University of Toronto, Faculty of Applied Science & Engineering

Chemical Engineering & Applied Chemistry

R. Mahadevan

R Mahadevan

B.Tech. (Indian Institute of Technology), Ph.D. (Delaware)

Room: WB326
Tel.: 416-946-0996
Websites: and


American Institute of Chemical Engineering
American Chemical Society
American Society for Microbiology
Biophysical Society

Research Interests

Systems Analysis and Engineering of Biological Processes
Research Interests: modeling and analysis of metabolic and regulatory networks, bioinformatics and systems biology, engineering biological systems for applications in metabolic engineering, bioremediation, bioenergy and bioprocess optimization.

Recent advances in experimental and computational technologies have enabled the detailed characterization of biological systems. In particular, the molecular components of these systems including the list of genes, proteins they encode, and compounds that interact with these proteins can be determined. This availability of tools to analyze system-wide changes at the level of the genes, proteins, and metabolites has created significant opportunities to understand cellular functions, and to ultimately design processes in a systematic way for applications in industrial and medical biotechnology (e.g. metabolic engineering, bioprocess optimization and control). The research interests of our group involve the development and utilization of dynamic mathematical models of biological systems for improved design, optimization and control.

Genome-scale models of cellular processes
Although detailed models of cellular processes have been constructed in the past, research in this area has attained a new dimension in the last few years due to the development of novel high-throughput experimental techniques for both sensing and manipulating cellular processes at a molecular level. As an example, both steady state genome scale models and smaller dynamic models of metabolism of several industrially important organisms including Escherichia coli have been developed in the past. More recently, such models have been developed for a metal reducing bacteria (Geobacter sulfurreducens) with applications in bioelectricity and bioremediation and have been used to rationally engineer the metabolism for improved electricity generation. However, further research is required to extend such models of metabolism to represent the inherent dynamics of biological systems and to account for the increased complexity in multi-cellular organisms and microbial communities.

Optimization and control of biological processes
Several engineering disciplines (e.g., mechanical, electrical, & chemical) routinely use quantitative models for design and optimization of processes of interest. However, such rational approach to design and optimization has been possible in the life science only recently due to the lack of predictive large-scale models of biological processes in the past. Research activities in our group include the design of dynamic model-driven engineering strategies for biological process optimization and control across different length and time scales (i.e., from microscopic (intracellular) processes to macroscopic (bioreactor) processes). Applications can include metabolic engineering (e.g., increasing the rate of electrical current in microbial fuel cells, designing dynamic gene manipulation strategies for increased product yields), biomedical engineering (drug design and dosage), bioreactor control and optimization (designing optimal substrate and inducer feeding strategies), and bioremediation (determining the spatiotemporal substrate addition strategies to effectively stimulate microbial activity).

Selected Publications

Y.K. Oh, S. M. Park, B.O. Palsson, C. H. Schilling and R. Mahadevan*, “Iterative model development of Bacillus subtilis”, Journal of Biological Chemistry, 2007, 282(39):28791-9.

J.L. Hjersted, R. Mahadevan, and M. A. Henson, “Genome-Scale Analysis of Saccharomyces cerevisiae Metabolism and Ethanol Production in Fed-Batch Culture”, Biotechnology and Bioengineering, 2007, 97(5), 1190-204.

K.G. Gadkar, R. Mahadevan, and F. J. Doyle III, “Batch Control of Genetic Alterations for Optimal Metabolic Engineering”, Automatica, 2006 Oct; 42(10), 1723-1733.

R. Mahadevan*, Bond DR, Butler JE, Esteve-Nunez A, Coppi MV, Palsson BO, Schilling CH, Lovley DR. “Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling”, Appl Environ Microbiol., 2006 Feb;72(2):1558-68

I. Famili, R. Mahadevan, and B. O. Palsson, "k-Cone Analysis: Determining All Candidate Values for Kinetic Parameters on a Network-scale", Biophysical Journal, 2005, 88(3), 1616-25.

R. Mahadevan*, and B. O. Palsson, "Properties of Large-Scale Biochemical Networks: Structure vs. Function", Biophysical Journal, 2005, 88(1), L07-L09.

K. G. Gadkar, F. J Doyle III, J. S. Edwards, and R. Mahadevan*, "Design of Optimal Genetic Manipulation Strategies for Metabolic Engineering", Biotechnology and Bioengineering, 2005, 89(2), 243-251.

S. J. Wiback, R. Mahadevan, and B. O. Palsson, "Using Metabolic Flux Data to Further Constrain the Metabolic Solution Space and Predict Internal Flux Patterns: The Escherichia coli -Spectrum", Biotechnology and Bioengineering, 2004, 86(3), 317-331.

R. Mahadevan*, C. H. Schilling, " Effects of Alternate Optimal Solutions in Constraint-based Genome Scale Metabolic Models", Metabolic Engineering, 2003, 5:264-276.

R. Mahadevan and F. J. Doyle III, "Efficient On-line Optimization of a Recombinant Product in a Fed-batch Bioreactor ", Biotechnology Progress, 2003, 19(2), 639-646.

R. Mahadevan and F. J. Doyle III, "Efficient Optimization Approaches to Nonlinear Model Predictive Control", International Journal of Robust and Nonlinear Control, 2003, 13 (3-4): 309-329.

R. Mahadevan, J. S. Edwards, and F. J. Doyle III, "Dynamic Flux Balance Analysis Approaches", Biophysical Journal, 2002, 83, 1331-1340.

(*: Corresponding Author)

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