Functional models for large-scale gene regulation networks: realism and fiction

摘要

High-throughput experiments are shedding light on the topology of largeregulatory networks and at the same time their functional states, namely thestates of activation of the nodes (for example transcript or protein levels) indifferent conditions, times, environments. We now possess a certain amount ofinformation about these two levels of description, stored in libraries,databases and ontologies. A current challenge is to bridge the gap betweentopology and function, i.e. developing quantitative models aimed atcharacterizing the expression patterns of large sets of genes. However,approaches that work well for small networks become impossible to master atlarge scales, mainly because parameters proliferate. In this review we discussthe state of the art of large-scale functional network models, addressing theissue of what can be considered as realistic and what the main limitations maybe. We also show some directions for future work, trying to set the goals thatfuture models should try to achieve. Finally, we will emphasize the possiblebenefits in the understanding of biological mechanisms underlying complexmultifactorial diseases, and in the development of novel strategies for thedescription and the treatment of such pathologies.