Algorithms in Nature
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 inherently algorithms
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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Reviews and Perspectives
Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell
W.A. Lim et al. Cell 2013.
The ecology of collective behavior
D. Gordon. PLoS Computational Biology 2014
Theoretical distributed computing meets biology: a review
O. Feinerman and A. Korman. Proc. Intl. Conf. on Distributed Computing and Internet Technologies, LNCS 7753, 1-18, 2013.
Biology as reactivity.
J. Fisher, D. Harel, T.A. Henzinger. Communications of the ACM, 54(10):72-82, 2011.
Algorithms in nature: the convergence of systems biology and computational thinking.
S. Navlakha and Z. Bar-Joseph. Nature-EMBO Molecular Systems Biology, 7:546, 2011.
Algorithmic systems biology.
C. Priami. Communications of the ACM, 52:80-88, 2009.
Executable cell biology.
J. Fisher and T.A. Henzinger. Nature Biotechnology, 25(11):1239-1249, 2007.
J. Wing. Communications of the ACM, 49:33-35, 2006.
Design patterns from biology for distributed computing.
Babaoglu et al. ACM Trans. Auton. Adapt. Syst., 1:1, 26-66, 2006.
Network Design and Analysis [see all papers]
Coordination and Consensus [see all papers]
Computer Vision and Neuroscience [see all papers]
A universal strategy for visually guided landing.
Baird et al. PNAS, 2013.
[CS+Bio: Proposed a new visually-guided landing strategy used by bees that removes some common assumptions used within current engineering approaches. The simple bee-inspired strategy may be applicable to future flying robots.]
Saket Navlakha and Ziv Bar-Joseph, 2012.