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Chapter 1 - Introducing Computational Systems Biology
Book chapter

Chapter 1 - Introducing Computational Systems Biology

Andres Kriete and Roland Eils
Computational Systems Biology, pp 1-14
2006
DOI: https://doi.org/10.1016/B978-012088786-6/50020-4

Abstract

The aggregate of statistical bioinformatics tools for collecting, storing, retrieving, and analyzing complex biological data has repeatedly proven useful in biological decision support and discovery, a notable hallmark being the deciphering of the human genome as led by the Genome International Sequencing Consortium. An envisioned digital blueprint of complex diseases but also of biological development, aging, and immunity should not solely consist of descriptive charts as widely found in scientific literature or in genomic databases. They should instead be based on rigorously quantitative data-based mathematical models of metabolic pathways, signal transduction cascades, cell-to-cell communication, and so on. Data is not only generated by genomics sequencing and structural proteomics, but increasingly by image-based spatial and time-lapse microscopic observations. Modeling approaches can be divided into bottom-up and top-down. In the bottom-up approach, we use a reductionist approach and study basic components, and integrate these to find relevant patterns and functions, such as pathways. Time-discrete dynamic systems models have long been used in biology. Biologic computer simulations require careful consideration as to the level of details necessary for a representative model, because unnecessary details will lead to models so complex that detailed numerical study would become highly cumbersome or impossible.

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