Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session R23: Evolutionary and Ecological Dynamics III: EvolutionFocus
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Sponsoring Units: DBIO GSNP Chair: Thierry Mora, Ecole Normale Superieure Room: 304 |
Thursday, March 5, 2020 8:00AM - 8:36AM |
R23.00001: Evolutionary dynamics of immune repertoires Invited Speaker: Thierry Mora Our adaptive immune system protects us against a wide variety of pathogens using a broad repertoire of specific receptors expressed by B and T cells, whose concentrations adapt to past experiences to encode immune memory. In this talk I will discuss how the diversity of receptors is generated and how it evolves and self-organises over time to protect us efficiently. I will present models of population dynamics as well as design principles of optimal memory encoding, and will relate them to high-throughput sequencing data. I will also discuss diversity measures and the power-law nature of the distribution of clone sizes observed in the repertoire. |
Thursday, March 5, 2020 8:36AM - 8:48AM |
R23.00002: Evolutionary regain of lost network function Mirna Kheir Gouda, Michael Manhart, Gabor Balazsi Natural or synthetic genetic network modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore such broken function is poorly understood. To test the reversibility of evolutionary breakdown, we use a synthetic gene circuit (PF) integrated into yeast cells. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation, corrupting gene circuit function. Since PF activation also provides drug resistance, we grew such corrupted mutants in both drug and inducer, imposing selection to regain drug resistance and possibly PF function. We observe various adaptation scenarios with or without repairing lost gene circuit function. The data suggest interactions between intracellular gene network dynamics and evolutionary dynamics, with possible consequences for understanding the evolution of drug resistance and developing future synthetic biology applications. |
Thursday, March 5, 2020 8:48AM - 9:00AM |
R23.00003: Measuring mutations with droplet microfluidics Mike Hennessey-Wesen, Calin Guet, Bjoern Hof Mutations are random errors in genetic material that create the diversity which underpins the development of all life. However, work done over recent years has suggested that certain mutations are not as random as once thought - several factors such as stress, chromosomal neighborhood, and transcription level have been implicated to affect genetic stability. Typically, quantitative measures of frequency and distribution of point mutations have either relied on fluctuation tests that make assumptions about the shape of mutation distributions, or on large volumes of data from multiple sources that can harbor unknown inconsistencies. We use a droplet-based, microfluidic platform to make real-time measurements of point mutations in bacteria under various conditions with high precision. We are able to alter various conditions of growth, such as heat/drug stress and transcriptional activity, and detect mutations without the use of a selective or metabolic screen, which makes our system a versatile tool for studying mutant occurrence. Our results offer a direct look into this often-simplified area of evolutionary dynamics. |
Thursday, March 5, 2020 9:00AM - 9:12AM |
R23.00004: The Evolutionary Dynamics of Incubation Periods Bertrand Ottino-Loffler, Jacob Scott, Steven Strogatz The incubation period for typhoid, polio, measles, leukemia and many other diseases follows a right-skewed, approximately lognormal distribution. Although this pattern was discovered more than sixty years ago, it remains an open question to explain its ubiquity. Here, we propose an explanation based on evolutionary dynamics on graphs. For simple models of a mutant or pathogen invading a network-structured population of healthy cells, we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness, competition dynamics, and network structure. The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk. Unlike previous explanations that rely crucially on heterogeneity, our results hold even for homogeneous populations. Thus, we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may, by chance alone, show remarkably different time courses of disease. |
Thursday, March 5, 2020 9:12AM - 9:24AM |
R23.00005: Evolution of systems with power-law memory: Do we have to die? Mark Edelman Various features of the development of individual living species are programmed. Is death also programmed and what can be the underlying mechanism providing the inevitability of death? The presented hypothesis is based on the similarity of human evolution to the evolution of discrete nonlinear systems with power-law memory. Caputo fractional logistic map is a discrete system with power-law memory and quadratic nonlinearity. In the area of parameters where the fixed point is unstable, its evolution starts as the evolution of a system with a stable fixed point but then this fixed point becomes unstable, suddenly breaks, and turns into a period two point. Under random perturbations the time spans of the evolution as a fixed point before the break (lifespans) obey the Gompertz-Makeham law, which is the observed distribution of the lifespans of living species. The reasons for modeling the evolution of humans by fractional systems are the observed power law in human memory and the viscoelastic nature of organ tissues of living species. Models with power-law memory may explain the observed decrease at very large ages of the rate of increase of the force of mortality and they imply limited lifespans. |
Thursday, March 5, 2020 9:24AM - 9:36AM |
R23.00006: Spatial Expansions and Serial Bottlenecks Produce Different Topologies of Genealogical Trees Gabriel Birzu, Oskar Hallatschek, Kirill S Korolev What do the genealogies of expanding populations look like? While recent studies recognize the importance of genealogies for inferring and predicting evolutionary dynamics, very little is known about genealogies in expanding populations. Here, we show that range expansions can produce extremely different topologies of genealogical trees, which are very sensitive to the growth dynamics at the front. When growth is cooperative, genealogies are described by the Kingman coalescent—a backward-in-time analog to neutral evolution in which only pairwise mergers between lineages occur. Weakly cooperative and non-cooperative growth result in fundamentally different trees, with multiple lineages merging at the same time. We explain these results by deriving the distribution of the effective offspring number at the front, and show that the transition between the two topologies occurs when the variance of this distribution diverges. This divergence arises due to rare fluctuations of the front shape and position. Thus, evolutionary dynamics of range expansions cannot be approximated by a deterministic model of serial bottlenecks. Our results also show that range expansions provide a robust mechanism for non-Kingman genealogies, which previously have only been attributed to natural selection. |
Thursday, March 5, 2020 9:36AM - 9:48AM |
R23.00007: Early Multicellular Organisms Co-opt Cell-Level Characteristics into Group-Level Properties via the Principle of Maximum Entropy Thomas Day, David B Yanni, Shane Jacobeen, Peter Yunker In the earliest stages of the evolution of multicellularity, genetic changes occur at the individual cell level yet selection acts at the group level. New group level traits emerge when mutations affecting cell-level properties are co-opted into consistent group-level traits. However, it is unclear how readily coherent group-level properties emerge absent a regulatory developmental plan. It even seems likely that small fluctuations at the cellular level may elicit large fluctuations at the group level, destroying the chance for survival. Here we demonstrate that lab-evolved simple multicellular groups with permanent intercellular bonds follow the principle of maximum entropy. As a result, a large space of microstates (e.g. specific cell configurations) correspond to a smaller space of macrostates (e.g. cluster volume), thereby achieving robust, consistent macroscopic properties. We derive an equation of state that relates these macroscopic properties together and experimentally verify predictions from the equation of state, demonstrating that robust group-level properties readily emerge from individual cell traits. Finally, we speculate that the emergence of group-level properties is possible within any cluster with fixed bonds between mother and daughter cells. |
Thursday, March 5, 2020 9:48AM - 10:00AM |
R23.00008: Evolution of microbial growth traits under serial dilution Jie Lin, Michael Manhart, Ariel Amir Selection of mutants in a microbial population depends on multiple cellular traits. In serial-dilution evolution experiments, three key traits are the lag time when transitioning from starvation to growth, the exponential growth rate, and the yield (number of cells per unit resource). Here we investigate how these traits evolve in laboratory evolution experiments using a minimal model of population dynamics, where the only interaction between cells is competition for a single limiting resource. We find that the fixation probability of a beneficial mutation depends on a linear combination of its growth rate and lag time relative to its immediate ancestor, even under clonal interference. The relative selective pressure on growth rate and lag time is set by the dilution factor; a larger dilution factor favors the adaptation of growth rate over the adaptation of lag time. The model shows that yield, however, is under no direct selection. |
Thursday, March 5, 2020 10:00AM - 10:12AM |
R23.00009: Physical constraints on epistasis Kabir Husain, Arvind Murugan
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Thursday, March 5, 2020 10:12AM - 10:24AM |
R23.00010: Emergence of heritability of higher-level traits in a major transition Anthony Burnetti, Seyed Alireza Zamani Dahaj, Matthew Herron, William Ratcliff
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Thursday, March 5, 2020 10:24AM - 10:36AM |
R23.00011: Lineage Branching During Recovery from Simulated Mass Extinction Dawn King, Tyler Hanke, Sonya Bahar How do population lineages diversify to fill new ecological niches? What governs the dynamics of population recovery from near-extinction? These questions are particularly urgent in our current age of climate-driven mass extinction. We investigate these questions using a computational model of evolutionary dynamics in which simulated organisms reproduce by bacterial fission or assortative mating on a two-dimensional phenotype space. This model has been shown to undergo a directed-percolation-like nonequilibrium phase transition from survival to extinction as system parameters such as maximum mutation size or death rate are varied. Here, we use methods from coalescent theory to show that population lineages undergo a structural change near the extinction-survival transition, with a sharp divergence in the time to most recent common ancestor (TMRCA). We also simulate mass extinctions, both in the neighborhood of the phase transition and in the survival regime, by either increasing the death rate of organisms, or increasing the parameter that controls their competition. We then analyze lineages, TMRCA, and other measures of population structure during successful and unsuccessful recoveries from mass extinction events. |
Thursday, March 5, 2020 10:36AM - 10:48AM |
R23.00012: Evolution of Macroscopic size in nascent multicellular organism Seyed Alireza Zamani Dahaj, Gonensin Bozdag Bozdag, Thomas Day, William Ratcliff, Peter Yunker
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Thursday, March 5, 2020 10:48AM - 11:00AM |
R23.00013: Evolution of hardwired behavioral strategies through competitive population dynamics Tong Liang, Braden A. W. Brinkman Normative approaches are commonly used to predict an organism's hardwired behaviors by optimizing utility functions such as sensory information or reward, which are loosely interpreted as proxies for evolutionary fitness. However, the validity of the assumption that utility function optimization confers true evolutionary success has seldom been explored. Here we develop mechanistic evolutionary models to investigate whether normative principles can predict the most evolutionarily advantageous strategies. With mean-field approximations and agent-based stochastic simulations, we show that the most competitive strategies that emerged from these models in environments of randomly distributed resources conform well with normative predictions, but only when organisms are sparsely distributed and interactions are rare. These results suggest that normative approaches can predict the evolutionarily most competitive innate behavioral strategies, at least when the optimized utility is directly relevant for the organisms’ survival and accounts for environmental constraints. This work bridges the gap between normative approaches and the underlying fundamental evolutionary dynamics. |
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