Integrated modeling of bacterial metabolism

Liao, Chen

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https://hdl.handle.net/2142/101340 Copy

Description

Title

Integrated modeling of bacterial metabolism

Author(s)

Liao, Chen

Issue Date

2018-04-17

Director of Research (if dissertation) or Advisor (if thesis)

Lu, Ting

Doctoral Committee Chair(s)

Lu, Ting

Committee Member(s)

• Jin, Yong-Su
• Rao, Christopher
• Maslov, Sergei

Department of Study

Bioengineering

Discipline

Bioengineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• integrated modeling
• systems biology
• synthetic biology
• bacterial metabolism
• quantitative molecular physiology
• dynamical system
• mathematical modeling
• biosimulation

Abstract

Cell metabolism is an extraordinary complex process that involves coordinated operation of multiple cellular processes. The overwhelmingly complexity due to the interconnected nature poses a grand challenge for systems biologists to achieve a system-level understanding of bacterial metabolism. Despite previous models have yielded insightful results, most of them focus on specific subsystems and endeavors to build highly integrated computational models are still rare. Here, I present my work towards developing and applying integrated modeling framework to quantitatively understand and predict bacterial physiology and metabolism. In Chapter 2, I present an integrated modeling framework for acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum. ABE fermentation is a biphasic metabolic process during which cells convert carbon sources to organic acids that are later re-assimilated to produce solvents as a strategy for cellular survival. A modular modeling approach is adopted for model construction: The entire system is decomposed into three modules (metabolic reactions, gene regulation, and environmental cues), which are later developed separately and assembled together through their input-output relationships. Parameters are identified by reproducing fermentation profiles of the wild-type strain and its several mutants. With the trained model, I systematically perturb the three modules, one at a time, and validate model predictions with experimental data. The results provide a systematic understanding of ABE fermentation, expanding the spectrum of our knowledge about complex clostridial metabolism and physiology and possibly facilitating the development of systems engineering strategies for the production of advanced biofuels. In Chapter 3, I present another integrative modeling framework to predict synthetic gene networks behaviors in Escherichia coli host across scales from single-cell dynamics to population structure and to spatial ecology. The framework at the single-cell level consists of a coarse-grained yet mechanistic description of host physiology that involves dynamic resource partitioning, multi-layered circuit-host coupling including both generic and system-specific interactions, and a detailed kinetic module of exogenous circuits. After model training, it successfully predicts a large set of experimental data concerning the host and simple foreign gene over-expression and from both wild-type cells and genetic mutants growing in different environmental conditions. The single-cell model is then used to examine a growth-modulating feedback circuit whose dynamics can be qualitatively altered by host-circuit interactions. To further elucidate the host-circuit coupling effects across biological scales, I integrate the single-cell model presented above and an individual-based population model into a multi-scale framework, and use this framework to simulate population structures of Escherichia coli carrying a genetic toggle switch in homogenous and heterogeneous space. This work advances our quantitative understanding of gene circuit behaviors and also benefits the rational design of synthetic gene networks. In Chapter 4, I aim to understand whether and how bacteria robustly achieve optimal proteome allocation in stationary and dynamic environments. By developing a coarse-grained model of proteome partitioning centering on ppGpp-mediated regulation, we find that the ability of E. coli to optimize its proteome lies in an ultrasensitive, negative feedback controlling topology, with the ultrasensitivity arising from zero-order kinetics of tRNA aminoacylation and translation and the negative feedback from ppGpp-controled ribosome synthesis. Interestingly, together with the time scale separation between slow ribosome kinetics and fast ppGpp and amino acid turnovers, the ultrasensitive negative feedback topology confers E. coli an optimization mechanisms that mimics sliding mode control, a widely used nonlinear controlling method in man-made systems. We show that such a controlling mechanism is robust against parameter variations and molecular fluctuations, and also efficient for maximal biomass production. This work uncovers the quantitative mechanism controlling E.coli proteome allocation, shedding light on quantitative microbial physiology, and benefiting synthetic biology for designing robust gene networks. In the final chapter, I summarize my contributions of the thesis and discuss their overall significance and innovation. The directions for future extensions of the current modeling framework are also provided.

Graduation Semester

2018-05

Type of Resource

text

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http://hdl.handle.net/2142/101340

Copyright and License Information

Copyright 2018 Chen Liao

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