Hybrid systems and LIFE methods for Mycobacterium tuberculosis
Project Information
matlabProject Status: Complete
Project Region: CAREERS
Submitted By: Sean McQuade
Project Email: piccoli@camden.rutgers.edu
Project Institution: Rutgers-Camden
Project Address: 303 Cooper St
Camden, New Jersey. 08102
Mentors: Galen Collier, Sean McQuade
Students: Christopher Denaro
Project Description
Mycobacterium tuberculosis infected one third of world population and current therapiesinvolve up to 4 antibiotics and 6 months of treatment. Using MTB gene expression data
From the main available drugs, KEGG and other databases for pathways and Linear-in-flux-expression (briefly LIFE) methodology, we aim to evaluate the potential effectiveness of drug combination therapies. We can do this by simulating the evolution of metabolites with the LIFE technique.
Another goal is to include hybrid methods to model metabolic pathway changes in MTB due to immune system, drug action, and other environmental conditions. Large scale metabolic and gene-regulation network dynamics will be used to assess drug treatment.
Project Information
matlabProject Status: Complete
Project Region: CAREERS
Submitted By: Sean McQuade
Project Email: piccoli@camden.rutgers.edu
Project Institution: Rutgers-Camden
Project Address: 303 Cooper St
Camden, New Jersey. 08102
Mentors: Galen Collier, Sean McQuade
Students: Christopher Denaro
Project Description
Mycobacterium tuberculosis infected one third of world population and current therapiesinvolve up to 4 antibiotics and 6 months of treatment. Using MTB gene expression data
From the main available drugs, KEGG and other databases for pathways and Linear-in-flux-expression (briefly LIFE) methodology, we aim to evaluate the potential effectiveness of drug combination therapies. We can do this by simulating the evolution of metabolites with the LIFE technique.
Another goal is to include hybrid methods to model metabolic pathway changes in MTB due to immune system, drug action, and other environmental conditions. Large scale metabolic and gene-regulation network dynamics will be used to assess drug treatment.