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Biological Systems from an Engineer’s Point of View

Reeves, Gregory T. and Fraser, Scott E. (2009) Biological Systems from an Engineer’s Point of View. PLoS Biology, 7 (1). e1000021 . ISSN 1544-9173. PMCID PMC2628404.

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Mathematical modeling of the processes that pattern embryonic development (often called biological pattern formation) has a long and rich history [1,2]. These models proposed sets of hypothetical interactions, which, upon analysis, were shown to be capable of generating patterns reminiscent of those seen in the biological world, such as stripes, spots, or graded properties. Pattern formation models typically demonstrated the sufficiency of given classes of mechanisms to create patterns that mimicked a particular biological pattern or interaction. In the best cases, the models were able to make testable predictions [3], permitting them to be experimentally challenged, to be revised, and to stimulate yet more experimental tests (see review in [4]). In many other cases, however, the impact of the modeling efforts was mitigated by limitations in computer power and biochemical data. In addition, perhaps the most limiting factor was the mindset of many modelers, using Occam’s razor arguments to make the proposed models as simple as possible, which often generated intriguing patterns, but those patterns lacked the robustness exhibited by the biological system. In hindsight, one could argue that a greater attention to engineering principles would have focused attention on these shortcomings, including potential failure modes, and would have led to more complex, but more robust, models. Thus, despite a few successful cases in which modeling and experimentation worked in concert, modeling fell out of vogue as a means to motivate decisive test experiments. The recent explosion of molecular genetic, genomic, and proteomic data—as well as of quantitative imaging studies of biological tissues—has changed matters dramatically, replacing a previous dearth of molecular details with a wealth of data that are difficult to fully comprehend. This flood of new data has been accompanied by a new influx of physical scientists into biology, including engineers, physicists, and applied mathematicians [5–7]. These individuals bring with them the mindset, methodologies, and mathematical toolboxes common to their own fields, which are proving to be appropriate for analysis of biological systems. However, due to inherent complexity, biological systems seem to be like nothing previously encountered in the physical sciences. Thus, biological systems offer cutting edge problems for most scientific and engineering-related disciplines. It is therefore no wonder that there might seem to be a “bandwagon” of new biology-related research programs in departments that have traditionally focused on nonliving systems. Modeling biological interactions as dynamical systems (i.e., systems of variables changing in time) allows investigation of systems-level topics such as the robustness of patterning mechanisms, the role of feedback, and the self-regulation of size. The use of tools from engineering and applied mathematics, such as sensitivity analysis and control theory, is becoming more commonplace in biology. In addition to giving biologists some new terminology for describing their systems, such analyses are extremely useful in pointing to missing data and in testing the validity of a proposed mechanism. A paper in this issue of PLoS Biology clearly and honestly applies analytical tools to the authors’ research and obtains insights that would have been difficult if not impossible by other means [8].

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Fraser, Scott E.0000-0002-5377-0223
Additional Information:© 2009 Reeves and Fraser. Published online 2009 January 20. The authors would like to thank Tuomas Brock for helpful suggestions on the manuscript. GTR is a fellow of The Jane Coffin Childs Memorial Fund for Medical Research and has been aided by a grant from The Jane Coffin Childs Memorial Fund for Medical Research. SEF was partially supported by the National Institutes of Health Centers of Excellence in Genomics Science grant (HG004071).
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Jane Coffin Childs Memorial Fund for Medical ResearchUNSPECIFIED
Issue or Number:1
PubMed Central ID:PMC2628404
Record Number:CaltechAUTHORS:REEplosb09
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13610
Deposited By: Jason Perez
Deposited On:04 Mar 2009 19:36
Last Modified:03 Oct 2019 00:41

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