Problems at the interface between biology and physics offer unique
opportunities for physicists to make quantitative contributions to biology. Equally important,
they enrich the discipline of physics by challenging its practitioners to think in new ways.
In
the introduction to his classic magnetic-monopole paper of 1931, Paul Dirac remarked,
There are at present fundamental problems
in theoretical physics awaiting solution, e.g. the relativistic formulation of quantum mechanics
and the nature of atomic nuclei (to be followed by more difficult ones such as the problem of life),
the solution of which problems will presumably require a more drastic revision of our fundamental
concepts than any that have gone before.1
Dirac was famously and fanatically
economical with words; his observation is therefore probably more than just a flight of fancy or
a throwaway comment. Dirac posits that the nature of life is a fundamental and central question
not only for biologists, but for physicists as well. In the excitement of the quantum revolution,
however, that view was never widely adopted, and only a small fraction of the physics community
took up Dirac's challenge.
Today, humanity is reaping
the fruits of nearly 100 years of basic research into the quantum world. Many great fundamental
problems have been solved, and much effort is now spent on developing engineering applications
of quantum mechanics in such diverse areas as communication, metrology, and computation. One
can therefore ask, If many of the central ideas that have dominated physics for a century are now
maturing into engineering tools, what are the next great fundamental problems for physicists
to work on?
We think that any top-10
list of challenges for physicists would have to include some items that address the startling complexity
of the living world. Many beautiful and mysterious problems are revealed in the puzzling variety
of living organisms that range from viruses—molecules that copy themselves—to rock-eating
bacteria to beings with complex and conscious thought and action. Furthermore, the stunning successes
of molecular and structural biology, biochemistry, and genetics have yielded an explosion of
biological data that are increasingly quantitative in character. For example, gene expression
is routinely characterized in terms of how much, when, and where. Similarly, data on some machinery
of the cell are reported graphically in terms of force–velocity curves. As a result, despite
the field's reputation as a soft science, nearly all of biology is now ripe for quantitative analysis
of the sort that physicists are used to. The opportunities are analogous to those that came to astrophysics
once astronomical observations were coupled to spectrometry.
Life presents many interesting
questions for physicists. As illustrations, we discuss three problems at the interface between
physics and biology—steppingstones to more general thinking that will enrich physics.
First, we describe the molecular machines that form the basis of life. The energies and length scales
at which those machines operate are intriguing because they are in the regime where the energy-versus-length
curves for a host of different phenomena converge. Our second thrust concerns biological "many-body"
problems, in particular the orchestrated activities of the macromolecular assemblies within
the cell. Our third illustration concerns the need for a theory of biological dynamics that respects
not only the many-body character of biological systems, but also their far-from-equilibrium
operation.
The scope of this article
is limited—other authors would emphasize different problems and examples—but its
main argument is indifferent to the particular case studies. In many ways, we who work at the biological
frontier of physics are only getting our first inklings of the rich interplay between biological
phenomena and the physical principles that animate them (see the article by Ray Goldstein, Phil
Nelson, and Tom Powers, PHYSICS TODAY, March 2005, page 46). As a result, the study of living matter
should be seen as an exciting and substantive part of the modern definition of physics.
The machinery of life
Molecular machines are the basis of life.
DNA, a long molecule that encodes the blueprints to create an organism, may be life's information
storage medium, but it needs a bevy of machines to read and translate that information into action.
The cell's nanometer-scale machines are mostly protein molecules, although a few are made from
RNA, and they are capable of surprisingly complex manipulations. They perform almost all the important
active tasks in the cell: metabolism, reproduction, response to changes in the environment, and
so forth. They are incredibly sophisticated, and they, not their manmade counterparts, represent
the pinnacle of nanotechnology. Yet scientists have no general theory for their assembly or operation.
The basic physical principles are individually well understood; what is lacking is a framework
that combines the elegance of abstraction with the power of prediction.
Proteins are quite different
from the simple diatomic molecules that represent the traditional border between physics and
chemistry; they are enormously large, and for many purposes quantum mechanics plays a negligible
role in their function. Of course, if the question of interest happens to be the chemistry that takes
place in the active site of an enzyme, one must ultimately look to quantum mechanics as the basis
for understanding. Quantum mechanics can be neglected in the same sense that it is ignored in dynamical
descriptions of everyday objects: On the smallest length scales, all atoms are fundamentally
quantum, but Planck's constant is not needed to formulate and apply the principle of least action.
Indeed, one would be hard put to describe many physical phenomena, ranging from protein behavior
to critical phenomena to galaxies, if a fully quantum mechanical description were required. Proteins
as molecules are polymers, and can often be treated with a combination of continuum mechanics and
statistical mechanics. They act, in other words, as essentially classical objects.
How much can one molecule
do? Consider, for example, ATP (adenosine triphosphate) synthase. This macromolecular assembly,
only about 10 nanometers on a side, is an essential part of the cellular factory that produces ATP,
the universal energy currency of life. We will not get into the details of the biological role of
ATP synthase in the cell, but consider merely what it is capable of doing in isolation: It is a rotary
motor. In the presence of a proton gradient, this remarkable machine turns a spindle as it adds phosphate
groups to molecules of adenosine diphosphate to produce ATP.2 And every day, as discussed
in box 1, the cells in your body perform this phosphate-addition reaction to produce roughly your
body weight in ATP molecules.
But that is not all: ATP synthase
can run in reverse. It can consume ATP, and with each ATP molecule that is hydrolyzed, the central
shaft of ATP synthase turns by 120 degrees, directly converting chemical to mechanical energy.
That reverse operation was explicitly demonstrated through a series of elegant experiments in
which a molecular propeller was attached to the shaft and then imaged with optical microscopy (see
Figure 1).3 The propeller rotated in the presence of ATP, with absolute thermodynamic
efficiencies of up to 90%. Despite the tremendous strides made in nanotechnology, no device of
similar functionality can yet be fabricated with inorganic materials. Furthermore, many questions
remain about the basic principles by which molecular machines such as ATP synthase convert chemical
energy to mechanical forces.
As noted in our introductory remarks,
molecular machines operate at energies and lengths common to a host of different processes. In
addition to being intriguing, that regime adds to the challenge of analyzing the cell's machines.
Figure 2 shows how thermal, chemical, mechanical, and electrostatic energies scale with the size
of an associated object, and illustrates the confluence of energies. As the characteristic size
approaches that of biological macromolecules, all the energies converge. The convergence is
remarkable, since the energies range over 20 orders of magnitude as object size scales from subatomic
to macroscopic; its existence is an opportunity for complex physical phenomena and processes
that are evidently utilized by life. Broadly speaking, the interplay between thermal and deterministic
forces is what gives rise to the rich behavior of molecular machines. For example, thermal effects
permit such processes as diffusion, conformational changes, the dissolution of hydrogen bonds,
and the wandering of charges from their molecular hosts. Those processes, in turn, often serve
as the basis of macromolecular functions ranging from copying and reading DNA to the motor action
of molecules, such as myosin, that power our muscles.
One of the most important
distinctions between molecular machines and their macroscopic counterparts is that molecular
machines live in an environment of large thermal forces. As a result of the interplay between thermal
and deterministic forces, statistical mechanics is an essential tool in understanding molecular
machines. To get a feel for the importance of thermal effects, note that the natural energy unit
of physical biology is the piconewton-nanometer: The piconewton is the characteristic force
generated by molecular machines, as determined through single-molecule experiments for example,
and the nanometer is the typical length scale. A molecular machine that operates with 100% efficiency
and uses up one ATP per cycle produces about 100 pN-nm of work. By comparison, the thermal energy
kT is roughly 4 pN-nm.
It is surely one of the triumphs
of evolution that Nature discovered how to make highly accurate machines in such a noisy environment.
One marvelous example is DNA polymerase, a molecular copying machine only 13 nanometers in size,
capable of copying DNA molecules with an intrinsic error rate approaching one part per million.
Much remains to be understood about the general principles behind such impressive fidelity, especially
as it is achieved in the violent thermal environment of a test tube or a cell.
Molecular machines need
to be accurate in the face of noise, but they can also use fluctuations as an essential part of their
function. As an example, consider restriction enzymes—proteins that recognize and cut
specific DNA sequences. Those enzymes are extremely efficient at searching through a genome consisting
of millions, sometimes even billions, of base pairs to find and bind to their recognition sequences.
The rates at which they accomplish their tasks are inconsistent with simple one-dimensional diffusion
along the DNA molecule or strictly three-dimensional diffusion and binding to the DNA target site.
Instead, restriction enzymes take advantage of the entropic forces that cause long DNA molecules
to fold into a compact coil. They hop from one strand to another, which speeds up the search process
relative to 1D diffusion.
A complete theory of molecular
machines needs to take into account all the effects illustrated in Figure 2 and so must include ideas
from continuum mechanics, statistical mechanics, chemical kinetics, and fluid mechanics. It
should provide a predictive, unifying framework that, without resorting to a full atomic description,
allows an accurate description of the dynamic behavior of any molecular machine. Paradoxically,
the challenge is to take the hard-won atomic-level coordinates that fill structural databases
such as the Worldwide Protein Data Bank and to build models that no longer make explicit reference
to those coordinates. Indeed, one of the most intriguing challenges for physicists to tackle in
their analysis of cellular machines is to find out to what extent it will be possible to construct
coarse-grained models of those machines. Box 2 offers a fable that speaks to the dangers of ignoring
that challenge.
Machines do not a cell make
Scientists have made dramatic progress
in understanding the molecular machines that operate in cells. They have determined many of their
structures, characterized individual motors for their ATP activity and force–velocity
properties, explored the connections between mutations and function, and more. Nonetheless,
dissecting individual machines is only a step toward understanding how collections of such machines
give rise to the activities of living organisms. Though many quantitative models treat cells as
a "bag of enzymes," in reality cells have a great deal of internal structure.
One of the key hallmarks
of biological function is ordering in space and time, and at least two great classes of biological
orchestration should serve as a call to action for physicists: the coordination of physical structures
and processes and the orchestration of information. In a sense, we offer in this section a counterpoint
to our presentation of the machinery of life. That discussion celebrated magnificent molecular
machines and some of the challenges scientists must face to understand them individually. By way
of contrast, this section argues that even a perfect understanding of each and every individual
molecular machine would be inadequate for explaining what goes on in a cell, just as an understanding
of the hydrogen atom is merely a prelude to explaining the electronic behavior of crystalline solids
and, more dramatically, collective effects like the quantum Hall effect.
In many instances, the machines
of the cell are integrated into collections of many parts, often with proteins, nucleic acids,
lipids, and other molecules working in concert. One of the most important ways that physicists
come to terms with systems comprising many interacting degrees of freedom is to consider collective
excitations. For example, phonons characterize the vibrations of a crystalline solid and magnons
describe collective excitations of magnetic spins.
Indeed, physicists talk
of "-ons" of all kinds. The biological setting provides a loose analogy because some biological
structures are characterized with the label "-somes," which derives from the Greek word for "body."
The term refers to macromolecular assemblies that are made from multiple molecular components
that act in a collective fashion to perform multiple functions. Some of the most notable examples
include the ribosome, used in protein synthesis; the nucleosome, which is the individual packing
unit for eukaryotic DNA; the proteasome, an assembly that mediates protein degradation; and the
transcriptisome, which mediates gene transcription. By mechanisms and principles that are still
largely unknown, proteins assemble into -somes, perform a task, and then disassemble again.
One of the most pleasing
examples of biological collective action is revealed by the machines of the so-called central
dogma. The term refers to the set of processes whereby DNA is copied (replication), genes are read
and turned into messenger RNA (transcription), and finally, messenger RNA is turned into the corresponding
protein by ribosomes (translation). Such processes involve multiple layers of orchestration
that range from the assembly of macromolecular complexes to the simultaneous action of multiple
machines to the collective manner in which cells may undertake the processes. Figure 3 shows the
machines of the central dogma in bacteria engaged in the processes of transcription and translation
simultaneously.
The theme of collective
action is also revealed in the flow of information in biological systems. For example, the precise
spatial and temporal orchestration of events that occurs as an egg differentiates into an embryo
requires that information be managed in processes called signal transduction. Biological signal
transduction is often broadly presented as a series of cartoons: Various proteins signal by interacting
with each other via often poorly understood means. That leads to a very simple representation:
a network of blobs sticking or pointing to other blobs. Despite limited knowledge, it should be
possible to develop formal theories for understanding such processes. Indeed, the general analysis
of biological networks—systems biology—is now generating great excitement in the
biology community.
Information flow in the
central dogma is likewise often presented as a cartoon: a series of directed arrows showing that
information moves from DNA to RNA to proteins, and from DNA to DNA. But information also flows from
proteins to DNA because proteins regulate the expression of genes by binding to DNA in various ways.
Though all biologists know that interesting feature of information flow, central-dogma cartoons
continue to omit the arrow that closes the loop. That omission is central to the difference between
a formal theory and a cartoon. A closed loop in a formal theory would admit the possibility of feedback
and complicated dynamics, both of which are an essential part of the biological information management
implemented by the collective action of genes, RNA, and proteins.
Understanding collective
effects in the cell will require merging two philosophical viewpoints. The first is that life is
like a computer program: An infrastructure of machines carries out arbitrary instructions that
are encoded into DNA software. The second viewpoint is purely physical: Life arises from a mixing
together of chemicals that follow basic physical principles to self-assemble into an organism.
Presumably, the repertoire of available behaviors is more limited in the latter. The two viewpoints
are complementary, not incompatible: Either one could best describe cell behavior, depending
on the particular situation.
Time in its place
One popular way to capture biological
thinking about the machines of the cell is through the linked qualities of structure and function.
With increasing regularity, structure is being brought under control, as evidenced by huge databases,
including the Worldwide Protein Data Bank, that are the repository for the hard-won successes
of structural biologists. On the other hand, function is inherently a question about dynamics.
And for the moment, it is a question that remains unanswered in any general way. As a result, one of
the most compelling challenges for those trying to shed light on the function of the cell's machines
is to put time in its rightful place.
In a deep sense, the problem
of the dynamics of macromolecules and their assemblies, of organelles, and of cells themselves
strikes right to the heart of just how much physicists will be able to do with systems that are far
from equilibrium. Indeed, we believe that biological dynamics is the example of nonequilibrium
physics. Until now, much of the emphasis in the study of nonequilibrium systems has been on small
departures from equilibrium. Furthermore, in many instances the debate that has swirled around
questions of nonequilibrium has been philosophical rather than centered on making predictions
about specific experimental case studies. Biology, though, may provide the jumping-off point
for systematic and predictive ideas on nonequilibrium physics because of the existence of so many
manifestly important and well-characterized systems.
Erwin Schrödinger
appreciated that understanding biology requires understanding nonequilibrium systems and
enunciated that view in his classic 1944 essay What Is Life? (Cambridge U. Press, 1992).
He called for a new theory of physics that is concerned with understanding the behavior of single
molecules far from equilibrium. When Schrödinger wrote, scientists did not know the identities
of the molecules that form the basis of life. Still, it was possible to infer that the gene was a molecule
and that understanding the mechanisms of life depended on understanding the properties of molecules
as machines.
Several categories of thinking
may be applied to the subject of nonequilibrium systems. The pessimistic view argues that the search
for general principles is doomed and that one will likely do no better than to solve problems on a
case-by-case basis. Some observers have expressed impatience with that point of view. Physicist
Percy Bridgman, for example, has eloquently noted that "the admission of general impotence in
the presence of irreversible processes appears on reflection to be a surprising thing. Physics
does not usually adopt such an attitude of defeatism. Of course this may be made a matter of words
if one chooses, and one can say that thermodynamics by definition deals only with equilibrium states.
But this verbalism gets nowhere; physics is not thereby absolved from dealing with irreversible
processes."4 The study of biological systems demands that physicists redouble their
efforts to make progress on nonequilibrium processes since biological systems are intrinsically
out of equilibrium. Moreover, there is a growing list of biological examples whose nonequilibrium
behavior has been characterized quantitatively.
A key feature of the cellular
interior that makes studying cells especially challenging is its intense crowding, beautifully
illustrated in the paintings of David Goodsell (see the cover of this month's issue).5
As an example, the standard apparatus of equilibrium statistical mechanics needs to be called
into question for the dynamic assemblies seen at the leading edge of motile cells. Not only are they
far from equilibrium, but standard approaches to such systems are often dominated by chemical
potentials based on dilute solutions and on diffusion equations suitable for dilute and homogeneous
bulk systems. As a result, the study of the crowded and bustling interior of living cells raises
numerous questions about physical materials that are neither dilute, static, nor homogeneous.
With increasing regularity,
experimental observations on single molecules, macromolecular assemblies, and even cells themselves
are couched in terms of trajectories (see the article by Carlos Bustamante, Jan Liphardt, and Felix
Ritort, PHYSICS TODAY, July 2005, page 43). That is, scientists have recognized that the temporal
evolution of biological systems and their building blocks is measurable, interesting, and reproducible.
For example, a beautiful set of recent experiments whose results are illustrated in Figure 4a showed
that it is possible to monitor the trajectories of individual molecular motors for extended periods
of time and with extremely high spatial resolution. Not only do such experiments get at the mechanism
by which motors move, but they also reveal something about both the collective action of motors
and the fluctuations suffered by individual motors. As shown in Figure
4b, trajectory analysis
also proves useful at larger scales. Indeed, the trajectories of motile cells exposed to a time-varying
temperature provide clues about the dynamics and control of cytoskeletal proteins' rich behavior.
Beyond the cartoons
In the 75 years since Dirac posed his challenge,
scientists have made tremendous progress in discovering and cataloging the molecules that form
the basis of life. In what respect is their pursuit intellectually distinct from the "stamp-collecting"
mindset of the pre-molecular era? One of the biggest opportunities provided by the explosion of
biological data is the chance to revisit biological phenomena and use the quantitative interplay
between theory and experiment as a measure of understanding. In this article we have outlined major
areas that are amenable to the kinds of experiments and theories that physicists are used to: understanding
the operational principles of molecular machines and assemblies, understanding the collective
effects that give rise to the exquisite orchestration in space and time revealed by cellular life,
and developing new ideas on nonequilibrium statistical mechanics that provide a suitable framework
for understanding in vivo cellular processes. Clearly, biologists have already thought deeply
about those issues, but we believe a physics perspective brings its own unique contributions.
The advent of a host of powerful
experimental techniques is opening new windows into the study of living matter. Full genome sequencing
and gene-expression analysis serve as a reminder that organisms are finite, closed systems, and
that data limit the number of possible models. In addition, one can make exhaustive measurements
of the effects of perturbations on both cells and their environments. Such investigations admit
a new conceptual point of view in which one makes a systemwide analysis of the effects of perturbations
rather than an incomplete, piecemeal assessment. Single-molecule biophysics techniques, which
create new ways to observe, study, and characterize macromolecular machines (see the article
by Terence Strick, Jean-François Allemand, Vincent Croquette, and David Bensimon, PHYSICS
TODAY, October 2001, page 46), are providing exactly the sort of data needed to address some of the
problems we have described in this article. We are convinced that such problems will pose fruitful
challenges for experimental and theoretical physicists for a long time to come.
We are grateful to Robert
Bao, Seth Blumberg, Curt Callan, Ken Dill, Dave Drabold, Hernan Garcia, Bill Gelbart, Paul Grayson,
Mandar Inamdar, Marc Kamionkowski, Jané Kondev, Albion Lawrence, Phil Nelson, Eric Peterson,
David Politzer, Paul Selvin, Julie Theriot, David Van Valen, and Jon Widom for commenting on the
manuscript.
Rob Phillips
is a professor of applied physics and mechanical engineering at the California Institute of Technology
in Pasadena. Steve Quake is a Howard Hughes Medical Institute investigator, and
is a professor of bioengineering with a courtesy appointment in the applied physics department
at Stanford University in Stanford, California.
8. F. S. Soo, J. A. Theriot, Proc. Natl. Acad. Sci. U.S.A.102, 16233 (2005) [MEDLINE].
9. F. C. Neidhardt, J. L. Ingraham, M. Schaechter, Physiology of the Bacterial Cell: A Molecular Approach, Sinauer Associates, Sunderland, MA (1990), chap. 1.
10. A. Kornberg, For the Love of Enzymes: The Odyssey of a Biochemist, Harvard U. Press, Cambridge, MA (1989), p. 65.
11. J. L. Borges, Collected Fictions, A. Hurley, trans., Penguin, New York (1999).
Featured Jobs
