Agent-Based Modeling of Microbial Communities

Nagarajan, K.; Ni, Congjian; Lu, Ting

doi:10.1021/acssynbio.2c00411

Cited by 22 publications

(15 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To model the spatiotemporal dynamics of bacterial colonies in our microfluidic devices, we used an agent-based approach. There have been a number of mechanistic agent-based models of cellular populations described in the literature that share the same general methodology. − Briefly, individual cells are represented as spherocylinders that increase in length based on their growth rate and divide once reaching a set length. Cells repel contact with each other and the chamber walls.…”

Section: Resultsmentioning

confidence: 99%

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

Steppe,

Rey-Bedón,

Kumar

et al. 2024

ACS Synth. Biol.

View full text Add to dashboard Cite

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

show abstract

Section: Resultsmentioning

confidence: 99%

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

Steppe,

Rey-Bedón,

Kumar

et al. 2024

ACS Synth. Biol.

View full text Add to dashboard Cite

show abstract

“…An ABM is a collection of autonomous agents with a predefined set of rules, which depend on the existing state of the agent and external factors [22][23][24][25]. The rules can be discrete following logical if-else statements, continuous, i.e., Ordinary Differential Equations (ODEs) for intra-cellular reactions or a combination of both.…”

Section: Agent-based Modelsmentioning

confidence: 99%

Agent-based models in cellular systems

Pleyer

Fleck

2023

Front. Phys.

View full text Add to dashboard Cite

This mini-review discusses agent-based models as modeling techniques for studying pattern formation of multi-cellular systems in biology. We introduce and compare different agent-based model frameworks with respect to spatial representation, microenvironment, intracellular and extracellular reactions, cellular properties, implementation, and practical use. The guiding criteria for the considered selection of agent-based model frameworks are that they are actively maintained, well documented, and provide a model development workflow.

show abstract

“…They therefore fail to capture the stochasticity, heterogeneity and collective emergent behaviour that is intrinsic to biofilms. Agent-based models can better capture these characteristics and have become increasingly popular for studying cellular populations and biofilms [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach

Blee,

Gorochowski,

Hauert

2024

J. R. Soc. Interface.

View full text Add to dashboard Cite

Biofilms are responsible for most chronic infections and are highly resistant to antibiotic treatments. Previous studies have demonstrated that periodic dosing of antibiotics can help sensitize persistent subpopulations and reduce the overall dosage required for treatment. Because the dynamics and mechanisms of biofilm growth and the formation of persister cells are diverse and are affected by environmental conditions, it remains a challenge to design optimal periodic dosing regimens. Here, we develop a computational agent-based model to streamline this process and determine key parameters for effective treatment. We used our model to test a broad range of persistence switching dynamics and found that if periodic antibiotic dosing was tuned to biofilm dynamics, the dose required for effective treatment could be reduced by nearly 77%. The biofilm architecture and its response to antibiotics were found to depend on the dynamics of persister cells. Despite some differences in the response of biofilm governed by different persister switching rates, we found that a general optimized periodic treatment was still effective in significantly reducing the required antibiotic dose. As persistence becomes better quantified and understood, our model has the potential to act as a foundation for more effective strategies to target bacterial infections.

show abstract

Agent-Based Modeling of Microbial Communities

Cited by 22 publications

References 87 publications

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

Agent-based models in cellular systems

Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach

Contact Info

Product

Resources

About