Mathematical Modelling of Emergent Survival in Microbial Communities

This thesis investigates the principles governing emergent survival dynamics in microbial communities, with a focus on elucidating complex interaction networks that determine community stability, invasion success, and resilience to external perturbations. The concept of emergence – where community-level behaviours and properties arise from interactions among individual member species – has proven essential for understanding microbial ecosystems like the gut microbiome, where community interactions drive resilience, disease resistance, and functional diversity. Using a combination of mathematical modelling and experimental validation, I explore how factors such as nutrient dependencies, cross-feeding, and drug interactions drive community-level outcomes. The modelling results demonstrate how collective community behaviours override individual species’ sensitivities, leading to unexpected survival or extinction events, and highlight the limits of single-species predictions in complex environments. By modelling microbial communities using coupled ordinary differential equations under varied conditions, I reveal that certain inter-species interactions – particularly those involving metabolic byproducts, secondary metabolites, and cross- feeding – are essential for determining a species’ emergent community survivorship, in particular under stress conditions like low (relative) abundance, low/incomplete nutrient availability, or drug exposure. Notably, across diverse community compositions and nutrient environments, emergent survival was observed in approximately ~20% of cases, while emergent extinction occurred in ~30%. Insights into these interactions allow us to predict conditions under which communities can suppress pathogens, resist invasion, or demonstrate resilience to perturbations. The results in this thesis point towards new approaches in microbiome modulation that leverage community structure and emergent properties, from enhancing colonisation resistance to developing targeted probiotic therapies with greater chances of establishment. This work highlights and contextualises the value of population-based mathematical modelling in translating individual microbial traits to community-scale behaviours, advancing our understanding of microbial ecosystems and paving the way for precision interventions in microbiome-associated health outcomes.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• van den Berg, Naomi

Advisor dc:contributor.advisor

• Patil, Kiran

• co-invasion
• emergent extinction
• emergent properties
• emergent survival
• gut microbiome
• microbial communities
• microbial ecology
• microbial invasion dynamics
• systems biology

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/15b95dea-c4d9-4a34-87ab-dcd2f778fd31/download
• http://purl.org/NET/rdflicense/allrightsreserved

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.119712

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/386552