THE BAKSHI Laboratory of Systems and Synthetic Microbiology
We are interested in reverse engineering (systems microbiology) and forward engineering (synthetic microbiology) biological networks (gene-regulatory networks and microbial eco-systems) for fundamental and applied reasons. A major focus in our current research is to understand and combat antibiotic resistance and tolerance in microbes. We are using a combination of advanced experiments and modelling to understand these processes in model microbes, and engineering modified microbes to fight back in natural contexts. We effectively combine and use existing methods, and develop new ones when the existing ones fail. Please see below for more details.
What are the factors that regulate growth, and the intrinsic and extrinsic mortality of bacterial cells? What are the implications of these on the fitness of a population? How can we tune these for applications?
The fate of a single bacteria is dictated by the interactions of genes within it, and its interaction with other bacterial cells present in the system. Both of these processes are intrinsically noisy and therefore give rise to substantial heterogeneity in the population. We are trying understand the different aspects of genetic and cellular networks that make microbes suppress or exploit these heterogeneity to coordinate phenotypes of multiple cells, or to diversify for hedging their bets to improve population fitness in uncertain times.
Why do genetic control circuits fail to operate as intended? What are the factors that dictate their stable operation in a population over time? How can we engineer circuits that perform in a robust and stable manner over time?
The stochastic nature of gene-expression and cellular interference effects impair most synthetic control circuits, and also makes the functional ones genetically unstable. The vast majority of approaches to engineer synthetic circuits still relies on cumbersome slow design-build-test cycles to optimize the performance. We are developing pipelines to combinatorially engineer many different variants in parallel, and a platform for screening phenotypes of thousands of different circuit-variants at once, in tightly controlled conditions, with single cell resolution. This will not only help us to find the functional variants, but also to identify the failure modes and develop strategies for predictive engineering.
"Measure what is measurable, and make measurable what is not so." - Galileo
We are developing new approaches and tools in molecular biology, microfluidics, and microscopy, and combining them in novel ways to study the gene-expression and physiology of microbes at different levels (single molecule, single cell, population) and designing software to extract meaning information from these data. We have developed several platforms to quantify the growth, death, lag, and gene-expression of individual cells over many generations in constant or complex changing conditions for systems-level analysis or for characterising synthetic systems. We iteratively use computational modelling to interpret the observations and to predict testable outcomes, and use experiments to refine and refute these predictions.