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In silico biology through “omics”

Nature Biotechnology volume 20, pages 649–650 (2002)Cite this article

A variety of high-throughput experimental technologies are helping to unravel the detailed molecular composition and complexity of living cells. Such technologies—genomic, transcriptomic, proteomic, and metabolomic—produce data in fundamentally different formats from previous approaches, and our ability to logically analyze them simultaneously remains a key challenge in the further development of systems biology. In silico models represent a way to meet the challenge of integrating diverse sets of “omics” data.

Genome-scale structural and functional networks representing the interaction of all their components are being reconstructed1,2, and we are now faced with the challenge of formulating genome-scale in silico models of network behavior. At this scale, such models should begin to help us unravel how biological properties arise from the large number of components from which cells are comprised. Traditional theory-based models of large-scale cellular processes are faced with fundamental difficulties. First, the intracellular chemical environment is complex and hard to define in terms needed for the formulation of equations that describe the physics of the intracellular milieu. Second, assuming that we had all the governing equations defined, we would have to find numerical values for all the parameters that appear in these equations. Third, even if we could overcome the first two challenges, we would face the fact that evolution changes the numerical values of kinetic constants over time. In addition, in an out-bred population, we could have a perfect in silico model for one individual, but it would not apply to other individuals in the population due to polymorphism in the genes and therefore non-identical kinetic parameters. Such time-dependency and diversity of parameter values are key distinguishing features between biological systems and physico-chemical systems. An alternative approach to theory-based models is the use of data-driven constraints-based models that lead to functionally integrated databases of specific organisms or cellular processes.

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  1. professor of bioengineering at the University of California and a co-founder of Genomatica, San Diego, 92093, CA

    Bernhard Palsson

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Palsson, B. In silico biology through “omics”. Nat Biotechnol 20, 649–650 (2002). https://doi.org/10.1038/nbt0702-649

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