In a paper co-authored by several SFI researchers, a group of scientists explores the prospects for general, predictive theories in biology akin to those in the physical sciences, and suggests that such theories take inspiration not only from physics, but also from the information sciences.
Read the paper (in press, published online February 10, 2011)
Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints, the authors write in the March 7 Journal of Theoretical Biology titled "The Challenges and Scope of Theoretical Biology." But 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, they say.
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.
"Future theoretical biology is likely to represent a hybrid of parsimonious reasoning and algorithmic or rule-based explanation," the authors write. "In this context, we discuss the role of machine learning in the early stages of scientific discovery."
They argue that "evolutionary history is not only a source of uncertainty, but also provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life."
The paper's authors include SFI Faculty Chair David Krakauer; James Collins (Arizona State University); SFI Professor Doug Erwin; SFI Professor Jessica Flack; SFI Science Board member and External Professor Walter Fontana; Manfred Laubichler (Arizona State University); Sonja Prohaska (University of Leipzig); SFI Distinguished Professor Geoffrey West; and SFI External Professor Peter Stadler.