Bulletin of the American Physical Society
APS March Meeting 2018
Volume 63, Number 1
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session P51: Single-Cell Variability and DynamicsFocus
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Sponsoring Units: DBIO Chair: Daniel Charlebois, State Univ of NY- Stony Brook Room: LACC 511C |
Wednesday, March 7, 2018 2:30PM - 3:06PM |
P51.00001: Regulatory mechanisms of the multi-scale effects of intrinsic and extrinsic noise in gene expression on single cells and cell populations Invited Speaker: Andre Ribeiro Intrinsic and extrinsic noise are ubiquitous in gene expression, affecting single-cell functioning and cell-to-cell phenotypic diversity. It is thus expected that cells have evolved means to regulate the influence of these noise sources. We conjectured that the rate-limiting steps in transcription initiation, due to their sequence-dependence as well as their responsiveness to external regulation, are key regulators of this influence. Here, we provide empirical evidence that, at the single-gene level, the kinetic regulation of these rate-limiting steps influences the impact of intrinsic and extrinsic noise sources on gene expression dynamics and on the cell-to-cell variability of its product numbers. Next, we use in silico models with empirically validated parameter values to explore the multi-scale range of these effects, i.e. from single gene, to small and large-scale genetic circuits. Based on our results, we hypothesize that bacterial cells capitalize on the rate-limiting steps’ nature of transcription initiation to evolve and tune the temporal and population-level behavioral diversity of genes and gene networks. |
Wednesday, March 7, 2018 3:06PM - 3:18PM |
P51.00002: Biophysical constraints determine the selection of phenotypic fluctuations during directed evolution Hong-Yan Shih, Harry Mickalide, David Fraebel, Seppe Kuehn, Nigel Goldenfeld We address the question of the role of selection in evolution and its relationship with phenotypic fluctuations. Phenotypic fluctuations have been conjectured to be beneficial characteristics to protect against fluctuating selection from environmental changes, the so-called “bet-hedging” strategy. However, it is not well-understood how phenotypic fluctuations shape the evolutionary trajectories of organisms. To address these questions we have performed directed evolution experiments and modeling on the speed of migration phenotype of chemotactic bacteria. We present a theoretical model that recapitulates the observed reduction of phenotypic fluctuations in experiment. Our stochastic modeling on the evolution of migration fronts suggests that whether or not phenotypic fluctuations grow or shrink during successive rounds of selection and growth is determined by both strength of selection and the existence of physical constraints. |
Wednesday, March 7, 2018 3:18PM - 3:30PM |
P51.00003: Population Genetics of Single-cell Variation in Microbial Growth Michael Manhart, Eugene Shakhnovich Microbial populations undergo multiple stages of growth, including a lag phase, an exponential growth phase, and a stationary phase. Mutations can therefore improve the frequency of a genotype not only by increasing its exponential growth rate, but also by decreasing the lag time or adjusting the yield (resource efficiency). However, recent experiments in E. coli and yeast have shown that these traits can also vary across genetically-identical single cells. Both the mean and variance of these distributions can evolve under mutation, selection, and genetic drift. To interpret this data in an evolutionary context, we develop a framework for population genetics with single-cell variation across multiple phases of microbial growth. We show how variation in single-cell lag times creates large fluctuations in lineage dynamics, allowing a mutation to fix more rapidly than would otherwise be expected. We also quantify how selection acts on this variability, which we use to predict patterns of coevolution for these growth traits. |
Wednesday, March 7, 2018 3:30PM - 3:42PM |
P51.00004: Using Single-Cell Microfluidics to Measure Cellular Memory Tamas Szekely, Zhihao Cai, Martin Sauzade, Eric Brouzes, Gabor Balazsi Microbial populations of genetically identical cells can contain different phenotypes, with each cell's phenotype changing over time. Tolerant phenotypes confer protection from environmental stresses. By restoring the original population, a few tolerant cells can save the entire population from extinction. Their main downside is slower growth, implying a trade-off between maximizing growth and ensuring survival during stress. |
Wednesday, March 7, 2018 3:42PM - 4:18PM |
P51.00005: Protein fluctuations in single cells and cell-to-cell variability Invited Speaker: Hanna Salman An important characteristic of biological cells is their ability to generate a wide spectrum of phenotypes across a genetically homogeneous population. This variability is believed to play an important role in diverse biological phenomena. We have recently found that variability in cellular protein content among genetically identical cells (used here to simply represent non-genetic phenotypic variability), displays some universal statistical properties that seem to result from the single-cell expression dynamics. In other words, temporal fluctuations in protein expression measured in a single cell exhibit similar statistical properties to fluctuations measured in a snapshot of a population (i.e. cell-to-cell variability at a given time). However, this does not imply that the dynamics of protein expression is ergodic. Our measurements show that cells exhibit distinct characteristics that are maintained over their lifetime. This individuality is observed in the average cell-size as well as the average protein content measured over tens of generations. A simple mapping of the cellular dynamics with a feedback mechanism that links consecutive generations can reproduce all statistical properties observed. It also provides valuable insight on how cell individuality can be maintained for many generations. |
Wednesday, March 7, 2018 4:18PM - 4:30PM |
P51.00006: A Markovian Approach towards Bacterial Cell Size Control and Homeostasis Yanyan Chen, Javier Buceta Regardless recent developments, the mechanisms coupling growth and division to achieve cell size homeostasis are still not well understood. Herein we propose a Markovian growth/division theoretical model for rod-shaped bacteria, that we complement with experimental and computational approaches, to reconcile current knowledge. In this context we show that size control relies on the limited efficiency of the division machinery and that classifying cell sizes as a function of their prospective septa provides a compelling framework to understand growth and division. Our results reveal that size homeostasis in wild-type E. coli cells depends mainly on three cell populations and that the incremental rule is a general consequence, and not the cause, of a memoryless process that is compatible with a well-defined length scale set by cells to orchestrate division. Altogether, our study sheds light on the processes that lead to homeostasis in rod-shaped bacteria and paves the way to understand the problem of size regulation in other organisms. |
Wednesday, March 7, 2018 4:30PM - 4:42PM |
P51.00007: Engineering cellular computations through biophysical design Ching-Hao Wang, Caleb Bashor, Pankaj Mehta It is known that living cells use complex biochemical networks to perform sophisticated computational tasks. Yet, a major question in synthetic and systems biology remains: How are network level computational properties encoded in the biophysics of protein-protein interactions (PPIs)? We address this question by developing a new computational framework relating the thermodynamics of PPIs to network signaling properties in a simplified synthetic biochemical system inspired by the “reader-writer-eraser" signaling paradigm. Our computational framework allows us both to rationally design a desired input-output relation and to identify networks that optimize information transmission (InfoMax). More generally, our work shows that complex computational and information processing tasks can be programmed in cellular signaling circuits by manipulating biophysical parameters. |
Wednesday, March 7, 2018 4:42PM - 4:54PM |
P51.00008: Measuring Gene Expression Dynamics in the Early Drosophila Embryo. Stefano Ceolin, Christophe Jung, Ulrich Unnerstall, Ulrike Gaul The regulatory network that drives the patterning of the early Drosophila embryo is a well-known paradigm to study the general principles underlying spatial and temporal control of gene expression. |
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