Box 6.3Functional Modules in Biology

An important theme in systems biology has been to look for functional modules that have been conserved and reused. The idea of breaking biological systems into small functional blocks has obvious appeal; the parts can be divided and conquered so that the most complex of machines become readily understood in terms of block diagrams or sets of subroutines. Clearly, some conserved modules exist such as the ribosome and the tricarboxylic acid cycle. One method to search for modules involves looking for higher-order structures or recurring sub-networks (often termed “motifs”) in metabolic or gene regulatory networks. Another approach mentioned earlier is clustering expression profiles to produce groups of genes that appear to be co-regulated that should ideally reveal the functional modules. However, this assumption does not appear to generalize to all functional groups under all conditions, as some functional groups show well-correlated expression profiles whereas others do not. The low correlation of genes observed within some functional groups has been attributed to the fact that some of these genes belong to multiple functional classes. In another analysis in E. coli, 99 cases were found where one reaction existed in multiple pathways in EcoCyc. These observations suggest potential pitfalls with anticipating too much functional modularity in terms of biology being neatly partitioned into non-overlapping modules. Moreover, the tissue- or species-specific differences mentioned earlier may prevent simplistic transfer of modules from one biological system to another. It remains to be seen if biology is as modular as the system biologist might like it to be.

Biological modules may turn out be more interconnected and overlapping than independent in many systems. In addition, the experiences with pathway reconstruction suggest that the combinations of data source produce a more accurate if not more complete characterization of the system under study. These observations point to an eventual need to develop large-scale, predictive models based on a multitude of data sources. For example, metabolic models may combine data from many sources into a quantitative set of equations that can make predictions amenable to experimental verification. In another system, cardiac models can bridge data at multiple levels (i.e. molecular, cellular, organ, etc.) and their corresponding characteristic timescales. In this system, modeling efforts at the single-cell level in the heart suggested a mechanism of increased contraction force that was later confirmed in experimental studies of whole heart.

SOURCE: Reprinted by permission from J.J. Rice and G. Stolovitzky, “Making the Most of It: Pathway Reconstruction and Integrative Simulation Using the Data at Hand,” Biosilico 2(2):70-77. Copyright 2004 Elsevier.

From: 6, A Computational and Engineering View of Biology

Cover of Catalyzing Inquiry at the Interface of Computing and Biology
Catalyzing Inquiry at the Interface of Computing and Biology.
National Research Council (US) Committee on Frontiers at the Interface of Computing and Biology; Wooley JC, Lin HS, editors.
Washington (DC): National Academies Press (US); 2005.
Copyright © 2005, National Academy of Sciences.

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.