Systems Biology

Systems Biology: An Overview

Systems biology has spurred interest in thousands of researchers, some just starting their careers, others well established but interested in learning more about it.

The use of systematic genomic, proteomic and metabolomic technologies to construct models of complex biological systems and diseases is becoming increasingly common place. These endeavors, collectivelly known as Systems Biology, establish an approach by which to interrogate and iteratively refine our knowledge of the cell. In doing so, systems biology integrates knowledge from diverse biological components and data into models of the system as a whole.

The development of systems biology has been driven by a number of recent advances

in our ability to perturb biological systems systematically. Three technological

trends have emerged in this respect. First, techniques for genetic manipulation

have become more high-throughput, automated, and standardized by several orders

of magnitude. Second, the availability of complete genomic sequences has

stimulated the development of several systematic mutagenesis projects to complement

more traditional efforts involving random mutagenesis. Third, technologies

for disrupting genes in trans allow the application of genetic perturbations to a

wide range of eukaryotic organisms.

The development of systems biology has been driven by a number of recent advances in our ability to perturb biological systems systematically. Three technological trends have emerged in this respect. First, techniques for genetic manipulation have become more high-throughput, automated, and standardized by several orders of magnitude. Second, the availability of complete genomic sequences has stimulated the development of several systematic mutagenesis projects to complement more traditional efforts involving random mutagenesis. Third, technologies for disrupting genes in trans allow the application of genetic perturbations to a wide range of eukaryotic organisms.

Knowledge discovery is used extensively within bioinformatics for tasks such as the prediction of exon–intron and protein structure from sequence, and the inference of gene regulatory networks from expression profile. These methods typically use predictions based on heuristics, on statistical discriminators that often involve sophisticated approaches (such as hidden Markov models) and on other linguistic-based algorithms.

Although traditional bioinformatics has been used widely for genome analysis, simulation-based approaches have received little mainstream attention. This is now changing. Current experimental molecular biology is now producing the high-throughput quantitative data needed to support simulation- based research. Combined with rapid progress of genome and proteome projects, this is convincing increasing numbers of researchers of the importance of a system-level approach. At the same time, substantial advances in software and computational power have enabled the creation and analysis of reasonably realistic yet intricate biological models.

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Systems Biology Podcast