Algorithms in nature: the convergence of systems biology and computational thinking

Navlakha, S., Bar-Joseph, Z. (November 2011) Algorithms in nature: the convergence of systems biology and computational thinking. Mol Syst Biol, 7. p. 546. ISSN 1744-4292

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URL: https://www.ncbi.nlm.nih.gov/pubmed/22068329
DOI: 10.1038/msb.2011.78

Abstract

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.

Item Type: Paper
Subjects: organism description > animal > insect > Drosophila
bioinformatics > computational biology > algorithms
bioinformatics > computational biology
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene network
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 8 November 2011
Date Deposited: 08 Nov 2019 14:16
Last Modified: 08 Nov 2019 14:16
PMCID: PMC3261700
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38688

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