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FAQ on Trajectory Ensembles

Q: What is a trajectory?

A trajectory is the time-ordered sequence of system configurations which occur as all the coordinates evolve in time following some rules – hopefully rules embodying reasonable physical dynamics, such as Newton’s laws or constant-temperature molecular dynamics.

Q: What is a trajectory ensemble?

It’s a set of independent trajectories that together characterize a particular condition such as equilibrium or a non-equilibrium steady state.  That is, the trajectories do not interact in any way, but statistically they describe some condition because of how they have been initiated – and when they are observed relative to their initialization … see below.

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More is better: The trajectory ensemble picture

The trajectory ensemble is everything you’ve always wanted, and more.  Really, it is.  Trajectory ensembles unlock fundamental ideas in statistical mechanics, including connections between equilibrium and non-equilibrium phenomena.  Simple sketches of these objects immediately yield important equations without a lot of math.  Give me the trajectory-ensemble pictures over fancy formalism any day.  It’s harder to make a mistake with a picture than a complicated equation.

A trajectory, speaking roughly, is a time-ordered sequence of system configurations.  Those configurations could be coordinates of atoms in a single molecule, the coordinates of many molecules, or whatever objects you like.  We assume the sequence was generated by some real physical process, so typically we’re considering finite-temperature dynamics (which are intrinsically stochastic due to “unknowable” collisions with the thermal bath).  The ‘time-ordered sequence’ of configurations really reflects continuous dynamics, so that the time-spacing between configurations is vanishingly small, but that won’t be important for this discussion.

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Everything is Markovian; nothing is Markovian

The Markov model, without question, is one of the most powerful and elegant tools available in many fields of biological modeling and beyond.  In my world of molecular simulation, Markov models have provided analyses more insightful than would be possible with direct simulation alone.  And I’m a user, too.  Markov models, in their chemical-kinetics guise, play a prominent role in illustrating cellular biophysics in my online book, Physical Lens on the Cell.

Yet it’s fair to say that everything is Markovian and nothing is Markovian – and we need to understand this.

If you’re new to the business, a quick word on what “Markovian” means.  A Markov process is a stochastic process where the future (i.e., the distribution of future outcomes) depends only on the present state of the system.  Good examples would be chemical kinetics models with transition probabilities governed by rate constants or simple Monte Carlo simulation (a.k.a. Markov-chain Monte Carlo).  To determine the next state of the system, we don’t care about the past: only the present state matters.

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“Proof” of the Hill Relation Between Probability Flux and Mean First-Passage Time

The “Hill relation” is a key result for anyone interested in calculating rates from trajectories of any kind, whether molecular simulations or otherwise.  I am not aware of any really clear explanation, including Hill’s original presentation.  Hopefully this go-around will make sense.

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Why Hair Gel Matters to Statistical Biophysicists

I was worried that a discussion of hair gel would have a certain bias toward men, but my wife assures me that women are just as likely to use a leave-in hair product.  I’m going to rely on that unstatistical assurance and roll right on.

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A hello: The point of this blog

Statistical physics governs the behavior of biological systems from the molecular scale (think protein stability and fluctuations) to the cellular scale (including heterogeneity and stochasticity of cellular behavior).  This is not a claim that understanding statistical physics, a.k.a. statistical mechanics, implies an understanding of cell biology.  But I do claim that cell biology cannot be understood without statistical physics.

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