Does It Compute?

Abstract

Computational Biology in-article table of contents A discussion of computational biology has to start with a pioneer of the field, Alan Turing, especially in this centennial year of his birth. He introduced us to the digital computer and proposed that much biology could be described by mathematical equations—the number of spirals in a sunflower is a Fibonacci number and pattern formation in animal skins can be described by a reaction diffusion model. Turing lacked the data and the computing power to substantiate his models. Today, the availability of vast quantities of new data, together with striking advances in computing power, is promising to give us new insights into the mechanisms of life. This special section, together with related content in Science Signaling and Science Careers, highlights recent advances and outstanding challenges. Models that simulate biological processes can be characterized both by their level of biological detail and by their mathematical complexity. Mogilner et al. (p. [175][1]) describe these different classes of computer models, using the example of cell polarity mechanisms to show that diverse models are required to fit different experimental studies in an iterative loop of modeling and experimentation. In a News Focus, Pennisi (p. [172][2]) illustrates this loop, describing how researchers have combined modeling and experimentation to understand how lizards, turtles, and other animals cope with heat stress and potentially global warming (videos of some of these models can be found at ). ![Figure][3] CREDIT: SEITA MIYAZAWA/OSAKA UNIVERSITY Advances in sequencing technology have resulted in a barrage of genomic information. Zerbino et al. (p. [179][4]) review the development of computational methods and algorithms to efficiently analyze these data, from the initial genome reconstruction to their use in comparing individuals and organisms, reconstructing phylogenies, and tying genotype to phenotype. Munsky et al. (p. [183][5]) note that phenotypic variation can occur even in genetically identical cells. They discuss stochastic gene expression as the likely source of this variability and describe how the analysis of variability can give insight into the mechanisms of gene regulation. At the level of morphogenesis that so fascinated Turing, Morelli et al. (p. [187][6]) describe how a combination of theory and experiment is being used to investigate developmental processes. They describe how patterning can arise from molecular gradients, from coupled biological oscillations, or from mechanical deformations of cells and tissues. Underlying all of this analysis is computer code, and in a Policy Forum, Morin et al. (p. [159][7]) call for this code to be made widely available and suggest how this might be implemented. Turing not only sought mathematical rules that govern biology, as described in Perspectives by Hodges (p. [163][8]) and French (p. [164][9]); he also questioned whether there were fundamental differences between how machines and biological organisms compute. There is much more work to be done in exploring such questions, and we look forward to the many new insights that will be gained along the way. [1]: /lookup/volpage/336/175?iss=6078 [2]: /lookup/volpage/336/172?iss=6078 [3]: pending:yes [4]: /lookup/volpage/336/179?iss=6078 [5]: /lookup/volpage/336/183?iss=6078 [6]: /lookup/doi/10.1126/science.1215478 [7]: /lookup/doi/10.1126/science.1218263 [8]: /lookup/volpage/336/163?iss=6078 [9]: /lookup/doi/10.1126/science.1218350