Research Groups

Integrative Biology: Systems Biology for Biofuel

Research Interests and Description

Group Leader: Shireesh Srivastava, PhD

Group Members

Research Interests

Systems biology, Metabolic engineering, Flux Balance Analysis, Metabolic flux analysis, Cyanobacterial biotechnology, Metabolic labeling studies, Bioprocess Technology

Description of Research

The Group applies different approaches related to metabolic systems biology in order to (i) Gain systems level insights about the biological processes, and (ii) Identify non-obvious metabolic engineering targets. The approaches are being employed in a species-independent manner.
Metabolic labeling studies, employing stable isotopes, provide other useful information such as the precursor contributions to a product. Targeted metabolomics analyses provide information on variations of levels of major metabolites and, coupled with labeling studies, can provide the rates through various pathways. The formal modeling analysis of labeling data is termed metabolic flux analysis (MFA), which provides a snapshot of actual (experimental) flux through the major central metabolic pathways. The fluxes measured using MFA are used to constrain the flux solution space of the FBA. Advanced FBA-based methods are employed to identify effective gene targets for metabolite overproduction. These are then implemented through the application of traditional genetic engineering approaches.
To create a successful bioprocess, genetic engineering would need to be supplemented with further adaptation and optimization of bioprocess parameters. These form the last, but very important component of our overall approach to improve bioprocesses.

Biomass composition analysis includes measurements of major macromolecules such as protein, RNA, DNA, lipids, polysaccharides, vitamins and cofactors. Comparative compositional analyses could provide useful information on metabolic differences of bacteria, cyanobacteria and algae under different conditions. Reconstruction of Genome-Scale Metabolic Models GSMM is a collection of all metabolic reactions occurring inside a microbe that can be used to investigate the metabolism of the cell in a few seconds compared to time-consuming and costly experimental techniques.  Flux Balance Analysis A mathematical approach based on the principle of linear programming used to analyze the flow of metabolites inside the cell assuming intracellular steady state. We apply FBA approach to GSM to analyze flux distributions in different physiological conditions to gain insight into the microbial metabolism. Metabolic Labeling Studies followed by mass spectrometric analyses provides a safer and powerful method to answer important biochemical questions, e.g. novel quantitative information on precursor contributions for synthesis of biopolymers. Studies involve culturing cells in the presence of a suitably labeled substrate and measurement of the mass isotopomer distribution (MID) of the target metabolite/molecule.  Targeted Metabolomics Measurement levels can provide important insights into the metabolic changes associated with cellular responses and can help identify interventions to channel the metabolic flux through desired pathways.  Metabolic Flux Analysis MFA requires accurate measurement of label incorporation in cellular metabolites crucial in calculating the fluxes by MFA software. Mass spec has emerged as the technique of choice to measure the MIDs of intracellular metabolites and amino acids. The information generated is fit to a metabolic model in a mathematical framework that yields information on the unmeasured fluxes.  Target Identification Genetic engineering targets (Knockout/overexpression) for production or enhanced production of a metabolite can be identified computationally using GSMM by applying advanced FBA-based methods, e.g.  E. coli, Geobacillus thermoglucosidasius and cyanobacteria GSMMs to identify genetic interventions required for enhanced biofuel production.  Genetic Engineering Targets identified by the systems biological analyses need to be knocked out/overexpressed and their effects studied. Adaptation studies have shown that bacterial and yeast cells can significantly improve fermentation characteristics in industrial media. We characterize the genetic and proteomic changes associated with improved productivity.  Bioprocess Optimization Needed to reach economically viable product titers and productivities, this work optimizes fermentation parameters of heterotrophs and scale-up of the developed strains, and optimization of growth parameters of marine cyanobacteria: nutrients levels, light intensity, temperature, CO2 concentrations and photoperiods. The effects of these parameters on growth, biomass composition and productivity are measured.

Recent Publications

Shah, A.R., Ahmad, A., Srivastava, S., Jaffar Ali, B.M. 2017. Reconstruction and analysis of a genome-scale metabolic model of Nannochloropsis gaditana. Algal Res 26, 354–364

Ahmad, A., Hartman, H., Krishnakumar, S., Fell, D.A., Poolman, M.G., Srivastava, S. 2017. A Genome Scale Model of Geobacillus thermoglucosidasius (C56-YS93) reveals its biotechnological potential on rice straw hydrolysate. J Biotechnol 251, 30-37 PubMed link

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

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

Kemper, M.F., Srivastava, S., King, M.T., Clarke, K., Veech, R.L., Pawlosky, R.J. 2015. An Ester of β-Hydroxybutyrate Regulates Cholesterol Biosynthesis in Rats and Cholesterol Biomarker in Humans. Lipids 50, 1185-93 PubMed link

Srivastava, S., Veech, R.L. 2013. A ketogenic diet increases brown adipose tissue mitochondrial proteins and UCP1 levels in mice. IUBMB Life 65, 58-66 Erratum in: IUBMB Life 66, 519 PubMed link


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