Using Biological Networks to Improve our Understanding of Infectious Diseases

Abstract

Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating themost common infectious diseases, inmany cases themechanismof action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often notwell understood. Since proteins do notwork in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes,which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.