Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session B4: Systems Biology |
Hide Abstracts |
Sponsoring Units: DBIO Chair: Moumita Das, RIT Room: 263 |
Monday, March 13, 2017 11:15AM - 11:27AM |
B4.00001: Particle aggregation during receptor-mediated endocytosis Sheng Mao, Andrej Kosmrlj Receptor-mediated endocytosis of particles is driven by large binding energy between ligands on particles and receptors on a membrane, which compensates for the membrane bending energy and for the cost due to the mixing entropy of receptors. While the receptor-mediated endocytosis of individual particle is well understood, much less is known about the joint entry of multiple particles. Here, we demonstrate that the endocytosis of multiple particles leads to a kinetically driven entropic attraction, which may cause the aggregation of particles observed in experiments. During the endocytosis particles absorb nearby receptors and thus produce regions, which are depleted of receptors. When such “depleted” regions start overlapping, the corresponding particles experience “osmotic-like” attractive entropic force. If the attractive force between particles is large enough to overcome the repulsive interaction due to membrane bending, then particles tend to aggregate provided that they are sufficiently close, such that they are not completely engulfed before they come in contact. We discuss the necessary conditions for the aggregation of cylindrical particles during receptor-mediated endocytosis and comment on the generalization to spherical particles. [Preview Abstract] |
Monday, March 13, 2017 11:27AM - 11:39AM |
B4.00002: Multiscale Modeling of Angiogenesis and Predictive Capacity Samara Pillay, Helen Byrne, Philip Maini Tumors induce the growth of new blood vessels from existing vasculature through angiogenesis. Using an agent-based approach, we model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death-like processes. We use the transition probabilities associated with the discrete model and a discrete conservation equation for cell occupancy to determine collective cell behavior, in terms of partial differential equations (PDEs). We derive three PDE models incorporating single, multi-species and no volume exclusion. By fitting the parameters in our PDE models and other well-established continuum models to agent-based simulations during a specific time period, and then comparing the outputs from the PDE models and agent-based model at later times, we aim to determine how well the PDE models predict the future behavior of the agent-based model. We also determine whether predictions differ across PDE models and the significance of those differences. This may impact drug development strategies based on PDE models. [Preview Abstract] |
Monday, March 13, 2017 11:39AM - 11:51AM |
B4.00003: Temporal organization of cellular self-replication Victor Alexandrov, Rami Pugatch Recent experiments demonstrate that single cells grow exponentially in time. A coarse grained model of cellular self-replication is presented based on a novel concept -- the cell is viewed as a self-replicating queue. This allows to have a more fundamental look into various temporal organizations and, importantly, the inherent non-Markovianity of noise distributions. As an example, the distribution of doubling times can be inferred and compared to single cell experiments in bacteria. We observe data collapse upon scaling by the average doubling time for different environments and present an inherent task allocation trade-off. [Preview Abstract] |
Monday, March 13, 2017 11:51AM - 12:03PM |
B4.00004: Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation Xiao Gan, Réka Albert Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. We identified the long-term behaviors of a multi-level, 70-node discrete dynamic model of the stomatal opening process in plants. We reduce the model's huge state space by reducing unregulated nodes and simple mediator nodes, and by simplifying the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models. [Preview Abstract] |
Monday, March 13, 2017 12:03PM - 12:15PM |
B4.00005: Non-genetic phenotypic variability and its effect on population performance. Thierry Emonet, Adam J Waite, Nicholas W Frankel, Yann S Dufour, Junjiajia Long, Jessica F Johnston Substantial non-genetic diversity in complex behaviors, such as chemotaxis in E. coli, has been observed for decades, but the relevance of this diversity for the population is not well understood. What are the trade-offs that bacteria face in performing chemotaxis in different environments? Can population diversity be tailored to resolve these trade-offs? We examined the functional role of non-genetic diversity in cellular migration by measuring the phenotype and chemotactic performance of tens of thousands of individual, freely-swimming \textit{Escherichia} \textit{coli} as they climbed a gradient of attractant. We discovered that spatial structure spontaneously emerged from initially well-mixed wild type populations due to non-genetic diversity. By manipulating the expression of a key chemotaxis protein, we established a causal relationship between protein expression, non-genetic diversity, and performance that was theoretically predicted. This approach generated a complete phenotype-to-performance map, in which we found a nonlinear regime. We used this map to demonstrate how the shape of a phenotypic distribution can have as large of an effect on performance as changing the mean phenotype, suggesting that evolution could act on both during the process of adaptation. [Preview Abstract] |
Monday, March 13, 2017 12:15PM - 12:27PM |
B4.00006: Modeling population dynamics of mitochondria in mammalian cells Kellianne Kornick, Moumita Das Mitochondria are organelles located inside eukaryotic cells and are essential for several key cellular processes such as energy (ATP) production, cell signaling, differentiation, and apoptosis. All organisms are believed to have low levels of variation in mitochondrial DNA (mtDNA), and alterations in mtDNA are connected to a range of human health conditions, including epilepsy, heart failure, Parkinson’s disease, diabetes, and multiple sclerosis. Therefore, understanding how changes in mtDNA accumulate over time and are correlated to changes in mitochondrial function and cell properties can have a profound impact on our understanding of cell physiology and the origins of some diseases. Motivated by this, we develop and study a mathematical model to determine which cellular parameters have the largest impact on mtDNA population dynamics. The model consists of coupled ODEs to describe subpopulations of healthy and dysfunctional mitochondria subject to mitochondrial fission, fusion, autophagy, and mutation. We study the time evolution and stability of each sub-population under specific selection biases and pressures by tuning specific terms in our model. Our results may provide insights into how sub-populations of mitochondria survive and evolve under different selection pressures. [Preview Abstract] |
Monday, March 13, 2017 12:27PM - 12:39PM |
B4.00007: Visualizing the response of a gut bacterial population to antibiotic perturbations Brandon Schlomann, Travis Wiles, Karen Guillemin, Raghuveer Parthasarathy Each of our intestines is home to a vast ecosystem composed of trillions of bacteria in a dynamic environment. Bacterial communities face fluctuations in nutrient influx, invasions by new microbes, physical disturbances from peristalsis, and, perhaps, the arrival of antibiotic drugs. Metagenomic profiling has shown that antibiotic treatments can cause major changes in the composition of species present in the gut, at timescales shorter than a day. How this happens is unknown, as these dynamics have never been observed directly. I'll present recent work that addresses this by using well-defined microbial communities in a model organism, the zebrafish. Light Sheet Fluorescence Microscopy is used to image a commensal species of \textit{Vibrio }responding to antibiotic perturbations in the guts of live, larval fish. We find that sub-lethal concentrations of different classes of antibiotics induce similar physical responses in \textit{Vibrio}, namely filamentation and reduction of motility. The arrested bacteria then aggregate and can be ejected via peristalsis, resulting in large population collapses. These observations suggest that antibiotics can cause large disruptions to gut ecosystems even in low concentrations, and that physical processes may be important drivers of response dynamics. [Preview Abstract] |
Monday, March 13, 2017 12:39PM - 12:51PM |
B4.00008: NERNST Vortex Potential Of A Genetic Oscillator Merrill Garnett, Bill Jones The vortex is a dynamic spiral. In molecular biology these have not been reported. We report a vortex compound, with oscillating energy. Toroglobulin (1) transfers 416 mv. to histone. This histone reductase enriches charge in the chromosome in spool proteins around which DNA is coiled. Controlling chromosome charge introduces energetics to gene compression. Impedance spectroscopy shows symmetric oscillations. Specific frequencies show amplitude increases. The Mott-Schottky scans show frequency bands. Histone bands are electronically reduced by Toroglobulin by 416 mv. The Nernst potentials of chemical systems correlate electric gradient to concentration gradients of charged particles. Charge polarization refers to laminar alignment. In formation of the Toroglobulin Ginzburg-Landau vortex, the polarization follows filament curvatures which spiral back on themselves. The magnetic dipoles achieve interactive resonance (esr). This spiral resonator with magnetic interfaces produces the measured Nernst potential.\newline 1. Garnett, M., U.S. Pat. App. No. 62339699, Ruthenium Sphingomyelin Complexes and Methods for Their Use in the Treatment of Tumors, May 20, 2016. [Preview Abstract] |
Monday, March 13, 2017 12:51PM - 1:03PM |
B4.00009: Topologically protected modes in non-equilibrium stochastic systems Suriyanarayanan Vaikuntanathan Non-equilibrium driving of biophysical processes is believed to enable their robust functioning despite the presence of thermal fluctuations and other sources of disorder. Such robust functions include sensory adaptation, enhanced enzymatic specificity and maintenance of coherent oscillations. Elucidating the relation between energy consumption and organization remains an important and open question in non-equilibrium statistical mechanics. Here we report that steady states of systems with non-equilibrium fluxes can support topologically protected boundary modes that resemble similar modes in electronic and mechanical systems. Akin to their electronic and mechanical counterparts, topological protected boundary steady states in non-equilibrium systems are robust and are largely insensitive to local perturbations. We argue that our work provides a framework for how biophysical systems can use non equilibrium driving to achieve robust function. [Preview Abstract] |
Monday, March 13, 2017 1:03PM - 1:15PM |
B4.00010: Passive and active response of bacteria under mechanical compression Renata Garces, Samantha Miller, Christoph F. Schmidt Bacteria display simple but fascinating cellular structures and geometries. Their shapes are the result of the interplay between osmotic pressure and cell wall construction. Typically, bacteria maintain a high difference of osmotic pressure (on the order of 1 atm) to the environment. This pressure difference (turgor pressure) is supported by the cell envelope, a composite of lipid membranes and a rigid cell wall. The response of the cell envelope to mechanical perturbations such as geometrical confinements is important for the cells’ survival. Another key property of bacteria is the ability to regulate turgor pressure after abrupt changes of external osmotic conditions. This response relies on the activity of mechanosensitive (MS) channels: membrane proteins that release solutes in response to excessive stress in the cell envelope. We here present experimental data on the mechanical response of the cell envelope and on turgor regulation of bacteria subjected to compressive forces. We indent living cells with micron-sized beads attached to the cantilever of an atomic force microscope (AFM). This approach ensures global deformation of the cell. We show that such mechanical loading is sufficient to gate mechanosensitive channels in isosmotic conditions. [Preview Abstract] |
Monday, March 13, 2017 1:15PM - 1:27PM |
B4.00011: An Empirically Calibrated Model of Cell Fate Decision Following Viral Infection. Seth Coleman, Oleg Igoshin, Ido Golding The life cycle of the virus (phage) lambda is an established paradigm for the way genetic networks drive cell fate decisions. But despite decades of interrogation, we are still unable to theoretically predict whether the infection of a given cell will result in cell death or viral dormancy. The poor predictive power of current models reflects the absence of quantitative experimental data describing the regulatory interactions between different lambda genes. To address this gap, we are constructing a theoretical model that captures the known interactions in the lambda network. Model assumptions and parameters are calibrated using new single-cell data from our lab, describing the activity of lambda genes at single-molecule resolution. We began with a mean-field model, aimed at exploring the population averaged gene-expression trajectories under different initial conditions. Next, we will develop a stochastic formulation, to capture the differences between individual cells within the population. The eventual goal is to identify how the post-infection decision is driven by the interplay between network topology, initial conditions, and stochastic effects. The insights gained here will inform our understanding of cell fate choices in more complex cellular systems. [Preview Abstract] |
Monday, March 13, 2017 1:27PM - 1:39PM |
B4.00012: Information Propagation in Developmental Enhancers Siddhartha Jena, Michael Levine Rather than encoding information about protein sequence, certain lengths of noncoding DNA, called enhancers, interact with protein machinery such as transcription factors to precisely regulate gene expression. Enhancers have been studied extensively in the fruit fly \emph{Drosophila melanogaster}, where they regulate the expression of developmental genes that establish the blueprint of the adult fly. It has been suggested that enhancer sequences possess a specific but unknown syntax with regards to the placement and strength of transcription factor binding sites. Moreover, studies in divergent fly species have shown that compensatory evolution allows for maintenance of enhancer functionality despite considerable variation in primary DNA sequence. Here, the possible role of enhancers as signal processing modules is studied as a way of explaining these two findings. We first demonstrate how this framework can be used to explain the fine-tuned spatiotemporal dynamics of gene expression. We then explore the evolutionary pressure on enhancer sequences and the resulting emergence of enhancers that are linked by compensatory mutations. This study provides a possible mechanism for the function of multiple enhancers linked to a single gene. [Preview Abstract] |
Monday, March 13, 2017 1:39PM - 1:51PM |
B4.00013: Interference of two co-directional exclusion processes: stochastic kinetics of unconventional gene translation Debashish Chowdhury, Bhavya Mishra A molecular machine called ribosome carries out gene translation. During translation, the template mRNA also serves as a track for the noisy, but directed, motor-like movement of a ribosome that is powered by chemical energy. Many ribosomes can simultaneously move along the same mRNA, each synthesizing a distinct copy of the same protein. The concept of Totally Asymmetric Simple Exclusion Process (TASEP) provides a natural theoretical framework for modeling the stochastic ribosome traffic on an mRNA. Here we develop a model of two interfering co-directional TASEP on the same one-dimensional lattice, but with their respective distinct entry sites which correspond to the sites of initiation of gene translation. The model is motivated by an unconventional mode of gene translation called Internal Ribosome Entry Site (IRES). We solve the master equations under mean-field approximation and demonstrate the accuracy of the mean-field predictions by carrying out computer simulations of the model. Our results show the effects of interference of the flow of the two species of particles on their respective flux and density profiles. We present the rich phase diagram of the model. [Preview Abstract] |
Monday, March 13, 2017 1:51PM - 2:03PM |
B4.00014: Heterodimerization of wild-type and mutant fibroblast growth factor receptors in cell-derived vesicles. Kalina Hristova, Nuala Del Piccolo, Sarvenaz Sarabipour The activity of receptor tyrosine kinases (RTKs) is controlled through their lateral dimerization in the plasma membrane. RTKs are believed to form both homodimers and heterodimers, and the different dimers are believed to play unique roles in cell signaling. However, RTK heterodimers remain poorly characterized, as compared to homodimers, due to limitations in current experimental methods. Here, we develop a F\"{o}rster Resonance Energy Transfer (FRET)-based methodology to assess the thermodynamics of hetero-interactions in the plasma membrane. To demonstrate the utility of the methodology, we use it to study the hetero-interactions between three Fibroblast Growth Factor Receptors -- FGFR1, FGFR2, and FGFR3 -- in the absence of ligand. Our results show that all possible FGFR heterodimers form, suggesting that the biological roles of FGFR heterodimers may be as significant as the homodimer roles. We further investigate the effect of two pathogenic point mutations in FGFR3 (A391E and G380R) on heterodimerization. We show that each of these mutations stabilize most of the heterodimers, with the largest effects observed for FGFR3 wild-type/mutant heterodimers. We thus demonstrate that the methodology presented here can yield new knowledge about RTK interactions and can further our understanding of signal transduction across the plasma membrane.. [Preview Abstract] |
Monday, March 13, 2017 2:03PM - 2:15PM |
B4.00015: Reconstructing a network of health deficits during human aging Spencer Farrell, Arnold Mitnitski, Kenneth Rockwood, Andrew Rutenberg We have developed a computational model of human aging and mortality that captures Gompertz’s law of exponentially increasing mortality with age together with the approximately exponential average increase of the Frailty Index with age. The Frailty Index is the proportion of binary health deficits that an individual has acquired. Our stochastic dynamical model consists of a generated network of these interacting health deficits. Using information with respect to mortality, we observe an approximately power-law spectrum of mutual information of individual deficits with respect to their degree of connectivity in the generated network. To characterize the information spectrum of real health deficits, we must reconstruct the connectivity of the real network using observational health data. We have used our model data to test and develop reconstruction algorithms, which we apply to observational data. [Preview Abstract] |
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