DEVELOPMENT OF CONTEXT-SPECIFIC INTEGRATED BIOLOGICAL NETWORK MODELS WITH APPLICATIONS TO STRAIN ENGINEERING

Biological-system-based production of chemicals and fuels (Eg: ethanol, bio-diesel, bio-nylon, etc.) has lesser or no impact on the environment than their conventional, chemical processes But, these biofactories are not evolved to produce industrially important chemicals yet. Hence, the productivity of the desired chemical is low in an isolated biological system. Metabolic engineering modifies the metabolic network (network of biochemical reactions) by blocking or appending a few reactions in a static or dynamic manner to increase the chemical production. We have to determine which reactions to target? Or what pathways to modify? before performing metabolic engineering. Mathematical models of the biosystems enable us to analyze these biosystems in different conditions, like what happens if a reaction is removed or added in an in-silico manner. These models are mathematical equations that capture bio-entities as symbolic variables and their interactions as mathematical operations among the symbolic variables. In this work, we propose to model the biosystem, Kluyveromyces lactis, as an integrated model to design new strains for chemical production using our group’s advanced algorithms like MoVE, mcPecaso, cRegMCS.

Faculty Supervisor:

Radhakrishnan Mahadevan

Student:

Partner:

Indian Institute of Technology Madras

Discipline:

Engineering

Sector:

Biotechnology; Green/Alternative Energy; Life Sciences (not health)

University:

University of Toronto

Program: