In a world where biology and physics were once distant cousins, Philip Nelson's Physical Models of Living Systems builds a bridge, showing how the laws of physics illuminate the wondrous chaos of life.
Imagine trying to understand a city's traffic patterns by studying a single car. For centuries, this was the challenge biologists faced: how to decipher the intricate dance of life from the seemingly chaotic motion of its microscopic parts. The breakthrough came when scientists began wielding the powerful tools of physics and mathematics to model living systems. At the forefront of this revolution is Philip Nelson's groundbreaking work, which teaches us that to truly understand life, we must learn to speak the language of physical models.
For decades, biology and physics existed in separate spheres. Biology was a science of cataloging and qualitative description, while physics was a science of mathematical laws and quantitative prediction. Physical Models of Living Systems shatters this divide, advocating for a unified approach where biological problems are understood through the lens of physical principles and mathematical modeling 1 2 .
This isn't just a slight shift in perspective; it's a fundamental change in methodology. As physicist Eva-Maria Collins notes in Physics Today, Nelson's book is "particularly compelling for its smooth integration of biological experiments, physical models, and computational exercises" 2 . This triad—experiment, theory, and simulation—forms the core of a new scientific skill set, essential for anyone hoping to unravel the complexities of modern biology.
One of the most compelling case studies in this new science is the modeling of HIV dynamics within the human body 1 . Before mathematical models were applied, the battle against the virus was understood in purely biological terms. Researchers knew the virus attacked immune cells, but the precise dynamics of the infection were a black box.
Nelson's textbook introduces this problem to showcase the power of simple models. Scientists like Alan Perelson developed a set of ordinary differential equations that described the interaction between the virus, infected cells, and the immune system with stunning simplicity .
The model didn't need to include every single biological detail; instead, it focused on the essential variables and their interactions.
When researchers compared the model's predictions to patient data, they achieved a breakthrough. The model revealed the shockingly rapid turnover of the virus—billions of new viral particles produced and cleared every day .
This wasn't a slow, simmering infection but a violent, high-speed war happening inside the body. This critical insight, born from a physical model, directly informed the development of antiretroviral therapy.
If the HIV model shows the power of deterministic equations, the classic Luria-Delbrück experiment demonstrates how embracing randomness can answer one of biology's most fundamental questions: how does evolution work?
In 1943, Salvador Luria and Max Delbrück set out to settle a heated debate about the nature of genetic mutation in bacteria. Do mutations occur randomly, as a matter of chance, or are they directed by the environment?
The elegance of their experiment lay in its simplicity. They designed two distinct scenarios :
The data showed an enormous variance in the number of resistant colonies. Most dishes had few or none, but a few had a very large number—the tell-tale "jackpots" predicted by the random mutation hypothesis.
| Culture Number | Resistant Colonies |
|---|---|
| 1 | 0 |
| 2 | 1 |
| 3 | 28 |
| 4 | 0 |
| 5 | 3 |
| 6 | 0 |
| 7 | 105 |
| 8 | 0 |
| 9 | 1 |
| 10 | 0 |
| Hypothesis | Predicted Pattern | Conclusion |
|---|---|---|
| Random Mutation | High variance; rare "jackpot" cultures | Supported |
| Acquired Inheritance | Low variance; similar counts | Rejected |
This was a monumental achievement. As Nelson explains, "The authors laid out two competing hypotheses, and sought to generate testable quantitative predictions from them. But unusually for the time, the predictions were probabilistic in character" . They had used the statistics of randomness to prove that evolution acts through random mutation and natural selection, a discovery that earned them the 1969 Nobel Prize in Physiology or Medicine.
So, what does it take to start building these physical models? Nelson's approach equips students with a versatile toolkit that blends classic physics with modern computational power 1 4 .
Checks model consistency and reveals fundamental relationships.
Example: Verifying units in equations modeling virus concentration.Describes how system variables change continuously.
Example: Modeling population dynamics during infection.Models random processes to capture biological noise.
Example: Simulating random gene expression fluctuations.Extracts parameters and tests hypotheses from data.
Example: Determining bacterial mutation rates.Visualizes dynamics of systems with feedback.
Example: Analyzing stable states of genetic switches.Implementation using Python, MATLAB, or similar platforms.
Essential for creating, testing, and exploring models.This toolkit is not just theoretical. A central theme of Nelson's book is that "readers will acquire... computer programming using a general-purpose platform like MATLAB or Python, with short codes written from scratch" 1 . This hands-on skill allows students to move beyond black-box software and truly create, test, and explore their own models of the living world.
The impact of this physical approach to biology extends far beyond the classroom. It is driving some of the most exciting research on the planet.
For instance, at the University of Chicago, physicist Arvind Murugan and his team are exploring what they call "neuroscience without neurons" 5 . They ask: what if molecular systems can think and learn using their innate physics, without a separate "brain"?
They have shown that physical systems can use phase boundaries—like the edge between ice and water—to make complex decisions, acting like a simple neural network made of matter itself 5 .
This is the future that Philip Nelson's work prepares us for—a future where the boundary between the physical and biological sciences dissolves. By building physical models of living systems, we are not reducing life to mere machinery. On the contrary, we are uncovering a deeper, more mathematical beauty within it. We are learning that the same principles that govern a swinging pendulum can help us understand the rhythm of a cellular clock, and that the random dance of molecules holds the key to evolution itself. As Nelson inspires his readers, the goal is to see how "physical science and life science illuminate each other" , guiding us toward a brighter, more quantitative understanding of the living world.