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2.1  Introduction: The Material Stuff of Life

thermodynamics, one would normally assume a bulk ensemble population in regard to

thermal physics properties of the material. In addition, one would use the assumptions that

the material properties of each soft matter component in a given material mix are reasonably

homogenous. That is not to say that one cannot have multiple different components in such a

soft matter mix and thus hope to model complex heterogeneous biological material at some

appropriate level, but rather that the minimum length scale over which each component

extends assumes that each separate component in a region of space is at least homogenous

over several thousand constituent molecules. In fact, a characteristic feature of many soft

matter systems is that they exhibit a wide range of different phase behaviors, ordered over

relatively long length scales that certainly extend beyond those of single molecules.

The trouble is that there are many important examples in biology where these assumptions

are belied, especially so in the case of discrete molecular scale process, exhibited clearly by

the so-​called molecular machines. These are machines in the normal physicist definition that

operate by transforming the external energy inputs of some form into some type of useful

work, but with important differences to everyday machines in that they are composed of

sometimes only a few molecular components and operate over a length scale of typically

∼1–​100 nm. Molecular machines are the most important drivers of biological processes in

cells: they transport cargoes; generate cellular fuel; bring about replication of the genetic

code; allow cells to move, grow, and divide; etc.

There are intermediate states of free energy in these molecular machines. Free energy is

the thermodynamic quantity that is equivalent to the capacity of a system to do mechanical

work. If we were to plot the free energy level of a given molecular machine as a function of

some “reaction coordinate,” such as time or displacement of a component of that machine,

for example, it typically would have several peaks and troughs. We can say therefore that

molecular machines have a bumpy free energy landscape. Local minima in this free energy

landscape represent states of transient stability. But the point is that the molecular machines

are dynamic and can switch between different transiently stable states with a certain prob­

ability that depends upon a variety of environmental factors. This implies, in effect, that

molecular machines are intrinsically unstable.

Molecular free energy landscapes have many local minima. “Stability,” in the explicit

thermodynamic sense, refers to the curvature of the free energy function, in that the greater

the local curvature, the more unstable is the system. Microscopic systems differ from macro­

scopic systems in that relative fluctuations in the former are large. Both can have landscapes

with similar features (and thus may be similar in terms of stability); however, the former

diffuses across energy landscapes due to intrinsic thermal fluctuations (embodied in the

fluctuation–​dissipation theorem). It is the fluctuations that are intrinsic and introduce the

transient nature. Molecular machines that operate as a thermal ratchet (see Chapter 8) illus­

trate these points.

This is often manifested as a molecular machine undergoing a series of molecular con­

formational changes to bring about its biological function. What this really means is that

a given population of several thousands of these molecular machines could have signifi­

cant numbers that are in each of different states at any given time. In other words, there is

molecular heterogeneity.

KEY POINT 2.2

Molecular machines function through instability, resulting in significant molecular

heterogeneity.

This molecular heterogeneity is, in general, in all but very exceptional cases of molecular

synchronicity of these different states of time, very difficult to capture using bulk ensemble

biophysical tools, either experimental or analytical, for example, via soft matter modeling

approaches. Good counterexamples of this rare synchronized behavior have been utilized

by a variety of biophysical tools, since they are exceptional cases in which single-​molecule