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Understanding biological complexity: lessons from the past

The UCLA Cardiovascular Research Laboratory, and Departments of Medicine (Cardiology), Physiology and Physiological Science, UCLA School of Medicine, Los Angeles, California, USA

¹Correspondence: UCLA Cardiovascular Research Laboratory, 3645 MRL Bldg., UCLA School of Medicine, Los Angeles, CA 90095-1760, USA. E-mail: jweiss{at}mednet.ucla.edu

Advances in molecular biology now permit complex biological systems to be tracked at an exquisite level of detail. The information flow is so great, however, that using intuition alone to draw connections is unrealistic. Thus, the need to integrate mathematical biology with experimental biology is greater than ever. To achieve this integration, obstacles that have traditionally prevented effective communication between theoreticians and experimentalists must be overcome, so that experimentalists learn the language of mathematics and dynamical modeling and theorists learn the language of biology. Fifty years ago Alan Hodgkin and Andrew Huxley published their quantitative model of the nerve action potential; in the same year, Alan Turing published his work on pattern formation in activator-inhibitor systems. These classic studies illustrate two ends of the spectrum in mathematical biology: the detailed model approach and the minimal model approach. When combined, they are highly synergistic in analyzing the mechanisms underlying the behavior of complex biological systems. Their effective integration will be essential for unraveling the physical basis of the mysteries of life.—Weiss, J. N., Qu, Z., Garfinkel, A. Understanding biological complexity: lessons from the past.

Key Words: modeling • computational biology • pattern formation • emergent properties • nonlinear dynamics

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