The book, Epistemology
of the Cell, by Dougherty and Bittner is a gem. Dougherty is a Professor at
Texas A&M and has written extensively and brilliantly on the issue of using
systems thinking in the growing field of genomics. Now systems thinking was, in
way, started by Norbert Wiener in the 1930s, as he began to model various
systems, from the dynamics of nerve fibers to the development of the first
artificial arm. Yes, he did the arm, not the physician at Mass General. Wiener
also developed the systems to the control the pointing of radar controlled guns
on ships in WW II.
What made Wiener unique was that Wiener asked “Why”. The why
meant finding the cause and the result and establishing the connection between
the two, establishing the system. Physicists and chemists ask “why” questions,
the find the cause and the causality chain. Biologists were for ages asking
“what” as people who classified.
Darwin broke that chain of biologists somewhat by asking
“why” as regards to evolutions and he is pilloried even to this day. Physicians
often ask “what” and “how” and do not really want to be bothered by the “why”.
It is not their jobs to find out why, just find out “what” is wrong and know “how”
to fix it. Thus a physician is taught “what” to look for to diagnose prostate
cancer and is also taught “how” to remove it. The physician does not know or
care “why” the cancer is doing what it is doing. That is epistemological.
That, in essence, is a simplification of what Dougherty and
Bitter go about to explain. Their book explores the challenges set forth to
those exploring the gene and the cell and entices them to think beyond just the
what and how to go to the why, the explanation of the process, the system, from
cause to effect. The authors have written a treatise which compares a few
others which look at the epistemological basis of research, asking the correct
questions, and pursuing the best path to answer them (see the work by Winograd
and Flores as a prime example written some 25 years ago). Dougherty has with
his colleagues and associate been developing the ideas contained herein for
well over the past decade and I have read many of his works, they provide great
insight to what should be done.
Chapter 1 begins with a discussion of the definition of
epistemology, on p. 2. They define scientific epistemology by what it
addresses; knowledge and its truth. Then they use Kant to develop the
transition from the Enlightenment to today. On p. 4 there is a brief but
focusing discussion where he explains the simple Newtonian world maturing into
an Einstein one and likewise a Watson and Crick paradigm of DNA/RNA/proteins
into the way we understand cell dynamics today, as pathways, miRNAs, repressor
enzymes and the complexity of both intracellular dynamics and intercellular
dynamics. They rely extensively on a Popperian view of Science, which for those
more familiar with Kuhn may tend to have some slight dissonance, but it holds
together quite well.
All in all, Chapter 1 sets the stage well, both from
establishing the necessity to have models which answer why, and to establish
the counterpoint of the thinkers who have reverted back to Aristotelian
classification as the finders of what.
Chapter 2 is a discussion of Aristotelian causality. p 14
states it well with the statement,
“explanation must
involve a causal relation…”.
On p 21 they state, “Galileo
and Newton do not deny causality as a category of knowledge but they widen the
scope of knowledge to include mathematical systems that relate phenomena, while
bracketing “questions about nature” of the phenomenon…”.
On pp 33-34 they have an excellent discussion of Bertrand Russell’s
work on causality.
In Chapter 4, on p 70, the authors use the work of Norbert
Wiener and his associate Arturo Rosenbluth on Cybernetics. For it was indeed “the synergy of communications, control, and
statistical mechanics…” that set the framework of how we should view
cellular and organ dynamics. The authors then given examples of gene
regulation. I would have simply stated that every cell and every organic system
is a multidimensional distributed random process.
One could take a Feynman like approach and posit the
obvious, and then fill in the details. The authors work from the bottom up to
demonstrate their world view. Namely that when we look at cells we are looking
at complex dynamic random systems. Systems we can ascribe states to, states
being measurable quantities, which in turn operate on other states in a dynamic
fashion.
In Chapter 5 the authors start the transition to complex
state models. They again rely on the wisdom of Wiener on p 89 to state:
“Wiener recognized the
difficulties that the mathematical requirement of science and translational
science would present for medicine …”
For back in 1948 when Wiener had published Cybernetics,
Medicine was still a “what and how”
practice. It did not transition to a “why” approach. The translational science
that the authors speak of is:
“… mathematical
engineering, applied mathematics with a translational purpose..”
Namely, to translate nature to measurable quantities.
Quantities which we can then by knowing the “system” we can then manipulate and
predict. We can observe and we can control, the end goals of translational
science. In a Popperian sense, the authors address the issue of measuring,
predicting, and examining what does not do what we said it would.
The key arguments are developed in Chapter 9 and 10. Chapter
9 is the “sola fides” discussion, faith alone, as a mantra to those who
fail to understand the system nature of the cell dynamics. The authors, on p
149, evoke William Barrett, the insightful Columbia University philosopher, who
wrote The Illusion of Technique, a
superb work integrating the principles of epistemology and science in the late
1970s. Frankly, to see Barrett in a book of this type was an exciting surprise,
for I had thought that Barrett was falling into obscurity, a loss to many who are
struggling with issues that Barrett has thought through decades ago. The
authors then on p 148 also discuss the nature of stochastic dynamic systems.
Dougherty brings insight via avenues that I found resonated
strongly. The discussion on Wiener, where Dougherty, unlike Gleick in what I feel
is presented with uninformed bias, sees Wiener as the father figure, one
combining systems thinking, clear and built upon strong mathematical
foundations, which is then integrated with real biological systems. Although I
find their approach insightful and compelling, I would have taken pathways in
cancer dynamics as somewhat well-defined stochastic systems.
For example, we know the effects of PTEN, the AKT pathway,
and the MYC pathway, the p53 pathway, and the complex dynamics which are well
described in the readily available NCI data base of pathways. One can use as
states the concentrations of any one of these proteins and then state simply
that they all interact with one another, the result being homeostasis or if a
change cancer. The model is multidimensional, stochastic, highly complex, and
strewn with “noise”, namely uncertainties. Models have been developed and
tested for such cancers as prostate, melanoma and colon. Dougherty, himself,
has made substantial contributions to this area. It would have been useful
perhaps to demonstrate this approach as well.
On p. 163 the authors place a stake in the ground to say
what would be expected for those to work in the field, that the books by Loeve
and Cramer be used as standard bearers! As I read that in the book I looked on
my bookcases and saw my old well-worn copy of Loeve, which got me through my
PhD. Cramer was my core text for my introductory course, but then again it was
MIT. Thus they set a high hurdle, but a necessary one for those to work in the
field. My first book, Stochastic Systems
and State Estimation (Wiley, 1972), in a sense was one of the many which
established the bar.
The clear strike at the adversaries is set on p. 165. After
again referring to Barrett and Kant, the authors end with:
“Does anyone really
believe that data mining could produce the general theory of relativity?”
I think this can be extended. For example many researchers
run millions of microarrays and are currently finding hundreds of SNPs or
thousands of miRNAs and each time they send out a press release saying they
have “discovered” some new “gene” or worse “cause” or “cure” of say prostate
cancer or melanoma. In reality one does not know whether this is a marker for
cancer, a marker for a predisposition for cancer or just plain noise.
What the authors, and others, have argued is that it is
essential to have a well-defined dynamic system model of how say PTEN and AKT
interact and how they in turn control MYC and where the controls on p53 are in
this chain. The microarray analyses should be done in the context of defining
the linkages in the state model and not as ends in themselves. The model can
then be validated. From such a model we can then see conditions on their way to
cancer and conditions representing advanced cancers. For example, recent
authors have announced a way to measure PTEN in prostate cancer and laud that
as a diagnostic step. In reality by the time PTEN has been deactivated there is
most likely a metastasis. Understanding and refining a model is the essence of
the “why” articulated by the authors.
On p 166 there is a superb critique of what the authors call
the pre-Galilean thinkers, namely the
biologists who like Linnaeus were really just classifiers of forms and shapes
failing to understand why they were what they were. One must remember that
biology was all too often just a study of things and a process of naming them
and classifying them. The systems which made for these differences were little
understood, and worse, beyond the mindset of many who practiced in the field.
Chapter 9 is. in my opinion. the pinnacle of their argument.
Simply put, we should now begin to perform our experiments within the context
of a model. For example, we know many of the pathways of the key genes
intracellular, but we do not yet understand the dynamic model that controls
them. Thus, when we do microarray tests, we should be doing them to determine
the constants in the model and then validate that design. We should, in effect,
identify the system, using a system
framework, not just some unstructured set of classifiers. We have the
structures, now is the time to put them to use.
Science is iterative. It is an iterative set of models and
refinements. On p 171, the authors refer to Turing’s last paper on
tessellation, or why zebras have stripes. The paper by Turing was submitted a
day or two before he committed suicide and it was done without the benefit of
Watson and Crick who were simultaneously doing their work at Cambridge. He
intuited intercellular flow of some yet to be defined controlling substances.
The concentration of these unknown substances would rise and fall in
concentration and as such the color would change.
This approach has recently been applied, using a system
model of flower genetics, and it explains and demonstrates the control of
patterning in a genus of flowers. Having the model for this genus of flower,
which is experimentally verifiable, one can then do the inverse, namely the
controllability issue of creating desired flower patterns. That also is the
essence in cancer dynamics, namely of creating a control or cure, but with a
verifiable model. One must have the model, thus say the authors. Thus says
nature! If one takes the authors systems approach and applies it to
intercellular systems thinking, then it can be argued that the stem cell of
cancer theory as has been recently evoked can be readily explained, as readily
as those zebra stripes! That is the strength of the model posited by the
authors.
My only negative is the price. There are some 187 pages and
the price is well above $100. Amazon does have a better price but not much.
That is over $0.60 per page, and for that I would blame not just Wiley, but the
IEEE which somehow has all too inflated prices. This book is so insightful that
the barrier to entry should be more modest. It is worth the insight at any
price however.
And one last nit. The work of the authors should also shine
a light on macroeconomics, which suffers not from a surfeit of models and
mathematics, but from any ability to validate them, just look at the current
state of the economy. Thus what biology brings from data to systems,
macroeconomics brings from systems to reality. Both have to merge and be able
to be predictive. We are approaching that in biology, especially with the
insight of the authors, it is a pity that such has not yet passed the minds of
those who opine on the world’s workings.
Posts
