A forecast for large-scale, predictive biology: Lessons from meteorology

Markus W Covert; Taryn E Gillies; Takamasa Kudo; Eran Agmon

doi:10.1016/j.cels.2021.05.014

A forecast for large-scale, predictive biology: Lessons from meteorology

Cell Syst. 2021 Jun 16;12(6):488-496. doi: 10.1016/j.cels.2021.05.014.

Authors

Markus W Covert¹, Taryn E Gillies², Takamasa Kudo³, Eran Agmon²

Affiliations

¹ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. Electronic address: mcovert@stanford.edu.
² Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
³ Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA.

Abstract

Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.

Keywords: computational biology; systems biology; whole-cell modeling.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Meteorology*
Models, Biological*
Models, Theoretical
Systems Biology

Abstract

Publication types

MeSH terms

Grants and funding