We Need Theoretical Physics Approaches to Study Living Systems

An editorial titled “We Need Theoretical Physics Approaches to Study Living Systems,” which was published recently in the journal Physical Biology (Volume 10, Article number 040201), has resonated with me. Krastan Blagoev, Kamal Shukla and Herbert Levine discuss the importance of using simply physical models to understand complicated biological problems. The debate about how much detail to include in mathematical models is a constant source of tension between physicists and biologists, and even between physicists and biomedical engineers. I agree with the editorial’s authors: simple models are vitally important. Biologists (and even more so, medical doctors) put great emphasis on the complexity of their systems. But the value of a simple model is that it highlights the fundamental behavior of a system that is often not obvious from experiments. If we build realistic models including all the complexity, they will be just as difficult to understand as are the experiments themselves. Blagoev, Shukla and Levine say much the same (my italics).

In this editorial, we propose that theoretical physics can play an essential role in making sense of living matter. When faced with a highly complex system, a physicist builds simplified models. Quoting Philip W Anderson’s Nobel prize address, “the art of model-building is the exclusion of real but irrelevant parts of the problem and entails hazards for the builder and the reader. The builder may leave out something genuinely relevant and the reader, armed with too sophisticated an experimental probe, may take literally a schematized model. Very often such a simplified model throws more light on the real working of nature... ” In his formulation, the job of a theorist is to get at the crux of the system by ignoring details and yet to find a testable consequence of the resulting simple picture. This is rather different than the predilection of the applied mathematician who wants to include all the known details in the hope of a quantitative simulacrum of reality. These efforts may be practically useful, but do not usually lead to increased understanding.

In my own research, the best example of simple model building is the prediction of adjacent regions of depolarization and hyperpolarization during electrical stimulation of the heart. Nestor Sepulveda, John Wikswo, and I used the “bidomain model,” which accounts for essential properties of cardiac tissue such as the tissue anisotropy and the relative electrical conductivity of the intracellular and extracellular spaces (Biophysical Journal, Volume 55, Pages 987–999, 1989; I have discussed this study in this blog before). Yet, this model was an enormous simplification. We ignored the opening and closing of ion channels, the membrane capacitance, the curvature of the myocardial fibers, the cellular structure of the tissue, the details of the electrode-tissue interface, the three-dimensional volume of the tissue, and much more. Nevertheless, the model made a nonintuitive qualitative prediction that was subsequently confirmed by experiments. I think the reason this research has made an impact (over 200 citations to the paper so far) is that we were able to strip our model of all the unnecessary details except those key ones underlying the qualitative behavior. The gist of this idea can be found in a quote usually attributed to Einstein: Everything should be made as simple as possible, but no simpler. I must admit, sometimes it pays to be lucky when deciding which features of a model to keep and which to throw out. But it is not all luck; model building is a skill that needs to be learned.

The editorial continues (again, my italics)

A leading biologist once remarked to one of us that a calculation of in vivo cytoskeletal dynamics that did not take into account the fact that the particular cell in question had more than ten isoforms of actin could not possibly be correct. We need to counter that any calculation which takes into account all these isoforms is overwhelmingly likely to be vastly under-constrained and ultimately not useful. Adding more details can often bring us further from reality. Of course, the challenge for models is then falsification, i.e., finding robust predictions which can be directly tested experimentally.

How does one learn and practice model building? One place to start—regular readers of this blog will have already guessed my answer—is the 4th edition of Intermediate Physics for Medicine and Biology. This book, and especially the homework problems at the end of each chapter, provide plenty of examples of model building (for simple models applied to the study of the heart, see Chapter 10, Problems 37–40). I think that this aspect of the book sets it apart from many others texts, which cover the biology in more detail.

Krastan Blagoev is the director of the Physics of Living Systems program at the National Science Foundation. According to the NSF website

The program “Physics of Living Systems” (PoLS) in the Physics Division at the National Science Foundation targets theoretical and experimental research exploring the most fundamental physical processes that living systems utilize to perform their functions in dynamic and diverse environments. The focus should be on understanding basic physical principles that underlie biological function. Proposals that use physics only as a tool to study biological questions are of low priority.

Because I might someday apply for a grant from the PoLS program, let me note that Dr. Blagoev is a gentleman and a scholar, who has done much to advance the application of physics to biology. To learn more about Blagoev, see the April 2008 issue of The Biological Physicist, the newsletter for the Division of Biological Physics of the American Physical Society. Shukla is the director for the “Biomolecular Dynamics, Structure and Function” program at NSF, which I am unlikely ever to seek funding from, so I’ll just say he is probably a good guy too. Levine is the Director of the Center for Theoretical Biological Physics at Rice University.