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).

Please contact us to have your paper added to this list.

Reviews and Perspectives

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.

Computational thinking.
J. Wing. Communications of the ACM, 49:33-35, 2006.

Network Design and Analysis [see all papers]

The Regulation of Ant Colony Foraging Activity without Spatial Information
Prabhakar et al. PLoS Comp. Biol., 8(8) e100267009, 2012.

Poisson distribution of intervals between the arrival of successive returning foragers at the nest.
[CS+Bio: Discovered the 'Anternet': connections between how ants determine the number of foragers to send out of the nest and the transmission control protocol used on the Internet.]

Coordination and Consensus [see all papers]

Cell-cycle regulation of NOTCH signaling during C. elegans vulval development.
S. Nusser-Stein, A. Beyer, I. Rimann, M. Adamczyk, N. Piterman, A. Hajnal, J. Fisher. Nature-EMBO Molecular Systems Biology, Nature Sci. Reports, 8:618, 2012.

A model for bounded asynchrony during VPC differentiation.
[CS+Bio: Describe how bounded synchrony models of synchronization can be achieved through cell-cycle control of signal transduction; a possible global principle used during the development of multicellular organisms.]

Computer Vision and Neuroscience [see all papers]

Convergent acoustic field of view in echolocating bats.
L. Jakobsen, J.M. Ratcliffe, A. Surlykke. Nature, 2012.

Sonar beam width decreases as emitter (bat) size increases relative to wavelength.
[CS+Bio: Explore the relationship between bat size and echolocation call frequency; suggest that echolocation is a dynamic system that allows different species, regardless of their body size, to converge on optimal fields of view in response to habitat and task.]

Machine Learning [see all papers]

Smart swarms of bacteria-inspired agents with performance adaptable interactions.
A. Shklarsh et al. PLoS Computational Biology, 7:e1002177, 2011.

Simulation of interacting agents moving collectively towards a target.
[CS: Proposed a new collective navigation model based on adaptive interactions whose strengths are determined by immediate performance.]

Optimization [see all papers]

A Slime Mold Solver for Linear Programming Problems.
Johannson and Zou. Proc. of the 8th Computability in Europe (CIE) Conf., 344-354, 2012.
[CS: Showed how to encode general linear programming (LP) problems as instances of the distributed growth dynamics of slime mold; prove that the model converges to the optimal solutions of the LP.]

Saket Navlakha and Ziv Bar-Joseph, 2012.