Bulletin of the American Physical Society
APS March Meeting 2016
Volume 61, Number 2
Monday–Friday, March 14–18, 2016; Baltimore, Maryland
Session F35: Population and Evolutionary Dynamics IIFocus Session
|
Hide Abstracts |
Sponsoring Units: DBIO GSNP Chair: Uwe Tauber, Virginia Tech Room: 338 |
Tuesday, March 15, 2016 11:15AM - 11:51AM |
F35.00001: The effect of extrinsic noise on the dynamics of simple gene network motifs Invited Speaker: Michael Assaf Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise has been experimentally shown to dominate the variation. Here we develop an analytical formalism that allows for calculation of the combined effect of intrinsic and extrinsic noise on gene expression motifs. We introduce a new and generic method for modeling bounded extrinsic noise as an auxiliary species in the master equation. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a non-regulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the extrinsic noise properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of extrinsic noise on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene expression experiments. [Preview Abstract] |
Tuesday, March 15, 2016 11:51AM - 12:03PM |
F35.00002: Backward evolution from gene network dynamics Merzu Belete, Daniel Charlebois, G\'{a}bor Bal\'{a}zsi Gene expression is often controlled by regulator genes that form gene regulatory network cascades. How mutation in the genes comprising regulatory cascades influences cell populations dynamics has not been adequately investigated. In this study, we developed a model to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. We find a paradoxical outcome of evolution, where a mutation in a regulator gene leads to an interaction between gene regulatory network dynamics and population dynamics, causing in certain cases a permanent decrease in population fitness. [Preview Abstract] |
Tuesday, March 15, 2016 12:03PM - 12:15PM |
F35.00003: Collective evolution of cyanobacteria and cyanophages mediated by horizontal gene transfer Hong-Yan Shih, Tim Rogers, Nigel Goldenfeld We describe a model for how antagonistic predator-prey coevolution can lead to mutualistic adaptation to an environment, as a result of horizontal gene transfer. Our model is a simple description of ecosystems such as marine cyanobacteria and their predator cyanophages, which carry photosynthesis genes. These genes evolve more rapidly in the virosphere than the bacterial pan-genome, and thus the bacterial population could potentially benefit from phage predation. By modeling both the barrier to predation and horizontal gene transfer, we study this balance between individual sacrifice and collective benefits. The outcome is an emergent mutualistic coevolution of improved photosynthesis capability, benefiting both bacteria and phage. This form of multi-level selection can contribute to niche stratification in the cyanobacteria-phage ecosystem. This work is supported in part by a cooperative agreement with NASA, grant NNA13AA91A/A0018. [Preview Abstract] |
Tuesday, March 15, 2016 12:15PM - 12:27PM |
F35.00004: Environmental quality modulates the cooperative and competitive nature of a microbial cross-feeding mutualism Tim Hoek, Kevin Axelrod, Eugene Yurtsev, Jeff Gore Mutualisms are essential for ecosystem function and stability. However, in some environments the competitive aspects of an interaction may dominate the mutualistic aspects. Although these transitions could have far-reaching implications, it has been difficult to study the causes and consequences of this mutualistic-competitive transition in experimentally tractable systems. Here we experimentally study a microbial cross-feeding mutualism in which each yeast strain supplies an essential amino acid for its partner strain. We find that, depending upon the amino acid concentration, this pair of strains can exhibit any of: obligatory mutualism, facultative mutualism, competition, parasitism, competitive exclusion, or failed mutualism leading to extinction of the population. A simple model capturing the essential features of this interaction predicts that environmental quality specifies the outcome and provides a ``phase diagram'' of net interactions in this mutualism. In addition, the model accurately predicts that changes in the dynamics of the mutualism in deteriorating environments can predict that population collapse is imminent. Our results provide a general framework for how mutualisms may transition between qualitatively different regimes of interaction. [Preview Abstract] |
Tuesday, March 15, 2016 12:27PM - 12:39PM |
F35.00005: Predicting evolutionary dynamics Gabor Balazsi We developed an ordinary differential equation-based model to predict the evolutionary dynamics of yeast cells carrying a synthetic gene circuit. The predicted aspects included the speed at which the ancestral genotype disappears from the population; as well as the types of mutant alleles that establish in each environmental condition. We validated these predictions by experimental evolution. The agreement between our predictions and experimental findings suggests that cellular and population fitness landscapes can be useful to predict short-term evolution. [Preview Abstract] |
Tuesday, March 15, 2016 12:39PM - 12:51PM |
F35.00006: Complex dynamics of selection and cellular memory in adaptation to a changing environment Edo Kussell, Wei-Hsiang Lin We study a synthetic evolutionary system in bacteria in which an antibiotic resistance gene is controlled by a stochastic on/off switching promoter. At the population level, this system displays all the basic ingredients for evolutionary selection, including diversity, fitness differences, and heritability. At the single cell level, physiological processes can modulate the ability of selection to act. We expose the stochastic switching strains to pulses of antibiotics of different durations in periodically changing environments using microfluidics. Small populations are tracked over a large number of periods at single cell resolution, allowing the visualization and quantification of selective sweeps and counter-sweeps at the population level, as well as detailed single cell analysis. A simple model is introduced to predict long-term population growth rates from single cell measurements, and reveals unexpected aspects of population dynamics, including cellular memory that acts on a fast timescale to modulate growth rates. [Preview Abstract] |
Tuesday, March 15, 2016 12:51PM - 1:03PM |
F35.00007: Exploiting temporal gradients of antibiotic concentration against the emergence of resistance Marianne Bauer, Vudtiwat Ngampruetikorn, Erwin Frey, Greg Stephens A very simple model for antibiotic resistance - involving one normal and one more resistant species interacting indirectly through a carrying capacity - shows that the temporal variation of the antibiotic can affect the effect of the antibiotic. For a single antibiotic pulse, we find that for different minimal inhibitory concentrations of the two species an optimal pulse shape may exist, which increases the likelihood of bacterial extinction. For a long series of pulses, efficiency does not vary monotonically with the length of the gap between two individual pulses, but instead, the gap length can be optimised by exploiting the competition between the two species. Finally, a series of pulses is not always more efficient than a single pulse. Shorter pulses may be more efficient in an initial time window without risking population level resistance. We elucidate this behaviour with a phase diagram, and discuss the meaning of this work for current experiments. [Preview Abstract] |
Tuesday, March 15, 2016 1:03PM - 1:15PM |
F35.00008: Emergence of elevated levels of multiple infections in spatial host-virus dynamics Bradford Taylor, Catherine Penington, Joshua Weitz Bacteria are subject to infection and potentially to multiple simultaneous infections by viruses. Multiply infected hosts have altered life-history traits (e.g., viral burst size) and evolutionary rates (e.g., viral recombination). Yet our understanding of multiple infections of microbes is limited to lab settings where the ratio of inoculant viruses to hosts is controlled. In contrast, rates of multiple infection in natural environments are unknown. Here, we develop an individual based model to quantify rates of multiple infections by a single viral type. We explore different dispersal regimes by varying the viral adsorption rate. High dispersal regimes lead to spatial dynamics and rates of multiple infection equivalent to predictions from mean field models. Local clustering of bacterial hosts occurs for low dispersal. Comparing to mean field, the clustering leads to increased rates of multiple infection and fatter tails in the distribution of the number of internal viruses. The emergence of increased colocalization of viruses with infected hosts leads to these deviations. We show these deviations result from the wave-like spread of viruses when invading clusters of bacteria. Our work represents a key step in understanding the population-level effects of multiple infections. [Preview Abstract] |
Tuesday, March 15, 2016 1:15PM - 1:27PM |
F35.00009: Focusing antibody responses against distraction and loss in diversity Shenshen Wang, Mehran Kardar, Arup Chakraborty Pathogens are complex and evolving fast. They have developed full ranges of disguises to divert immune responses and often manage to escape recognition and thereby outpace natural immunity. A prominent example is the scarce and staggered development of broadly neutralizing antibodies against highly mutable viruses. It remains unclear under what evolutionary conditions these exceptional antibodies could emerge and dominate the response. To address this challenge, we construct an individual-based stochastic model of the Darwinian evolution of antibody-producing immune cells. We consider complexity of viral epitopes, vary seeding diversity of the immune cell population, and allow a time varying population size and extinction -- new aspects essential for designing a realistic vaccine. We show that various temporal statistics of antigenic environments would select distinct evolutionary paths that lead to predominantly non-neutralizing, strain-specific or broadly neutralizing antibody responses. We suggest strategies to focus antibody responses on the targeted vulnerability of the virus and confer selective advantage to cross-reactive lineages. This implies a new step toward an effective vaccine against rapidly mutating complex pathogens. [Preview Abstract] |
Tuesday, March 15, 2016 1:27PM - 1:39PM |
F35.00010: Modeling the interactions between pathogenic bacteria, bacteriophage and immune response Chung Yin (Joey) Leung, Joshua S. Weitz The prevalence of antibiotic-resistant strains of pathogenic bacteria has led to renewed interest in the use of bacteriophage (phage), or virus that infects bacteria, as a therapeutic agent against bacterial infections [1]. However, little is known about the theoretical mechanism by which phage therapy may work. In particular, interactions between the bacteria, the phage and the host immune response crucially influences the outcome of the therapy. Few models of phage therapy have incorporated all these three components, and existing models [2] suffer from unrealistic assumptions such as unbounded growth of the immune response. We propose a model of phage therapy with an emphasis on nonlinear feedback arising from interactions with bacteria and the immune response. Our model shows a synergistic effect between the phage and the immune response which underlies a possible mechanism for phage to catalyze the elimination of bacteria even when neither the immune response nor phage could do so alone. We study the significance of this effect for different parameters of infection and immune response, and discuss its implications for phage therapy. References: [1] C. Potera, Environ. Health Perspect. 121, A48 (2013). [2] B. R. Levin and J. J. Bull, Nature Rev. Microbiol. 2, 166 (2004). [Preview Abstract] |
Tuesday, March 15, 2016 1:39PM - 1:51PM |
F35.00011: Modeling HIV Cure Alan Perelson, Jessica Conway, Youfang Cao A large effort is being made to find a means to cure HIV infection. I will present a dynamical model of post-treatment control (PTC) or “functional cure” of HIV-infection. Some patients treated with suppressive antiviral therapy have been taken off of therapy and then spontaneously control HIV infection such that the amount of virus in the circulation is maintained undetectable by clinical assays for years. The model explains PTC occurring in some patients by having a parameter regime in which the model exhibits bistability, with both a low and high steady state viral load being stable. The model makes a number of predictions about how to attain the low PTC steady state. Bistability in this model depends upon the immune response becoming exhausted when over stimulated. I will also present a generalization of the model in which immunotherapy can be used to reverse immune exhaustion and compare model predictions with experiments in SIV infected macaques given immunotherapy and then taken off of antiretroviral therapy. Lastly, if time permits, I will discuss one of the hurdles to true HIV eradication, latently infected cells, and present clinical trial data and a new model addressing pharmacological means of flushing out the latent reservoir. [Preview Abstract] |
Tuesday, March 15, 2016 1:51PM - 2:03PM |
F35.00012: The kinetics and location of intra-host HIV evolution to evade cellular immunity are predictable John Barton, Nilu Goonetilleke, Thomas Butler, Bruce Walker, Andrew McMichael, Arup Chakraborty Human immunodeficiency virus (HIV) evolves within infected persons to escape targeting and clearance by the host immune system, thereby preventing effective immune control of infection. Knowledge of the timing and pathways of escape that result in loss of control of the virus could aid in the design of effective strategies to overcome the challenge of viral diversification and immune escape. We combined methods from statistical physics and evolutionary dynamics to predict the course of \textit{in vivo} viral sequence evolution in response to T cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Our predictions agree well with both the location of documented escape mutations and the clinically observed time to escape. We also find that that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways, and the duration over which control is maintained by specific immune responses prior to escape, could be exploited for the rational design of immunotherapeutic strategies that may enable long-term control of HIV infection. [Preview Abstract] |
Tuesday, March 15, 2016 2:03PM - 2:15PM |
F35.00013: Outbreak and Extinction Dynamics in a Stochastic Ebola Model Garrett Nieddu, Simone Bianco, Lora Billings, Eric Forgoston, James Kaufman A zoonotic disease is a disease that can be passed between animals and humans. In many cases zoonotic diseases can persist in the animal population even if there are no infections in the human population. In this case we call the infected animal population the reservoir for the disease. Ebola virus disease (EVD) and SARS are both notable examples of such diseases. There is little work devoted to understanding stochastic disease extinction and reintroduction in the presence of a reservoir. Here we build a stochastic model for EVD and explicitly consider the presence of an animal reservoir. Using a master equation approach and a WKB ansatz, we determine the associated Hamiltonian of the system. Hamilton's equations are then used to numerically compute the 12-dimensional optimal path to extinction, which is then used to estimate mean extinction times. We also numerically investigate the behavior of the model for dynamic population size. Our results provide an improved understanding of outbreak and extinction dynamics in diseases like EVD. [Preview Abstract] |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2025 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700