2008 — 2011 |
Emonet, Thierry |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Emt/Bsse: Hierarchical Representation and Simulation of Modular Cellular Systems
Biological systems are the product of an evolutionary process of random tinkering and selection that resulted in unexpected and non-intuitive ?engineering? solutions to dynamically varying conditions. Thus, biological systems are robust, adaptive and evolvable information processing systems that operate asynchronously and in parallel on multiple scales. The examination and characterization of the design principles of biological circuits has the potential to revolutionize biology, medicine and the way computing and communication systems are built. This project is pioneering important advances at the interface between biology and computation by pursuing two complementary goals: (1) to develop a modular, parallel-ready simulator to replicate the multi-scalar architecture of complex biological systems; (2) to discover key design principles relevant to information processing systems in general by reproducing biological design in silico.
Information processing by cells encompasses multiple scales connecting molecular events to phenotypes. Current simulation techniques have limited multi-scale and modular capabilities, resulting in models that describe only a single feature of a given system and miss the relationships between architecture, function and behavior. This research effort addresses these limitations by representing biological systems as a hierarchy of functional executable modules. The design of the platform obeys four basic principles: 1) components are objects; 2) objects are governed by rules; 3) rules include some degree of stochasticity; and 4) objects and rules are organized in functional and spatial modules that compose a hierarchy. The development of the new platform is driven by the construction of simulations of key biological model systems with an unprecedented scope and precision, such as bacterial chemotaxis, epidermal growth factor receptor signaling, the acute inflammatory response, and parallel processing by bacterial colonies. The reproduction of these biological systems in silico is providing insights into their design principles, which in turn advances the future design and implementation of distributed technological systems.
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2013 — 2017 |
Emonet, Thierry |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
The Contribution of Phenotypic Variability to Chemotactic Performance
DESCRIPTION (provided by applicant): Phenotypic variability, a fundamental property of isogenic cell populations across all biological systems, creates challenges for medicine because it diversifies individual cell responses to treatments by introducing outliers that survive and resume the progression of the disease. So far, the mechanisms of phenotypic variability have been primarily studied in the context of developmental or regulatory pathways that produce discrete outcomes. The long-term goal of this project is to understand how complex dynamical functions can be tuned over a continuum by taking advantage of fluctuations in protein abundance. This project will build a quantitative understanding of the mechanisms and functional role of phenotypic variability for cellular dispersion and navigation using the well-characterized chemotaxis systems of E. coli and Salmonella as model systems. The central hypothesis is that cell-to-cell variability can resolve performance trade-offs of a single signaling pathway by creating individual cells with different capabilities, ensuring that subpopulations of cells will perform optimally in various environments and tasks. The experimental plan builds on a theoretical and computational framework established in previous works. An experimental platform recently developed in our lab allows for measurement of the trajectories of swimming cells and the abundance of fluorescently labeled proteins in the same live individual as they navigate controlled environments. Preliminary results indicate that quantitative relationships between protein quantity, behaviors, and chemotactic performance can be established at the single-cell level throughout the population. Iterative predictive modeling and experiments will extend current quantitative models to capture the cause and consequences of cell-to-cell variability on chemotactic behavior and population structure. Aim 1 will map distributions of chemotaxis protein levels onto distributions of individul diffusive behaviors and individual performance in exploratory or invasive tasks. Aim 2 will map chemotaxis protein abundance to chemotactic performance in static and time-varying chemical gradients to reveal the consequences of cell-to-cell variability for tracking various gradients. Ai 3 will characterize the trade-offs faced by individual cells in performing chemotaxis and examine whether cell-to-cell variability can alleviate these trade-offs at the population level. The experimental and theoretical framework developed for this project will have a broad impact on a fundamental challenge: to go beyond the characterization of average signaling network performance and to predict the consequences of fluctuations in molecular parameters on single-cell dynamical behaviors.
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2019 — 2021 |
Emonet, Thierry |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
The Contribution of Phenotypic Diversity and Temporal Variability to Population Signal Transduction
Project Summary: Biological functions are typically performed by groups of cells that predominantly express the same genes yet display a continuum of phenotypes. The long-term goal of this project is to understand how such variations influence functional properties at the population level, which is a fundamental problem in cell biology with critical implications for public health. As a model system, we have been using the bacterial chemotaxis system of Escherichia coli because it involves non-trivial functions, such as signal detection, amplification, memory and adaptation, and it is well-characterized molecularly. During the previous funding cycle, we developed microfluidics and computational technology to measure protein abundance, swimming behavior, and performance of the same individual cells in a race up a gradient of attractant. These data revealed that chemotactic performance depends nonlinearly on swimming phenotype, which in turn depends nonlinearly on protein abundances. These nonlinearities have important consequences: because the average of a nonlinear function is different from the nonlinear function of the average, the population could outcompete the performance of its mean phenotype in some conditions. This result illustrates a basic and ubiquitous mechanism by which phenotypic diversity can modulate function in cell biology, even in the absence of any interactions among cells. In this next funding cycle, we plan to examine the consequence of this mechanism for signal transduction by combining our microfluidics and computational framework with single-cell FRET technology developed by long- term collaborator Dr. Thomas Shimizu. This new combined platform enables high-throughput single-cell measurements of signaling dynamics in microfluidics chambers. Using this approach, we will examine how temporal variations in individual cells, due to spontaneous fluctuations in the pathway (Aim 1) and to cell cycle regulation (Aim 2), affect their ability to process signals. These aims will also quantify the contribution of these processes to the standing variation in an isogenic population. Finally, in Aim 3 we will examine how phenotypic diversity shapes the population?s capability to process signals. Taken together, the proposed aims will go beyond the population-average characterization of this signaling network to reveal how diverse individual cells process signals while growing and fluctuating, and how this diversity shapes the population?s signal transduction capabilities beyond those of its mean phenotype.
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2020 — 2021 |
Emonet, Thierry |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Spontaneous Organization of Phenotypes During Collective Migration
Project Summary Bacteria live in structured communities where coordinated interactions between individuals often result in collective behaviors beneficial to the population. At the same time, even isogenic bacteria display phenotypic heterogeneity, which diversifies individual behavior and enhances the resilience of the population in unexpected situations. Understanding how the interplay of diversity and collective behavior contributes to spatial organization and function in cell populations is a fundamental problem in cell biology that has been difficult to tackle experimentally and theoretically because of difficulties in measuring and modeling processes at multiple scales. Here we focus on how phenotypic diversity in motility and chemotactic ability contributes to the spatial organization and phenotypic composition of a population of bacteria as it migrates through diverse environments. Our starting point is the recent discovery by us and others that when chasing traveling fronts of attractant generated by their own consumptions, bacteria spontaneously sort themselves along the traveling gradient: high- performing phenotypes localize at the front where the signal (gradient steepness) is weaker and low-performing phenotypes at the back where the signal is stronger but the risk of falling behind is higher. Thus, a leader-follower organization of the phenotypes emerges, even in isogenic populations, with leaders driving the migration and followers falling off and colonizing space behind the moving front. These observations raise the following basic questions: 1) How do phenotypes reorganize themselves when the population encounters a different environment where the most performant phenotype is now different? What does that tell us about the capacity of a single genotype to navigate as a group through multiple environment? 2) To what extent can cell growth partially compensate for the leakage of cells and how does this affect the phenotypic composition and organization of the migrating population? 3) To what extent does the spatial sorting of motility and chemotaxis phenotypes seed spatial organization of virulence factors that tend to be coregulated? To address these questions, we will develop new mathematical models of collective bacterial migration that include three key ingredients: a continuum of phenotypes, cell growth, and diverse environments. To constrain models, we will use E. coli chemotaxis because it is well-characterized, positioning us to discover general principles, and P. aeruginosa, an opportunistic pathogen that shares some features of the E. coli chemotaxis pathway but expresses two different stator systems necessary for migration through different environments.
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