Bulletin of the American Physical Society
APS March Meeting 2014
Volume 59, Number 1
Monday–Friday, March 3–7, 2014; Denver, Colorado
Session J11: Focus Session: Gene Regulatory Networks in Medicine and Biology |
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Sponsoring Units: DBIO Chair: Yang Liu, Northeastern University Room: 203 |
Tuesday, March 4, 2014 2:30PM - 2:42PM |
J11.00001: Network Approach to Disease Diagnosis Amitabh Sharma, Amir Bashan, Albert-Laszlo Barabasi Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network ("interactome"). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. [Preview Abstract] |
Tuesday, March 4, 2014 2:42PM - 2:54PM |
J11.00002: Autonomous Boolean modeling of gene regulatory networks Joshua Socolar, Mengyang Sun, Xianrui Cheng In cases where the dynamical properties of gene regulatory networks are important, a faithful model must include three key features: a network topology; a functional response of each element to its inputs; and timing information about the transmission of signals across network links. Autonomous Boolean network (ABN) models are efficient representations of these elements and are amenable to analysis. We present an ABN model of the gene regulatory network governing cell fate specification in the early sea urchin embryo, which must generate three bands of distinct tissue types after several cell divisions, beginning from an initial condition with only two distinct cell types. Analysis of the spatial patterning problem and the dynamics of a network constructed from available experimental results reveals that a simple mechanism is at work in this case. [Preview Abstract] |
Tuesday, March 4, 2014 2:54PM - 3:06PM |
J11.00003: Construction of A Self-consistent Landscape for Multistable Gene Regulatory Circuits Mingyang Lu, Jose Onuchic, Eshel Ben-Jacob Cell fate decisions during embryonic development and tumorigenesis pose a major research challenge in modern developmental and cancer biology. Cell fate decisions between different phenotypes are regulated by multistable gene circuits that give rise to the coexistence of several stable states. Internal and external noise play crucial role in determining the transitions between and the relative stability of the coexisting phenotypes. The deterministic dynamics of these circuits is not derivable from a potential. Yet, motivated by Waddington Epigenetic Landscape, many rely on the notion of effective potential to describe cell fate determination in the presence of noise. Here, we present a construction of a self-consistent landscape (effective potential, W $\equiv $ -ln(probability)), utilizing the Eikonal equation approach (WKB approximation of the corresponding Fokker Planck equation) for the cases of white noise and shot noise. The approach is based on utilizing the method of characteristics in a special way. We also devised a numerical method to efficiently calculate the contour of the potential and the optimal path for the transitions from one stable state to another. We tested the method on the bistable and tristable double inhibition circuits, and we showed that the constructed landscape agrees very well with the numerical simulation of the stochastic equations. We expect this method to be valuable to a wide range of multistable gene circuits. [Preview Abstract] |
Tuesday, March 4, 2014 3:06PM - 3:18PM |
J11.00004: On the dephasing of genetic oscillators Davit Potoyan, Peter Wolynes The digital nature of genes combined with the associated low copy numbers of proteins regulating them is a significant source of stochasticity, which affects the phase of biochemical oscillations. We show that unlike ordinary chemical oscillators the dichotmoic molecular noise of gene state switching in gene oscillators affects the stochastic dephasing in a way that may not always be captured by phenomenological limit cycle based models.Through simulations of a realistic model of the $NFB\kappa B$/$I\kappa B$ network we also illustrate the dephasing phenomena which are important for reconciling single cell and population based experiments on gene oscillators. [Preview Abstract] |
Tuesday, March 4, 2014 3:18PM - 3:30PM |
J11.00005: A dynamical network model for frailty-induced mortality Swadhin Taneja, Andrew Rutenberg, Arnold Mitnitski, Kenneth Rockwood Age-related clinical and biological deficits can be used to build a frailty index that is a simple fraction of observed to possible deficits. As a proxy measure of aging, such a frailty index is empirically a better predictor of human mortality than chronological age. We present a network dynamical model of deficits that allows us to naturally consider causal interactions between deficits, deficit formation and repair, and mortality. We investigate the information provided by various model frailty indices, how they reflect the underlying dynamics of the network, and how well they predict mortality. [Preview Abstract] |
Tuesday, March 4, 2014 3:30PM - 4:06PM |
J11.00006: Network Medicine: From Cellular Networks to the Human Diseasome Invited Speaker: Albert-Laszlo Barabasi Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The tools of network science offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction not only enrich our understanding of complex systems, but are also essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases. [Preview Abstract] |
Tuesday, March 4, 2014 4:06PM - 4:18PM |
J11.00007: Epigenetic landscape of master regulators with cooperative feedback Andreas Kraemer A common view is that cell phenotypes can be understood as attracting valleys in a complex epigenetic landscape. On the other hand, cell fates have also been associated with master regulatory genes controlling development, which is particularly important in the context of cellular reprogramming. In this work I describe a simple noisy model of gene regulation in which master transcription regulators are involved in cooperative positive feedback loops with a large number of downstream regulated genes. It is shown that this model can be mapped onto a finite-temperature Hopfield associative memory spin model with effective pairwise Hebbian interactions, thus providing a mechanism for concurrent storage of gene expression patterns representing different cell states, where each cell state is associated with a particular master regulator. The inclusion of simple dynamics then leads to a description in terms of an N-dimensional potential landscape, N being the number of regulators. Within this model I discuss the stability of cell states as well as different mechanisms of switching between states when triggered by an external signal, suggesting possible scenarios for cell differentiation events. [Preview Abstract] |
Tuesday, March 4, 2014 4:18PM - 4:30PM |
J11.00008: Discrete-state stochastic simulation of mutant cell dynamics in different environments Merzu Belete, Gabor Balazsi Phenotypic heterogeneity among genetically identical cells was shown in a number of experiments.\footnote{E.M. Ozbudak, NATURE 427, (2004)} This non-genetic variability can arise from low copies of molecular components like DNA and protein within the cell. These low copy of molecules then cause cell-to-cell variation in their gene expression.\footnote{M.B. ELOWITZ, SCIENCE 297, (2002)} Gene expression interacts with the environment to give rise to different phenotypes in the population. Thus, the population has sub-populations with different growth rates and different cellular switching rates from one sub-population to others.\footnote{Nevozhay, PLoS 8,(2012)} The dynamics of mutation in such populations is not well understood. So, what is the fate of mutants in such populations? To address this problem, we developed a stochastic discrete-state model which incorporates a fixed number population, different cellular switching rates, and a different growth rate for each sub-populations. We randomly induced a single mutation in the population in various environments and measured the population fitness change and fraction of mutant cells in the population. The model predicts that the induced mutation follows the dynamics consistent with those experiments observed in our lab. [Preview Abstract] |
Tuesday, March 4, 2014 4:30PM - 4:42PM |
J11.00009: ABSTRACT WITHDRAWN |
Tuesday, March 4, 2014 4:42PM - 4:54PM |
J11.00010: A variational model for propagation time, volumetric and synchronicity optimization in the spinal cord axon network, and a method for testing it Bruno Mota Most information in the central nervous system in general and the (simpler) spinal cord in particular, is transmitted along bundles of parallel axons. Each axon's transmission time increases linearly with length and decreases as a power law of caliber. Therefore, evolution must find a distribution of axonal numbers, lengths and calibers that balances the various tradeoffs between gains in propagation time, signal throughput and synchronicity, against volumetric and metabolic costs. Here I apply a variational method to calculate the distribution of axonal caliber in the spinal cord as a function of axonal length, that minimizes the average axonal signal propagation time, subject to the constraints of white matter total volume and the variance of propagation times, and allowing for arbitrary fiber priorities and end-points. The Lagrange multipliers obtained thereof can be naturally interpreted as 'exchange rates', e.g., how much evolution is willing to pay, in white matter added volume, per unit time decrease of propagation time. This is, to my knowledge, the first model that quantifies explicitly these evolutionary tradeoffs, and can obtain them empirically by measuring the distribution of axonal calibers. We are in the process of doing so using the isotropic fractionator method. [Preview Abstract] |
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