Three states – now that is exciting! And I’m not kidding. You are poised to understand critical timescales in non-equilibrium statistical mechanics.
Remember our goal is to crawl before we walk. We want to absolutely master the basics, so that complicated systems are less incomprehensible. While it is essential to understand the two-state system, it’s not enough. Using three states will deepen our appreciation of relaxation phenomena and timescales. And we will take the first step toward understanding non-equilibrium steady states.
To become stronger in theory, we do math. But we need to understand mathematical results in physical terms. The math and the conceptual picture must reinforce one another in our minds, or we’ll forget both. We also have to understand the assumptions underlying solvable models.
So it’s time for the solution to the previous problem … and new questions to understand that solution.
With this post I want to begin a series of exercises designed to grow your strength in theory pertinent to statistical biophysics – i.e., in math, physics, theoretical chemistry. The goal is to help you find a sweet spot where you push yourself a little bit, and regularly so you can continue to improve. Along the way, you’ll (re)learn critical statistical physics, which will help you understand, implement, and assess methods and findings more effectively. Of course, you’re in!
It’s my view that we must become statistical biophysicists. Why statistical? Because microscopic behaviors must be repeated zillions of times to create macroscopic effects. Can you help me shift the thinking in our community? See below for a collaboration opportunity.
Here’s a true story from a number of years ago. A postdoc in the group comes to me in frustration. He has built a cool “semi-atomistic” coarse-grained protein model that has generated disappointing results. An alpha helix that’s clearly resolved in the X-ray structure of his protein completely unravels. Disappointment. But playing the optimistic supervisor, I ask, “Are we sure you’re wrong? Could that helix be marginally stable?” Further digging revealed an isoform of the protein where the helix in question was not resolvable via X-ray. Relief! I was pretty pleased with myself, I must say.
But now I’m disappointed that I was pleased.
I don’t about you but I grew up on equilibrium statistical mechanics. The beauty of a partition function, an ensemble, the ability to understand thermodynamic principles from microscopic rules. I love that stuff.
But what if we want to understand biology? Is a partition function really the most important object? This Fall, I’m going to lecture on biophysics for an assortment of biology and biomedical engineering students for just a few weeks; and for the first time in my teaching career, I’m planning to omit a partition-function based description of molecular behavior. I’m just not convinced it’s important enough for an abbreviated set of lectures.
Such a beautiful thing, the PMF. The potential of mean force is a ‘free energy landscape’ – the energy-like-function whose Boltzmann factor exp[ -PMF(x) / kT ] gives the relative probability* for any coordinate (or coordinate set) x by integrating out (averaging over) all other coordinates. For example, x could be the angle between two domains in a protein or the distance of a ligand from a binding site.
The PMF’s basis in statistical mechanics is clear. When visualized, its basins and barriers cry out “Mechanism!’’ and kinetics are often inferred from the heights of these features.
Yet aside from the probability part of the preceding paragraph, the rest is largely speculative and subjective … and that’s assuming the PMF is well-sampled, which I highly doubt in most biomolecular cases of interest.
“Proteins don’t know biology” is one of those things I’m overly fond of saying. Fortunately, it’s true, and it gives quantitative folks a foot in the door of the magical world of biology. And it’s not only proteins that are ignorant of their role in the life of a cell, the same goes for DNA, RNA, lipids, etc. None of these molecules knows anything. They can only follow physical laws.
Is this just a physicist’s arrogance along the lines of, “Chemistry is just a bunch of special cases, uninteresting consequences of quantum mechanics”? I hope not. To the contrary, you should try to see that cells employ basic physics, but of a different type than what we learned (most of us, anyway) in our physical sciences curricula. This cell biophysics is fascinating, not highly mathematical, and offers a way of understanding numerous phenomena in the cell, which are all ‘special cases’ … but special cases of what?
You’re a quantitative person and you want to learn biology. My friend, you are in a difficult situation. If you really want to learn how biology works in a big-picture sense, as opposed to cutting yourself a very narrow slice of the great biological pie, then you have a challenging road ahead of you. Fortunately, many have walked it before you, and I want to give you some advice based on my own experiences. I should say at the outset that my own learning has focused mostly on the cell-biology part of the pie – not physiology, zoology, ecology, … and so my comments here refer to learning cell biology.
The scary thing is that I have been at this for almost 20 years (very part-time admittedly) and I would never dare to call myself a cell biologist. But I think it’s fair to say that by now I have a decent sense of what I know and what I don’t know. I will never be able to draw out the Krebs cycle, but I have a qualitative sense of its purpose and importance, as well as of general principles of cycles and catalyzed reactions in biochemistry. Not that impressive, I know, but I’m proud of it anyway.