Research Group Overview

Modeling and simulation of biochemical networks are invaluable tools used by researchers to investigate cellular behavior and help in the interpretation of data arising from quantitative experiments. Systems biology brings together modeling, simulation and quantitative experiments under one umbrella, allowing researchers to use the data of one of these approaches to repeatedly define the framework of the other approaches.

Dr. Pedro Mendes' Research Group focuses on computer simulation and the analysis of biochemical networks. Researchers in Dr. Mendes' laboratory, in collaboration with the Kummer group at EML Research, have developed the COPASI software to model and simulate biochemical networks. COPASI, which supports the Systems Biology Markup Language standard for systems biology software, enables researchers to investigate how a system is working by allowing them to construct biochemical models. One of COPASI's main features is the ability to automatically adjust model parameters to reproduce experimental results, which helps to justify the validity of the chosen model.

Bottom-up and top-down modeling are the two main methods used to create models of biochemical networks. Bottom-up modeling uses in vitro kinetic properties of enzymes in the network to form a model of the entire pathway. In collaboration with VBI faculty members Drs. Reinhard Laubenbacher and Vladimir Shulaev, the group is developing a method to characterize every isoenzyme of an important pathway in yeast that plays a major role in metabolism and the response of yeast to various stresses, particularly oxidative stress. This work will aid the group in building a comprehensive model of the pentose-phosphate pathway. Top-down modeling is based on reverse-engineering the pathway dynamics using measurements of the system's response to environmental or genetic perturbations. The group has continued work in this area, developing a database to store large-scale systems biology data, applying several algorithms for data analysis and reduction, and developing a method for inference of biochemical models from time course data.

The ability to organize and analyze data from large-scale technologies is another important task in the area of systems biology. Dr. Mendes' group provides the tools needed to manage this type of data and is continuing to develop DOME, a client-server system that manages and integrates transcriptomic, proteomic and metabolomic data.

Leader: Pedro Pedrosa Mendes