Special scientific lecture by Dr. Neetika Ahlawat, Early Career Fellow, DBT/Wellcome Trust (IA), Department of Chemical Engineering, Indian Institute of Technology Bombay  on 25th Oct. 2024 at 11.00 AM in the seminar hall, main building. Dr. Neetika received her PhD from the IISER Mohali. Her areas of expertise are: Species interactions (Host-pathogen and intermicrobial coevolution); Niche specialization; Evolution of multicellularity; Genetic variation and dominance effects of mutations

Title:                  Laboratory experiments as a tool to predict evolution.

Speaker:           Dr. Neetika Ahlawat, IIT Bombay

Date & Time:  25th Oct (Friday) 11 AM

Venue:              Seminar Hall, Main Building

Abstract of the talk: 

Predicting evolutionary trajectories is important to understand a variety of processes  including the emergence & spread of antibiotic resistance, host-pathogen coevolution, generation of biodiversity, genetic disease risk, drug design, and biotic response to climate
change. However, despite several decades of research, we still lack a predictive framework of evolution, largely due to our inability to understand the role of genetics and environment in the process. In this context, I will talk about my work on understanding how closely related environments alter adaptive trajectories, and how do these adaptation patterns vary with the complexity of the organism. After evolving E. coli and S. cerevisiae in highly similar environments for 300 generations, I test their ability to grow and produce biomass. Our results show that the adaptive trajectories are non-identical even in highly similar (or, ‘synonymous’) environments, making our ability to predict evolution challenging. However, the growth rate and yield of these evolved populations was highly predictable in unrelated environments! In the future, I plan on working towards uncovering the genetic and ecological complexities that dictate evolutionary trajectories. Towards the end of my talk, I will discuss these two research projects focused on (a) understanding the nature of spontaneous mutations through statistical quantification of the distribution of heterozygosity coefficient, and (b) evolutionary dynamics of the integration of a novel species to a coexisting stable microbial community