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Microbial growth control in changing environments : Theoretical and experimental study of resource allocation in Escherichia coli

Abstract : Growth is the most fundamental property of life. Growth consists in the transformation of matter and energy from the environment into diverse organic structures. Interestingly, general growth laws relate the macromolecular composition of the cell to growth rate. These laws are widespread and conserved in different microbial species, suggesting a fundamental principle of design. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation and explained by the principles of natural selection. However, the vast majority of these studies focus on steady-state growth. Such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this thesis is to extend the theoretical and experimental studies of microbial growth strategies to changing environments.Using a self-replicator model, we developed a theoretical framework that encapsulates the main features of growth. We formulate dynamical growth maximization as an optimal control problem that the microbial cell must solve in order to allocate the available resources to the gene expression machinery or to metabolism. Using Pontryagin's Maximum Principle, we have derived a general solution to the optimization problem and we have compared the optimal strategy with possible implementations of growth control in bacterial cells. Our results show that simple control strategies that maximize the growth-rate at steady state are suboptimal for transitions from one growth regime to another. We show that a near-optimal control strategy in dynamical conditions requires information about several, rather than a single, physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by the signaling molecule ppGpp in the enterobacterium Escherichia coli. The strategy involves sensing a discrepancy between the concentrations of precursor metabolites and ribosomes, and the control of the rate of ribosome synthesis in a switch-like manner.Even though this switch-like ribosome synthesis has been suggested by published data, the phenomenon has never been experimentally confirmed. We therefore measured ribosomal abundance in Escherichia coli at the single-cell level during a nutrient upshift. More precisely, we constructed a strain in which a fluorescent marker has been attached to a ribosomal subunit, thus allowing in-vivo monitoring of the abundance of ribosomes. We monitored this strain in a microfluidics device designed for long-term imaging of individual cells in a continuous culture, and used this experimental setup to simulate a nutrient upshift by changing the input medium. We developed a Kalman smoothing method for extracting quantitative information about resource allocation to ribosome synthesis from the raw data. Even though our preliminary results do not allow to reach a final conclusion, they do suggest the presence of oscillatory patterns after an upshift that are reminiscent of the expected behavior.Our results demonstrate that the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment. The proposed control scheme correctly reproduces the observed growth laws at steady state, but also predicts novel and unexpected behaviors when applied to a dynamical environment. Our improved understanding of the principles that govern the control of bacterial growth could be used for improving biotechnological processes, in particular those that use microorganisms to produce high valuable-added products for the chemical or biomedical industry.
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Submitted on : Tuesday, January 16, 2018 - 3:43:06 PM
Last modification on : Friday, November 20, 2020 - 12:00:08 PM
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  • HAL Id : tel-01685626, version 1




Nils Giordano. Microbial growth control in changing environments : Theoretical and experimental study of resource allocation in Escherichia coli. Quantitative Methods [q-bio.QM]. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAV021⟩. ⟨tel-01685626⟩



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