Algorithms in Nature

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Computer science and biology have shared a long history together. For many years, computer scientists have designed algorithms to process and analyze biological data (e.g. microarrays), and likewise, biologists have discovered several operating principles that have inspired new optimization methods (e.g. neural networks). Recently, these two directions have been converging based on the view that biological processes are inherentlyalgorithms that nature has designed to solve computational problems.

This website documents new studies that have taken a joint computational-biological approach to study the algorithmic properties of biological processes across all levels of life (molecular, cellular, and organism).

 

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

Received 17 May 2011; Accepted 7 September 2011; Published online 8 November 2011

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.

 

 

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