation of Gene Expression by Multiple Competing Small RNAs Charles Baker, Tao Jia, Rahul Kulkarni A wealth of new research has highlighted the critical roles of small RNAs (sRNAs) in diverse processes such as quorum sensing and cellular responses to stress. The pathways controlling these processes often have a central motif comprised of a key protein regulated by multiple sRNAs. However, the regulation of stochastic gene expression of a single target gene by multiple sRNAs is currently not well understood. To address this issue, we analyze a stochastic model of regulation of gene expression by multiple sRNAs. For this model, we derive exact analytic results for the regulated protein distribution including compact expressions for its mean and variance. The derived results provide novel insights into the roles of multiple sRNAs in fine-tuning the noise in gene expression. In particular, we show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a mechanism for independently controlling the mean and variance of the regulated protein distribution. [Preview Abstract] |
Thursday, March 24, 2011 3:42PM - 3:54PM |
X40.00007: The up and down states of cortical networks Maryam Ghorbani, Alex J. Levine, Mayank Mehta, Robijn Bruinsma The cortical networks show a collective activity of alternating active and silent states known as up and down states during slow wave sleep or anesthesia. The mechanism of this spontaneous activity as well as the anesthesia or sleep are still not clear. Here, using a mean field approach, we present a simple model to study the spontaneous activity of a homogenous cortical network of excitatory and inhibitory neurons that are recurrently connected. A key new ingredient in this model is that the activity-dependant synaptic depression is considered only for the excitatory neurons. We find depending on the strength of the synaptic depression and synaptic efficacies, the phase space contains strange attractors or stable fixed points at active or quiescent regimes. At the strange attractor phase, we can have oscillations similar to up and down states with flat and noisy up states. Moreover, we show that by increasing the synaptic efficacy corresponding to the connections between the excitatory neurons, the characteristics of the up and down states change in agreement with the changes that we observe in the intracellular recordings of the membrane potential from the entorhinal cortex by varying the depth of anesthesia. Thus, we propose that by measuring the value of this synaptic efficacy, one can quantify the depth of anesthesia which is clinically very important. These findings provide a simple, analytical understanding of the spontaneous cortical dynamics. [Preview Abstract] |
Thursday, March 24, 2011 3:54PM - 4:06PM |
X40.00008: Adiabatic and Non-Adiabatic Non-Equilibrium Stochastic Dynamics of Single Regulating Genes Haidong Feng, Bo Han, Jin Wang We explore the stochastic dynamics of self regulative genes from fluctuations of molecular numbers and of on and off switching of gene states due to regulatory protein binding/unbinding to the genes. We found when the binding/unbinding is relatively fast (slow) compared with the synthesis/degradation of proteins in adiabatic (non-adiabatic) case, the self regulators can exhibit one or two peak (two peak) distributions in protein concentrations. This shows even with the same architecture (topology of wiring), networks can have quite different functions (phenotypes), consistent with recent single molecule single gene experiments. We derive the non-equilibrium phase diagrams of mono-stability and bi-stability in adiabatic and non-adiabatic regimes. We study the stability and robustness of the systems through mean first passage time (MFPT) from one peak (basin of attraction) to another. In addition, using the new method for quantifying the paths and the associated weights for complex systems in discrete state space (Markov chains), we identified the dominant paths among all possible paths from the ``off'' basin to the ``on'' basin for self-activators, and observe turnover kinetic behavior of transitions and MFPT from non-adiabatic to adiabatic regimes. [Preview Abstract] |
Thursday, March 24, 2011 4:06PM - 4:18PM |
X40.00009: Steady state growth of \textit{E. Coli} in low ammonium environment Minsu Kim, Barret Deris, Zhongge Zhang, Terry Hwa Ammonium is the preferred nitrogen source for many microorganisms. In medium with low ammonium concentrations, enteric bacteria turn on the nitrogen responsive (ntr) genes to assimilate ammonium. Two proteins in \textit{E. coli}, Glutamine synthetase (GS) and the Ammonium/methylammonium transporter AmtB play crucial roles in this regard. GS is the major ammonium assimilation enzyme below 1mM of NH$_{4}^{+}$. AmtB is an inner membrane protein that transports NH$_{4}^{+}$ across the cell membrane against a concentration gradient. In order to study ammonium uptake at low NH$_{4}^{+}$ concentration at neutral pH, we developed a microfluidic flow chamber that maintains a homogenous nutrient environment during the course of exponential cell growth, even at very low concentration of nutrients. Cell growth can be accurately monitored using time-lapse microscopy. We followed steady state growth down to micro-molar range of NH$_{4}^{+}$ for the wild type and $\Delta $amtB strains. The wild type strain is able to maintain the growth rate from 10mM down to a few uM of NH$_{4}^{+}$, while the mutant exhibited reduced growth below $\sim $20~uM of NH$_{4}^{+}$. Simultaneous characterization of the expression levels of GS and AmtB using fluorescence reporters reveals that AmtB is turned on already at 1mM, but contributes to function only below $\sim $30~uM in the wild-type. Down to $\sim $20~uM of NH$_{4}^{+}$, \textit{E.~coli} can compensate the loss of AmtB by GS alone. [Preview Abstract] |
Thursday, March 24, 2011 4:18PM - 4:30PM |
X40.00010: Threshold response and bimodality in non-cooperative auto-activation circuits Rutger Hermsen, David Erickson, Terence Hwa In prokaryotes as well as in eukaryotes, many transcription factors (TFs) activate their own gene. For that reason the benefits of auto-activation have been studied extensively. However, little attention is paid to the fact that many TFs are modified by a signal, usually through phosphorylation or binding of a ligand. Typically only one version of the TF---the modified or the unmodified one---can activate transcription. Consequently the TF's expression level responds to changes in the signal. Here, we use stochastic models to study the response properties of such circuits. In real examples the auto-activation is often mediated by a single binding site. Surprisingly, in that case we find that an arbitrarily sensitive threshold response can be obtained, while the bistability and hysteresis associated with multiple cooperative binding sites are avoided. Also, we find that the steady-state probability distributions of the TF expression level can be bimodal even though the system is not bistable. This is not caused by slow TF--DNA binding kinetics or bursty protein production, as in earlier studies, but by strongly reduced production and degradation rates at low expression levels. [Preview Abstract] |
Thursday, March 24, 2011 4:30PM - 4:42PM |
X40.00011: Inferring Complex Network Topology from Spatio-Temporal Spike Patterns Frank Van Bussel, Birgit Kriener, Marc Timme The problem of reconstructing or reverse-engineering the connectivity of networks consisting of dynamically interacting units has become an active area of study in fields such as genetics, ecology, and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence) of individual interactions, but there is commonly no direct way to probe for their existence. We present an explicit method for reconstructing neuronal networks from their spiking activity. The approach works well for networks in simple collective states, but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required. [Preview Abstract] |
Thursday, March 24, 2011 4:42PM - 4:54PM |
X40.00012: ABSTRACT WITHDRAWN |
Thursday, March 24, 2011 4:54PM - 5:06PM |
X40.00013: Temporal competition between differentiation programs determines cell fate choice Anna Kuchina, Lorena Espinar, Tolga Cagatay, Alejandro Balbin, Alma Alvarado, Jordi Garcia-Ojalvo, Gurol Suel During pluripotent differentiation, cells adopt one of several distinct fates. The dynamics of this decision-making process are poorly understood, since cell fate choice may be governed by interactions between differentiation programs that are active at the same time. We studied the dynamics of decision-making in the model organism \textit{Bacillus subtilis} by simultaneously measuring the activities of competing differentiation programs (sporulation and competence) in single cells. We discovered a precise switch-like point of cell fate choice previously hidden by cell-cell variability. Engineered artificial crosslinks between competence and sporulation circuits revealed that the precision of this choice is generated by temporal competition between the key players of two differentiation programs. Modeling suggests that variable progression towards a switch-like decision might represent a general strategy to maximize adaptability and robustness of cellular decision-making. [Preview Abstract] |
Thursday, March 24, 2011 5:06PM - 5:18PM |
X40.00014: Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes Mehmet Umut Caglar, Ranadip Pal Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared. [Preview Abstract] |
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