title: Algorithms in nature: the convergence of systems biology and computational thinking.
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.
authors: Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors: Saket
authors: S
authors: Navlakha
authors: Ziv
authors: Z
authors: Bar-Joseph
mesh_terms: D000465:Algorithms; D000818:Animals; D001686:Biological Phenomena; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical; D009434:Neural Pathways; D011597:Psychomotor Performance; D049490:Systems Biology / Q000379:methods*