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The challenges and scope of theoretical biology

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dc.contributor.author Krakauer, David C. en
dc.contributor.author Collins, James P. en
dc.contributor.author Erwin, Douglas H. en
dc.contributor.author Flack, Jessica C. en
dc.contributor.author Fontana, Walter en
dc.contributor.author Laubichler, Manfred D. en
dc.contributor.author Prohaska, Sonja J. en
dc.contributor.author West, Geoffrey B. en
dc.contributor.author Stadler, Peter F. en
dc.date.accessioned 2011-04-21T18:26:04Z
dc.date.available 2011-04-21T18:26:04Z
dc.date.issued 2011
dc.identifier.citation Krakauer, David C., Collins, James P., Erwin, Douglas H., Flack, Jessica C., Fontana, Walter, Laubichler, Manfred D., Prohaska, Sonja J., West, Geoffrey B., and Stadler, Peter F. 2011. "<a href="https://repository.si.edu/handle/10088/15976">The challenges and scope of theoretical biology</a>." <em>Journal of theoretical biology</em>. 276 (1):269&ndash;276. <a href="https://doi.org/10.1016/j.jtbi.2011.01.051">https://doi.org/10.1016/j.jtbi.2011.01.051</a> en
dc.identifier.issn 0022-5193
dc.identifier.uri http://hdl.handle.net/10088/15976
dc.description.abstract Abstract Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. At the same time, model building has proven to be very successful when it comes to explaining and predicting the behavior of particular biological systems. In this respect biology resembles alternative model-rich frameworks, such as economics and engineering. In this paper we explore the prospects for general theories in biology, and suggest that these take inspiration not only from physics, but from the information sciences. Future theoretical biology is likely to represent an hybrid of parsimonious reasoning and algorithmic or rule based explanation. An open question is whether these new frameworks will remain transparent to human reason. In this context, we discuss the role of machine learning in the early stages of scientific discovery. We argue that evolutionary history is not only a source of uncertainty, but provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life. en
dc.relation.ispartof Journal of theoretical biology en
dc.title The challenges and scope of theoretical biology en
dc.type Journal Article en
dc.identifier.srbnumber 99575
dc.identifier.doi 10.1016/j.jtbi.2011.01.051
rft.jtitle Journal of theoretical biology
rft.volume 276
rft.issue 1
rft.spage 269
rft.epage 276
dc.description.SIUnit NH-Paleobiology en
dc.description.SIUnit NMNH en
dc.description.SIUnit Peer-reviewed en
dc.citation.spage 269
dc.citation.epage 276


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