Individual-based modeling of bacterial chemotaxis and spatial ecosystem dynamics

One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Computational modeling is an effective strategy to investigate microbial spatial dynamics. Specifically, other than classic ODE and PDE models, individual/agent-based modeling uses designated agents that describe bioreactions and mechanistic behavior of individual cell, allowing to capture the heterogeneity, emergence and stochasticity of the spatial structure of microbial population. The agent-based modeling of microbial communities is thoroughly reviewed in Chapter 1. In Chapter 2, we proposed an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by comparison of single-cell chemotaxis movement with reported experiments and models. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns. In Chapter 3, we continued to apply the model framework developed in Chapter 2 into analyzing how motility and chemotaxis shapes spatiotemporal dynamics in bacterial colonies consist of six basic types of two-species social interactive relations: neutralism, mutualism, competition, commensalism, amensalism, and parasitism. The model shows that motility reduces the effectiveness of interaction, and for mutualism, there is a trade-off between motility and cooperation for optimal growth. Further, we found the composition of six types of interactions responses to differential motility in two patterns by whether the growth of individual strain is promoted or repressed by the interaction. Also nutrient consumption rate is tested to have negative impact on the effectiveness of interactions. Lastly the chemotaxis is found to change the spatial distribution thus affect the interaction effectiveness. In Chapter 4, the model is further applied to investigate the role that motility plays in a conceptual Rock-Paper-Scissors ecosystem, involving a cyclic repressive loop in which each strain kills one another by secreting target-specific toxic molecules. In one-motile scenario, instead of "survival of the weakest", the motile species is found to predominant the colony regardless the ranking of relative invasion rates, with exception when the difference in invasion rates is large. In two-motile scenario, the colony shows consistent co-existence pattern due to the separation between the non-motile species and the other two motile species. These findings suggest that the combination of motility overrides the dynamics of species based on invasion rates. In summary, this dissertation takes agent-based modeling as a strategy to explore how motility and chemotaxis affects spatiotemporal dynamics of microbial communities by increasing the complexity of the communities gradually. The results show that motility and chemotaxis can effectively shapes the spatial pattern and composition of the colonies. This work sets another milestone of applying agent-based modeling in complex microbial systems.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biophysics & Quant Biology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Ni, Congjian

Contributors dc:contributor

• Lu, Ting
• Chemla, Yann R
• Rao, Christopher V
• Zhang, Kai

• Individual-based Model
• Agent-based Model
• Bacteria
• Computational Biology
• Mathematical Methods

Statement dc:rights

• Copyright 2024 Congjian Ni

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127308

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127308