Lab

Evolutionary Systems µBiology Lab @ IITB

About the lab

We are working on different aspects of Evolutionary Systems Microbiology.

Featured projects (1)

Project
Theoretical explorations on evolution of digital organisms on different types of fitness landscapes. Focus on simple microbial species: resistance, persistence, growth rates.

Featured research (7)

During cooperative growth, microbes often experience higher fitness by sharing resources via metabolite exchange. How competitive species evolve to cooperate is, however, not known. Moreover, existing models (based on optimization of steady-state resources or fluxes) are often unable to explain the growth advantage for the cooperating species, even for simple reciprocally cross-feeding auxotrophic pairs. We present here an abstract model of cell growth that considers the stochastic burst-like gene expression of biosynthetic pathways of limiting biomass precursor metabolites and directly connect the amount of metabolite produced to cell growth and division, using a "metabolic sizer/adder" rule. Our model recapitulates Monod's law and yields the experimentally observed right-skewed long-tailed distribution of cell doubling times. The model further predicts the growth effect of secretion and uptake of metabolites by linking it to changes in the internal metabolite levels. The model also explains why auxotrophs may grow faster when supplied with the metabolite they cannot produce and why two reciprocally cross-feeding auxotrophs can grow faster than prototrophs. Overall, our framework allows us to predict the growth effect of metabolic interactions in independent microbes and microbial communities, setting up the stage to study the evolution of these interactions. IMPORTANCE Cooperative behaviors are highly prevalent in the wild, but their evolution is not understood. Metabolic flux models can demonstrate the viability of metabolic exchange as cooperative interactions, but steady-state growth models cannot explain why cooperators grow faster. We present a stochastic model that connects growth to the cell's internal metabolite levels and quantifies the growth effect of metabolite exchange and auxotrophy. We show that a reduction in gene expression noise can explain why cells that import metabolites or become auxotrophs can grow faster and why reciprocal cross-feeding of metabolites between complementary auxotrophs allows them to grow faster. Furthermore, our framework can simulate the growth of interacting cells, which will enable us to understand the possible trajectories of the evolution of cooperation in silico.
During cooperative growth, microbes often experience higher fitness, due to sharing of resources by metabolic exchange and herd protection through biofilm structures. However, the trajectory of evolution of competitive species towards cooperation is not known. Moreover, existing models (based on optimisation of steady-state resources or fluxes) are often unable to explain the growth advantage for the cooperating species, even for simple reciprocally cross-feeding auxotrophic pairs. We present an abstracted model of cell growth that considers the stochastic burst-like gene expression of biosynthetic pathways of limiting biomass precursor metabolites, and directly connects their cellular levels to growth and division using a "metabolic sizer/adder" rule. Our model recapitulates Monod's law and yields the experimentally observed right-skewed long-tailed distribution of cell doubling times. The model further predicts the growth effect of secretion and uptake of metabolites, by linking it to changes in the internal metabolite levels. The model also explains why auxotrophs may grow faster when provided the metabolite they cannot produce, and why a pair of reciprocally cross-feeding auxotrophs can grow faster than prototrophs. Overall, our framework allows us to predict the growth effect of metabolic interactions in microbial communities and also sets the stage to study the evolution of these interactions. Importance: Cooperative behaviours are highly prevalent in the wild, but we do not understand how it evolves. Metabolic flux models can demonstrate the viability of metabolic exchange as cooperative interactions, but steady-state growth models cannot explain why cooperators grow faster. We present a stochastic model that connects growth to the cell's internal metabolite levels and quantifies the growth effect of metabolite exchange and auxotrophy. We show that a reduction in gene expression noise explains why cells that import metabolites or become auxotrophs can grow faster, and also why reciprocal cross-feeding of metabolites between complementary auxotrophs allow them to grow faster. Our framework can simulate the growth of interacting cells, which will enable us to understand the possible trajectories of the evolution of cooperation in silico.
Cellular energetics is thought to have played a key role in dictating all major evolutionary transitions in the history of life on Earth. However, how exactly cellular energetics and metabolism come together to shape evolutionary paths is not well understood. In particular, when an organism is evolved in different energy environments, what are the phenomenological differences in the chosen evolutionary trajectories, is a question that is not well understood. In this context, starting from an E. coli K‐12 strain, we evolve the bacterium in five different carbon environments ‐ glucose, arabinose, xylose, rhamnose, and a mixture of these four sugars (in a predefined ratio) for approximately 2,000 generations. At the end of the adaptation period, we quantify and compare growth dynamics of the strains in a variety of environments. The evolved strains show no specialized adaptation towards growth in the carbon medium in which they were evolved. Rather, in all environments, the evolved strains exhibited a reduced lag phase and an increased growth rate. Sequencing results reveal that these dynamical properties are not introduced via mutations in the precise loci associated with utilization of the sugar in which the bacterium evolved. These phenotypic changes are rather likely introduced via mutations elsewhere on the genome. Data from our experiments indicate that evolution in a defined environment does not alter hierarchy in mixed‐sugar utilization in bacteria. This article is protected by copyright. All rights reserved.
Beneficial and deleterious mutations change an organism’s fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.
Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.

Lab head

Supreet Saini
Department
  • Department of Chemical Engineering

Members (4)

Anjali Mahilkar
  • University of Michigan
Rg Brajesh
  • Indian Institute of Technology Bombay
Dibyendu Dutta
  • University of Exeter
Rajesh Kannan
  • Indian Institute of Technology Madras

Alumni (3)

Mahendra Kumar Prajapat
  • National Cancer Institute NIH
Kirti Jain
  • Indian Institute of Technology Bombay
Debika Choudhury
  • Indian Institute of Technology Bombay