Sarika Mehra

Biography

B. Tech, Biochemical Engineering and Biotechnology, IIT Delhi, 1999 Ph.D., Chemical Engineering, University of Minnesota, 2005

Research Interest

Primary research interests are in the area of systems biology and metabolic engineering, with focus on systems of relevance in medicine and biotechnology. Systems biology is a study of biological processes in terms of the molecular components of genes, proteins and metabolites. These processes are a manifestation of biochemical reaction networks and complex gene regulatory networks. Currently, we are employing a multidisciplinary approach using high-throughput experimental techniques and sophisticated computational tools to decipher these networks, both at the macroscopic and microscopic levels. To build the macroscopic network topology, whole genome expression profiling (using microarrays) is used along with bioinformatics and reverse engineering algorithms. On the other hand, detailed mechanistic models are developed to provide a microscopic view of regulation in these systems. A few projects are listed below: System-wide analysis to reconstruct regulatory networks in Streptomyces coelicolor: In the past few years, many pathogenic bacteria have developed resistance to commonly used antibiotics. A variety of genes have been discovered that impart resistance to the pathogenic bacteria through different mechanisms. Complex genetic networks tightly regulate many of these genes. A better understanding of resistance networks is essential to combat this health crisis and to develop novel anti-microbials. In this project we are examining the cellular networks involved in resistance to commonly used antibiotics and drugs. Using microarrays along with computational approaches, a global map of resistance mechanisms is being built using Streptomyces coelicolor as a model system. Systems biology of Mycobacterium: Tuberculosis (TB) affects more than 2 billion people worldwide. Two-component systems (TCS) are intimately involved in generating suitable responses in bacteria while adapting to a variety of environmental conditions such as those encountered by M. Tb in vivo. In this multi-institutional project we are examining the cellular network(s) involved in stress response and pathogenesis in Mycobacterium species using a systems biology approach. To obtain a comprehensive picture of these pathways, various kinds of high throughput assays will be used in combination with computational methods of simulation and network identification. Cell Culture Engineering: The demand for recombinant proteins for therapeutic applications has increased dramatically in recent years. Many of these are complex glycoproteins and antibodies for which mammalian cells are the preferred expression systems. However, the capacity of these cultures is limited and has slowed the delivery of necessary biopharmaceutical products.. There is a need to increase the productivity of these cell lines and develop better processes in a short time. In this project we are trying to understand the biology of mammalian cell lines under production conditions. In addition, we are investigating different cell engineering approaches to improve the productivity of industrial cell lines. Mathematical Modeling of Genetic Networks: Signaling molecules produced by many bacteria for inter-cellular communication play an important role in many biological processes of biotechnological and medical relevance. For example, peptide pheromone inducible conjugation system contributes to the dissemination of antibiotic resistance by enterococci. Similarly, the butyrolactone system in Streptomyces species acts as a genetic switch in initiation antibiotic synthesis. We are using mathematical tools to understand the dynamics of such systems. These tools include deterministic and stochastic models coupled with cell population balance models. The stochastic models are necessary to capture the cell to cell variation due to intrinsic noise in gene expression, whereas the cell population balance models are essential to account for extrinsic noise.