396 9.2 Systems Biology and Biophysics: “Systems Biophysics”
A key question that systems biology has tried to address is, “what is the correct level of
abstraction at which to understand biology?” Reductionist approaches argue that we should
be able to understand all biology from a knowledge simply of the molecules present. In a
sense, this is quite obviously correct, though equally naïve, since it is not just the molecules
that are important but how they interact, the order in which they interact, and the gener
ating of higher order features from these interactions that can in turn feedback to the level
of interaction with single molecules. In other words, a more integrationist approach is valu
able, and physical scientists know this well from observing emergent behavior in many non
biological systems, complex higher length scale behavior that is difficult or impossible to
predict from a simple knowledge of just the raw composition of a system that can result from
the cooperativity between multiple shorter length scale elements that potentially obey rela
tively simple rules of interaction. As to where to draw the line in terms of what is the most
appropriate level of abstraction from which to understand biology, this is still a matter of
great debate.
KEY POINT 9.1
Many biologists treat the cell as the best level of abstraction from which to understand
biology, though some suggest that smaller length scales, even to the level of single genes
and beyond in just single sections of genes, are a more appropriated level. However,
feedback clearly occurs across multiple length scales in a biological organism, and in
fact in many cases between organisms and even between populations of organisms. So,
the question of where exactly to draw the line presents a challenge.
Modern systems biology has now adopted a core computational biology emphasis,
resulting in the development of powerful new mathematical methodologies for modeling
complex biosystems by often adapting valuable algorithms from the field of systems engin
eering. However, it is only comparatively recently that these have been coupled to robust bio
physical tools and techniques to facilitate the acquisition of far more accurate biomolecular
and physiological parameters that are inputted into these models. Arguably, the biggest
challenge the modern systems biology field set itself was in matching the often exquisite
quality of the modeling approaches with the more challenging quality of the data input, since
without having confidence in both any predictions of emergent behavior stemming from
the models may be flawed. In this section, we discuss some of the key engineering modeling
concepts applied to modeling interactions of components in an extended biological system,
and of their coupling with modern biophysical methods, to generate a new field of systems
biophysics.
9.2.1 CELLULAR BIOPHYSICS
The founding of systems biophysics really goes back to the early work of Hodgkin and Huxley
on the squid axon (see Chapter 1), which can also be seen as the first real example of a
methodical cellular biophysics investigation, which coupled biophysical experimental tools
in the form of time-resolved electrical measurements on extracted squid nerve cells, with a
mathematical modeling approach that incorporated several coupled differential equations to
characterize the propagation of the electrical impulse along the nerve cell. A key result in this
modeling is the establishment of feedback loops between different components in the system.
Such feedback loops are a general feature of systems biology and facilitate systems regulation.
For example, in the case of electrical nerve impulse propagation in the squid axon, proteins
that form the ion channels in the cell membrane of the axon generate electric current through
ion flux that either charges or discharges the electrical capacitance of that cell, which alters
the electrical potential across the cell membrane. But similarly, the electrical potential across
the cell membrane itself also controls the gating of the ion channel proteins.