(6iw) Leveraging Big Data and Engineering Fundamentals Towards Biological Discovery

Recent advances in -omics technologies allow us to rapidly collect large amounts of diverse information about biological systems. I am interested in building mechanistic models of biological processes through integration of the -omics data with physical and chemical dynamics that govern biological systems.

My future research will pursue two distinct directions: (I) Gaining mechanistic insights into cell-to-cell variability in mammalian cellular signaling networks and (II) Identifying the forces that shape bacterial genome evolution.

Aim I: Mechanistic insights into cell population heterogeneity. Mammalian signaling networks exhibit extensive cell-to-cell heterogeneity. This heterogeneity has important functional consequences, for example, in emergence of drug resistant subpopulations in tumors. The heterogeneity originates from the cell-to-cell variability in network parameters, such as protein abundances and rates of biochemical reactions. Thus, its mechanistic understanding requires knowledge of the distribution over network parameters consistent with single cell data. This problem is of particular importance now as single cell data is being rapidly generated for diverse cell types, organisms, and biological contexts. I have developed a maximum entropy based computational framework to infer parameter distributions from single cell data. I want to apply this framework to study phenotypic heterogeneity downstream of growth factor signaling in mammalian cells.

Aim II: Quantifying evolutionary forces in bacteria. Bacteria play a significant role in human health as well as biogeochemical cycles. Notably, multiple evolutionary forces such as mutations, recombination (swapping of DNA in the population), and ecological niche dynamics simultaneously shape genetic diversity in bacteria. Notably, we are experiencing an exponential growth in the number of sequenced bacterial genomes. I have developed computational tools to quantify the key evolutionary forces from genomics data. My goal is to better understand how external conditions, such as host diet, and selection dictate the innovation and spread of novel phenotypes in natural bacterial populations. This work will have broad applications in understanding, for example, evolutionary dynamics of host-associated microbial communities, determinants of the spread of antibiotic resistance, and stability of photosynthetic phenotypes in oceanic bacterial populations.

Teaching Interests:

I believe that an essential dimension of an ideal academician is the ability to convey complex ideas to an inquisitive audience. I see teaching and mentoring not as extraneous to my academic career but as one of its vital components.

I can teaching mathematically oriented core chemical engineering subjects such as thermodynamics, transport phenomena, chemical kinetics, and process control. Additionally, my postdoctoral training allows me to teach many non-traditional subjects, for example, computational and systems biology, modeling of biomolecules, and networks in biology. I will ensure that the course material has an extensive online component so that these courses will be easily transformed into MOOCs for students who are not able to physically attend the course.