Why Biology Needs Physics and Mathematics
The idea that biology is fundamentally separate from physics, and therefore less suited to mathematical modeling, is a misconception. I hear this a lot from Biologists actually. A physics domain encompasses not just spatial and temporal dimensions but also state spaces, configuration spaces, energy landscapes, and even complex theoretical spaces. These are the frameworks within which physical phenomena are modeled and analyzed. If a system can be measured or quantified, it falls within the scope of physics. Really as simple as that.
Biology is no exception. At the molecular and atomic levels, biological processes are governed by the same physical laws that apply to non-living systems. Consider the diffusion of molecules across a cell membrane, the kinetics of enzyme-substrate interactions, or the mechanics of protein folding. These processes, though complex, can be mathematically described. The real challenge is not in the applicability of mathematics but in developing the sophisticated models required to capture the intricacies of biological systems.
The resistance to applying mathematics to biology often comes from an under appreciation of the complexity that both fields can address. Biological systems are indeed complex, with non-linear dynamics and multi-scale interactions. But these are the types of problems that modern mathematics is well-equipped to handle. Physics has long dealt with complexity through tools like differential equations, network theory, and multi-scale modeling. These tools are now increasingly applied to biological questions.
As biology becomes more integrated with physics and mathematics, the lines between these fields are blurring. In areas like systems biology, genomics, biophysics, and computational biology, mathematical frameworks are essential. They help us understand the behavior of complex networks, the energetics of molecular interactions, and the dynamics of evolution.
The challenge now is refining our models to better capture the nuances of biological systems. Biological data is inherently multi-scale, spanning from molecular interactions to whole-organism behavior, from milliseconds to evolutionary timescales. To address this, we need new mathematical approaches that can handle the multi-grid, multi-fidelity nature of this data.
We are just beginning to explore the potential of this interdisciplinary convergence. The equations that govern biological systems are complex, but they are not beyond our reach. As our models become more sophisticated, we will uncover deeper insights into the principles that underlie life. The intersection of biology, physics, and mathematics is a frontier of discovery, one where traditional disciplinary boundaries are increasingly irrelevant. The future of science lies in this integrated approach, and we are only scratching the surface of what is possible.
