Assembling Functionally Stable Microbial Communities Under Fluctuating Environments

Microbes (Prokaryotes and Fungi) play a dominant role in carbon and nutrient cycling globally by decomposing organic matter in aquatic as well as terrestrial ecosystems. Therefore, understanding how microbial communities assemble and function in nature is among the foremost challenges in ecology. However, because these communities are extraordinarily complex with myriad interacting species, our ability to predict their dynamics in the face of natural and human-induced environmental changes (such as climatic warming) is severely limited. The goal of this PhD project is to develop a modelling framework implemented as a software package to predict the assembly and functional stability of microbial communities in fluctuating environments. Some key questions that may be addressed are:

1. What combination of resource competition and cooperation through cross-feeding on metabolic by-products (species interaction structure) guarantee functionally stable communities in a given temperature regime?

2. How does temperature change affect the stability of community-level species interaction structure over time?

3. What species functional capabilities (traits) and interaction structures maximize functional stability in the face of directionally changing or fluctuating environmental temperatures?

The student will take the novel approach of merging AI-assisted genome-scale stoichiometric modeling of microbial metabolic networks (GEMs) with models of consumer-resource dynamics to more accurately capture community dynamics under different resource availability and temperature conditions. The model will be applied to data from laboratory experiments, testing predictions about which microbial strains are likely to coexist in a shared environment based on their functional traits, and the resultant effects on community respiration rate, metabolite production, and population abundances. Respiration quantifies the carbon processing rate of the community and is a crucial factor in ecosystem and global carbon cycles. Metabolite production reflects the functional composition of the community. Total abundance reflects secondary productivity (capture carbon ability) of the community while strain-level abundances and covariances between them reflect how distinct metabolic strategies interact to produce community function. Successfully developing such an integrated, mechanistic modelling framework holds great potential to reveal general ecological rules, and will be a significant step towards predicting the effects of climate change on ecosystem functioning, with applications in ecosystem conservation, restoration and engineering, as well as fundamental microbiome research.

Thus this project will advance the current state-of-the-art of ecological theory through the development of a novel computational model. To this end, it will leverage a combination of theory — on Complex adaptive and ecological systems (Pawar), Metabolic networks (Kontoravdi and Pawar) and biochemical process optimisation (Chachuat) — with empirical data (Laboratory experiments - Ledesma-Amaro). This will result in a uniquely multi-disciplinary PhD research project that combines mathematical theory, computational biology, laboratory experiments, and statistical analyses of empirical data, to address an important and general problem in applied ecology. The project's objectives also contribute strongly to the UL National Environmental Research Council's goal of enabling society to predict and respond to the effects of climate and ecosystem functioning. The student will also receive advanced training in 6 of the 15 “most wanted skills” identified in NERC’s 2012 report: Modelling, Multi-disciplinarity, Data management, Numeracy, Microbiology, and Freshwater Science. The student will learn high-level computing and mathematical modelling skills in the area of Nonlinear Dynamics and Biological Systems & Network theory, and data management and analysis skills training through the empirical parameterization and validation. The complexity of the numerical computational analyses of dynamical networks along with the laboratory data will necessitate development of advanced numeracy skills.

This project is be part of the SSCP DTP program (https://www.imperial.ac.uk/grantham/education/science-and-solutions-for-a-changing-planet-dtp/), and the succesful candidate will join its unique multidisciplinary PhD training programme its multi-year and interdisciplinary cohort of existing students spanning all areas of earth science and environmental research at the College.

How to apply: Please send your CV, contact information for 2 references, and a 1 page statement describing your interest in the project to Samraat ([Email Address Removed] ) by Monday January 08 2024.