Now the ENG biomedical engineering professor and codirector of the Center for Biodynamics and a team including Timothy Gardner, an ENG biomedical engineering assistant professor, graduate student David Lorenz (ENG’05), and Diego di Bernardo of the Telethon Institute of Genetics and Medicine in Naples, Italy, have developed a simple, direct way to understand how genes and proteins interact to regulate processes within a cell, a tissue, or an organism. Using this new approach they are able to map biological networks with a minimum of experimental measurements.

Their new development provides a powerful tool to better understand complex biological processes and how they can malfunction. It is of particular interest and value for pharmaceutical research, since it can predict how pharmacological compounds will affect cell processes.

Called network identification by multiple regression (NIR), the approach is similar to that used to engineer large communication and control systems. In this systems engineering method, the researchers systematically perturb a cell with a genetic or a chemical stimulus, and measure how the changes impact mRNA or protein concentrations. They then apply computational algorithms to derive the strength and direction of all other regulatory interactions within the network, creating a map of the myriad gene and protein interactions that are possible.

In recent experiments, the researchers’ model correctly identified the major regulatory genes and their targets in a nine-gene subnetwork of the bacteria E coli, called the SOS pathway. They demonstrated that the algorithms were robust in the presence of biological noise, and are scalable to large networks.

This work was reported in the July 4, 2003 issue of the journal Science.