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Epistemology of the Cell: A Systems Perspective on Biological Knowledge (IEEE Press Series on Biomedical Engineering)
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Review
“The authors of this interesting and opinionated book state that the driving force behind the work was Einstein’s comment that “science without epistemology is–insofar as it is thinkable at all–primitive and muddled . . . The last chapter of the book is an excellent exposition of the need for a systems-level model-based approach in biology and medicine.” (Computing Reviews, 19 February 2013)
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About the Author
EDWARD R. DOUGHERTY, PhD, is Director of the Genomic Signal Processing Laboratory at Texas A&M University, where he holds the Robert M. Kennedy '26 Chair and is Professor in the Department of Electrical and Computer Engineering. He is also co-Director of the Computational Biology Division at the Translational Genomics Research Institute as well as Adjunct Professor in the Department of Bioinformatics and Computational Biology, M. D. Anderson Cancer Center at the University of Texas. Dr. Dougherty has published more than 300 peer-reviewed journal articles and book chapters. MICHAEL L. BITTNER, PhD, is co-Director and Senior Investigator at the Computational Biology Division at the Translational Genomics Research Institute. Previously, he was associate investigator in the Cancer Genetics Branch of the National Human Genome Research Institute at the National Institutes of Health. Dr. Bittner holds a dozen patents and has published more than 100 articles.
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Product details
Series: IEEE Press Series on Biomedical Engineering (Book 34)
Hardcover: 216 pages
Publisher: Wiley-IEEE Press; 1 edition (August 30, 2011)
Language: English
ISBN-10: 1118027795
ISBN-13: 978-1118027790
Product Dimensions:
6.3 x 0.7 x 9.5 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#5,349,995 in Books (See Top 100 in Books)
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 Understanding Computers and Cognition: A New Foundation for Design 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 Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine. 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 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 Probability Theory I (v. 1) and Cramer Mathematical Methods of Statistics. (PMS-9) 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) Stochastic Systems and State Estimation, 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. However, 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.
[For full disclosure, the reviewer is a colleague and personal friend of one of the authors.]This book is a philosophical disquisition on the Epistemology of Science, as it relates to the interface between Engineering on one hand, and Biology and Biomedicine, on the other. The book is written by two accomplished scientists: a Mathematician (Dougherty), with a long career in the Engineering, and a Biologist (Bittner). These distinct backgrounds reflect the multidisciplinary foundation of the book.The philosophical outlook of the book is that of the revolutions in Science and Engineering that occurred in the first half of the 20th century, including Einstein's Relativity Theory and Quantum Mechanics in Physics, as well as the views of Biologist Conrad Waddington and Engineer Norbert Wiener, both of whom advocated a Systems approach to Biology. The authors argue that this epistemology, based on a positivistic/idealistic framework, is the final, mature foundation of science, which is the outcome of a long process that began with Galileo and Newton. The authors argue that Biology will become a successful, mature science only if it adopts this same epistemological view of science. The book supports its viewpoint with many famous quotes by eminent scientists throughout the text.A brief outline of the book follows. Chapter 1 introduces the main epistemological framework of the book, stressing an idealistic view, based on mathematical modeling and prediction, as opposed to a realist view, based on "everyday" explanation. It also emphasizes the limits of Science, and its pragmatic nature. It likens the current state of Biology to that of Physics at the end of the 19th century, and argues that Biology can only make the leap to a mature science if it can resolve the same epistemological conundrums that Physics had to face in the early 20th century.Chapter 2 is a comprehensive discussion of the evolution of the concept of causality in philosophy, from its beginnings in Aristotle through its definitive (according to the authors) formulation, proposed by Hume, going through the views of Francis Bacon, Galileo, Laplace, Kant, Mill, Einstein, Russell, and Reichenbach. This is important to the book, the authors argue, as the pre-Galilean view of Science (combated in the book) had as a distinguishing feature a naïve realist view of causality.Chapter 3 focuses on the scientific method, and its evolution from its beginnings in the ancients through modern 20th century Physics. It emphasizes the role of the controlled experimentation and prediction, a breakthrough credited to the early modern scientists, such as Bacon, Galileo and Newton, and of Mathematics as the proper language of scientific knowledge. It classes the concept of "explanation" as rationalistic and as an impediment to modern science: explanation, as causality, must remain outside the arena of scientific discussion. This chapter emphasizes again that Mathematics is the proper, and the only safe, language of science. Finally, it highlights the importance of an epistemology including uncertainty (expectation) as opposed to a deterministic epistemology (based on causality).Chapter 4 discusses the abstract regulatory processes of the cell as the essence of the "secret of life," as opposed to the mere physicochemical components that implement them; in this way, biology is separate from physics and chemistry. This is a "systems" view, first proposed by Waddington, as the authors point out. From this, the authors argue that this essential component of the living cell must be modeled as a stochastic dynamic system, with the accompanying issues of synchronicity, fault tolerance, and parallelism present in physical plants. This chapter requires some mathematical sophistication from the reader in its later sections. It discusses the important notion of canalizing genes, and goes on to survey briefly the "master-slave" model, intervention in probabilistic regulatory networks, and the "intrinsic multivariate prediction" phenomenon, all studied by the authors in previous scientific papers. Finally, the material of the chapter is analyzed from the epistemological perspective outlined in earlier chapters.A definition of translational (applied) science is given in Chapter 5. Translational science is identified with synthesis, as opposed to "pure" science, which is concerned with analysis. Synthesis is then identified with the modern engineering approach of Wiener and others. A wonderful quote by Wiener (page 91) summarizes the need for truly multidisciplinary teams in translational science, a goal that is still mostly a prospect at the present time. The discussion is supplemented by an example involving the external control of Boolean regulatory networks.Chapters 6 and 7 are the most technical in the book, dealing with the concept of stochastic validation in pattern recognition (classification), and in the inference of regulatory networks, respectively. Chapter 6 also includes a discussion on the need of a stochastic approach to describe physical systems, and how this can be done using conditional expectation. It also discusses the need of modeling assumptions for validating classifiers (and indeed, in translational science in general). Chapter 7 deals with some of the same issues of validation and performance, but this time for the much more complex case of network models.Chapter 8 is a somewhat acid critique of the current subjectivist and relativistic currents observed in much of 21st century Science, using for that the particular perspective of unreliable methods for classifier error estimation. It starts by reaffirming the overall epistemological viewpoint of the book, through Hume and Kant, and then proceeds to give specific examples relating to classification error estimation, showing that there can be large variances and no correlation between estimated and true errors. In an interesting twist, the authors compare belief in these techniques to the exhortation to believers, made by early protestant leaders, to rely on faith alone (sola fides = faith alone or, more to the point, "blind faith"). This is followed by warnings on the "illusion of technique" offered by modern computer technology (this is, incidentally, in no way a luddite manifesto) and the attempts of expanding the scope of the scientific method to suit one's desires.Finally, Chapter 9 is an essay on the limited prospects of medical intervention in the absence of a systems approach, citing many examples to support this view, and thus returning to the main theme of systems biology proposed by the book.
Two of the leading figures in translational genomics and computational systems biology, Dougherty and Bittner have taken on a subject that is at the very core of modern biology. Evoking Wiener and Rosenblueth, the authors draw on their own immense experience in systems theory and engineering, molecular biology and biochemistry, to guide the reader through foundational issues that, if ignored, stand squarely in the face of progress in biology and medicine. This elegantly written book is unique in its scope while achieving the depth that the subject matter deserves. Every systems biologist ought to read this book.
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