Shireesh Srivastava

Group leader, Systems Biology for Biofuel
International Centre for Genetic Engineering and Biotechnology
Aruna Asaf Ali Marg
110 067 New Delhi, India
E-mail: shireesh@icgeb.res.in
Tel: +91-11-26741358 ext 450

Education

Michigan State University, East Lansing, MI, USA, PhD (Chemical Engineering), 2007

Indian Institute of Science, Bangalore, KA, India, MSc (Eng.), Chemical Engineering, 2001
Devi Ahilya University, Indore, MP, India, MSc (Biotechnology),1999

Career History

Since 2017, Group Leader, Systems Biology for Biofuels Group, ICGEB New Delhi, India
2012-2016, Team Leader, DBT-ICGEB Center for Advanced Bioenergy Research, New Delhi, India
2007-2012, Postdoctoral Visiting Fellow, Laboratory of Metabolic Control, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health (NIH), Rockville, MD, USA
2002-2007, Research Assistant, Department of Chemical Engineering, Michigan State University, East Lansing, MI, USA

Scientific Activity

Research in my lab involves applying the methods and skills of metabolic systems biology to optimize growth rate and biofuel production rates of diverse microorganisms. We supplement experiments with modeling approaches in order to streamline discovery and optimization. This includes application of flux balance analysis (FBA) and metabolic flux analysis (MFA) to identify the intracellular distribution of metabolic fluxes and identify the targets for further manipulation. The predictions of metabolic modeling are verified experimentally. This approach is being investigated in various microorganisms, including, but not limited to, native isolates of cyanobacteria. Additionally, traditional metabolic engineering (knock-out and overexpression of genes) as well as synthetic biology approaches (introduction of optimized heterologous pathways) are being employed in cyanobacteria to make diverse products including biofuels.

We are also actively investigating improved productivity through adaptation and process optimization. This includes systems biology investigation to identify the changes associated with improved strain performance.

Please click on the “Research Interests and Description” link above for a slightly more detailed description of our research activities.

Teaching Activity

Coordinator, PhD Course ICG 604 Synthetic and Systems Biology, ICGEB New Delhi
Member, PhD Admissinos Committee
Member, Insitutional Human Ethics Committee
2001, Teaching Assistant, Department of Chemical Engineering, Michigan State University, East Lansing, MI, USA

Selected publications

Shah AR, Ahmad A, Srivastava S†, Jaffar Ali BM†. Reconstruction and analysis of a genome-scale metabolic model of Nannochloropsis gaditana. Algal Res. 2017; 26:354–364.

Ahmad A, Hartman H, Krishnakumar S, Fell DA, Poolman MG†, Srivastava S†. A Genome Scale Model of Geobacillus thermoglucosidasius (C56-YS93) reveals its biotechnological potential on rice straw hydrolysate. J. Biotechnol. 2017;251:30-37.

Desai T, Srivastava S. Constraints-Based Modeling to Identify Gene Targets for Overproduction of Ethanol by Escherichia coli: The Effect of Glucose Phosphorylation Reaction. Metabolomics 2015;5: 145. doi:10.4172/21530769.1000145.

Srivastava S and Chan C. Application of metabolic flux analysis to identify the mechanisms of free fatty acid toxicity to human hepatoma cell line. Biotechnol Bioeng. 2008 Feb 1;99(2):399-410.

Srivastava S, Li Z, Yang X, Yedwabnick M, Shaw S, Chan C. Identification of genes that regulate multiple cellular processes/responses in the context of lipotoxicity to hepatoma cells. BMC Genomics. 2007 Oct 9;8:364.

Books

Desai TS, Dutt V, Srivastava S. Systems biology and metabolic engineering of cyanobacteria for biofuel production. In: Marine Bioenergy: Trends and Developments, Se-Kwon Kim (Ed), CRC Press 2015; 163–178. DOI: 10.1201/b18494-13.

Srivastava S, Li Z, Chan C. Identification of gene-networks associated with cell-death in lipotoxicity. Methods in Molecular Biology: Caspase Regulation. 2010. Ed. Jeffrey Varner.

Chan C, Li Z, Srivastava S. Integration of Micro-Array and Metabolic Data. Mathematical Modeling in Nutrition and Agriculture. 2007; Chapter 5: p 82-96. Ed. Mark Hanigan.