JCB Home > 2015 Archive > 28 September > Gonzalez-Gaitan and Roux 210 (7): 1041
Published September 28, 2015 // JCB vol. 210 no. 7:1041-1045
The Rockefeller University Press, doi: 10.1083/jcb.201504025
© 2015 Gonzalez-Gaitan and Roux
Feature
When cell biology meets theory
Marcos Gonzalez-Gaitan 1,2 and Aurélien Roux 1,2
1Biochemistry Department, University of Geneva, CH-1211 Geneva, Switzerland
2Swiss National Centre for Competence in Research Programme Chemical Biology, CH-1211 Geneva, Switzerland
Correspondence to Aurélien Roux: aurelien.roux{at}unige.ch
Cell biologists now have tools and knowledge to generate useful quantitative data. But how can we make sense of these data, and are we measuring the correct parameters? Moreover, how can we test hypotheses quantitatively? To answer these questions, the theory of physics is required and is essential to the future of quantitative cell biology.
The study of complex biological systems requires a strong effort to give a detailed description of the components and of their interactions. For the past decades, the task of dissecting cellular mechanisms to identify proteins, their functions, and their partners has been the focus of cell biologists, with great success. For processes such as membrane trafficking or cell motility, we now have a close to exhaustive list of proteins involved, with more or less an idea of their function, localization, and partners. But do we really understand how the cell machinery works? Have we mastered the essential properties and control parameters to a point that would allow us to tune the cell system in a way that we determine? In some cases, yes, but this is mostly limited to a few concrete examples.
Part of the problem is that our description of cellular processes is poorly quantitative. For example, to be able to “tune” the cell, we would need not only to know which factors activate a signaling pathway, but to have an idea of the dose–response curve. And in order to understand which components are essential to establish such dependence, we would need to have an idea of the binding constants of all proteins in the pathway (phosphatase rates, kinase activity, etc.), a fairly unrealistic task when applied to all cellular processes. Does this mean we should not think of measuring these parameters? On the contrary, to understand how cells behave and how they react to their environment, we must be able to measure them. But what should be measured? Does every interaction, every activity, need to be measured?
Physicists have faced the same problem; i.e., wanting a quantitative understanding of matter in order to use it in a constructive way. However, as the nature of molecules and atoms was not known in the early 19th century, engineers and scientists used a macroscopic approach, measuring the correlative curve between macroscopic parameters (heat, temperature, dilatation, force, work, etc.). From these experimental data, they tried to determine the mathematical functions linking these parameters together, and established the classical laws of thermodynamics with almost no microscopic understanding of matter. Once molecules and atoms had been discovered, and their interactions began to be explored, physicists faced another question: how do the microscopic structure of matter and its interactions lead to macroscopic thermodynamics laws? In a first approach, physicists believed that the only way to understand this issue was to measure the position and the speed of each molecule/atom and their interactions in the system. They soon realized that this was experimentally impossible, and it still is today. In fact, it turned out that theoretical tools that capitalize on statistical methods allowed physicists to describe accurately the behavior of gases, metals, and other material by measuring a few properties of their particles and then describing statistically the average behavior of the particles. The tools developed by statistical physics were able to predict emerging macroscopic properties (such as the expansion of a gas and electricity) from a small set of microscopic properties. The purpose of this article is to present how the same tools could play an essential role in understanding quantitatively how a cell works.
Why theory for cell biology?
We cell biologists value and have mastered a reductionist approach: the cell is such a complex system that understanding can only emerge from breaking its mechanisms into subparts and describing each of them in detail. In contrast, the physicist’s strategy has two approaches: the first one is disregarding microscopic details, and focusing only on the macroscopic properties; and the second, bottom-up, approach is neglecting many microscopic details in order to focus on only some relevant interactions, then trying to explain the macroscopic properties by statistical analysis. Both methods have proven to be very useful for understanding biological systems. We will consider a few examples.
Theory can picture the behavior of cells without having any details about their internal regulation. A beautiful example is given in the work of Pascal Martin with Frank Jülicher (Hudspeth, 2008; Jülicher et al., 2009). Martin studies oscillations of the cilia of hair cells, which oscillate when sounds vibrate in the ear (Martin and Hudspeth, 1999). He observed spontaneous oscillations of the cilia in the absence of sound, and also measured a strong amplification of the oscillations triggered mechanically by attaching a vibrating glass fiber to the cilia (see Fig. 1). The amplification is much stronger for an extremely weak mechanical trigger, and absent when the fiber vibrates strongly. Theory explains these properties (Camalet et al., 2000): the hair cell is close to a Hopf bifurcation, a phenomenon in oscillatory dynamics that describes a transition from a nonoscillatory state to a state where the system oscillates spontaneously. If the system is in a critical state close to the bifurcation, it does not oscillate spontaneously, but a very small stimulus will make it oscillate with a very strong response by crossing the bifurcation into the oscillatory mode (Martin et al., 2001). It also explains why cells in slightly nonoptimal conditions (small changes in ionic concentration, for example) will start to oscillate spontaneously as they cross the bifurcation. It may sound very theoretical, but it is not; this finding explains facts known to almost all of us. It explains why we can hear both very quiet and very loud sounds, because the ear is a nonlinear amplifier. It also explains why you hear a buzz after a loud concert, or why, in pathological tinnitus conditions, patients hear a noise (in fact generated by the ear) in a silent environment.
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