Research Projects

Microbes are omnipresent and significantly impact human health and the environment, both positively and negatively. Consequently, people have sought to harness microbes and microbial communities for various beneficial purposes, such as using probiotics to improve gut health or genetically engineering them to produce valuable enzymes. Therefore, understanding the strategies microbes use for survival and fitness is essential. The prevailing methodology in this field relies heavily on experimental approaches. This approach has uncovered numerous mysteries related to microbial growth patterns. For instance, it is observed that increasing the gene copy number of a protein does not guarantee a proportional rise in its production, highlighting the complexity of these systems. The substrate utilization pattern of microbes is another mystery. Different growth setups yield varying strategies for nutrient usage. Developing a predictive mathematical model explaining these behaviours could improve microbial growth optimization efforts in bioprocess engineering. Similarly, engineering microbial consortia for applications in health and agriculture often lacks a predictive modeling framework. This gap impedes rational designs of microbial communities that exhibit desirable traits. Additionally, bacterial size regulation remains an enigma. Elucidating the coordination between biomass growth and cell division, which determines the average birth size bacteria assume in a given condition, is paramount. It could provide valuable insights into combating antibiotic tolerance, as tolerant bacteria tend to be smaller than usual. In this project, we propose to utilize a coarse-grained mathematical modeling framework, recently co-developed by me, aiming to combine mathematical objectivity with biological insights. Although biochemical modeling is not new, our Coarse-Grained Modeling approach presents a novel avenue that could yield substantial insights. Traditional models attempt to detail all biochemical reactions via complex kinetic frameworks, which can obscure causal mechanisms. Coarse-grained models, on the other hand, focus on extracting critical variables while simplifying less relevant ones. This technique can help find minimal mathematical models that encapsulate the essential phenomena observed. Such a model can then predict the system's behaviour under novel circumstances transparently and interpretably and suggest intervention strategies to achieve desired results. The aim is to establish a unifying mathematical framework to explain various experimental observations related to macromolecular composition, nutrient uptake patterns, size and shape determination, and social behaviors of microbial cells. Additionally, we will develop testable predictions about how these microbes respond to different genetic and environmental perturbations.