Multi-scale stochastic analysis of cellular chemical reaction networks and their response to environmental change

Bianchi, David M.

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https://hdl.handle.net/2142/115725 Copy

Description

Title

Multi-scale stochastic analysis of cellular chemical reaction networks and their response to environmental change

Author(s)

Bianchi, David M.

Issue Date

2022-04-20

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

Luthey-Schulten, Zaida A

Doctoral Committee Chair(s)

Luthey-Schulten, Zaida A

Committee Member(s)

• Chemla, Yann R
• Hirata, So
• Vanderpool, Carin K

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Stochastic Biology
• Cell Reaction Network Simulations
• Computational Biology
• Gene Regulatory Networks

Abstract

This is a study of the varied responses of living cells from a variety of single celled organisms including S. cerevisiae, E. coli, and the synthetic organism JCVI-syn3A to environmental change. Biological cells are incredible physical-chemical systems with a multitude of avenues by which to respond to external stimuli including but not limited to metabolic reaction pathways, gene regulatory and expression networks, and growth mechanisms. I will discuss each of these cellular processes through the viewpoint of the chemistry and physics of the living cell as well as providing an analysis of the coupling between each of these respective components. The dynamic interactions between metabolic and gene expression networks in living cells are both awe-inducing and mystifying. Even for the most studied of laboratory organisms such as Escherichia coli the characterization of these interactions and furthermore the functions of a large percentage of the cellular genome (i.e approximately 40% or more in some cases) remain unknown. The development of multi-scale simulation paradigms is necessary in order to further capture the behaviors generated by the complex couplings across many levels that exist in the cellular milieu. Recently, the J. Craig Venter Institute (JCVI) developed JCVI-syn3A (syn3A): a synthetic, genetically minimal bacterial cell containing 493 genes. Due to its reduced genome, syn3A provides an ideal platform from which to approach one of the National Science Foundation’s “10 Big Ideas”: Understanding the Rules of Life, at least on a unicellular level. The renowned physicist Richard Feynman once said “What I cannot create, I do not understand”. With this way of thinking, here I will describe my work as part of a larger effort to address this fundamental question by working to create an in silico kinetic model of syn3A that enables the study of the fundamental linkages coupling its gene expression and metabolic networks. I will describe previous work in multi-scale method development applied to a genetic switch in yeast that was then utilized in making the syn3A model robust across the wide-range of biomolecular particles with varying intracellular concentrations found within a living cell. This methodology allows the in silico model of syn3A to be responsive to dynamic changes in gene expression and metabolism as a cell responds in its struggle to maintain homeostasis. I will then discuss later work in E. coli that analyzed the effects of the fundamental cellular process of gene duplication and its effects on an sRNA-mediated network of response to chemical stress due to the presence of a new nutrient source in its environment. I will then describe the application the aforementioned multi-scale method to communicate information between reaction networks in syn3A that enables simulations spanning the wide scales of space, time and concentration encountered in the cellular context, as well as the integration of experimental data to build and refine a model for cell growth, that predicts and agrees with experimentally measured doubling times across a population of synthetic syn3A cells. Finally, I will consider work that utilized computational analysis including novel protein structure prediction techniques to elucidate the function of previously mysterious genes that are involved in the growth and cell division of JCVI-syn3A. This extends my previous work in developing a growth model linked to lipid metabolism and membrane protein expression, by resolving key actors at the molecular level that are heavily responsible for the reproduction and generation of daughter cells with consistent morphology. The JCVI-syn3A minimal cell both in its in vivo and in silico forms, due to its reduced genetic composition, provides a basis from which to understand “the minimal cellular rules of life” in future scientific investigations and may perhaps provide a versatile platform for bio-engineering and bio-medical processes.

Graduation Semester

2022-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/115725

Copyright and License Information

Copyright 2022 David M. Bianchi

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