"Honorable mention – Biomedicine and Neuroscience, 2011 Prose Awards"
An examination of how the cell should be described in order to effectively process biological data
"The fruitful pursuit of biological knowledge requires one to take Einstein's admonition [on science without epistemology] as a practical demand for scientific research, to recognize Waddington's characterization of the subject matter of biology, and to embrace Wiener's conception of the form of biological knowledge in response to its subject matter. It is from this vantage point that we consider the epistemology of the cell."—from the Preface
In the era of high biological data throughput, biomedical engineers need a more systematic knowledge of the cell in order to perform more effective data handling. Epistemology of the Cell is the first authored book to break down this knowledge. This text examines the place of biological knowledge within the framework of science as a whole and addresses issues focused on the specific nature of biology, how biology is studied, and how biological knowledge is translated into applications, in particular with regard to medicine.
The book opens with a general discussion of the historical development of human understanding of scientific knowledge, the scientific method, and the manner in which scientific knowledge is represented in mathematics. The narrative then gets specific for biology, focusing on knowledge of the cell, the basic unit of life. The salient point is the analogy between a systems-based analysis of factory regulation and the regulation of the cell. Each chapter represents a key topic of current interest, including:
- Causality and randomness
- Translational science
- Stochastic validation: classification
- Stochastic validation: networks
- Model-based experimentation in biology
Epistemology of the Cell is written for biomedical researchers whose interests include bioinformatics, biological modeling, biostatistics, and biological signal processing.
|Series:||IEEE Press Series on Biomedical Engineering Series , #34|
|Product dimensions:||6.30(w) x 9.30(h) x 0.70(d)|
About the Author
EDWARD R. DOUGHERTY, PhD, is Director of the Genomic SignalProcessing Laboratory at Texas A&M University, where he holdsthe Robert M. Kennedy '26 Chair and is Professor in the Departmentof Electrical and Computer Engineering. He is also co-Director ofthe Computational Biology Division at the Translational GenomicsResearch Institute as well as Adjunct Professor in the Departmentof Bioinformatics and Computational Biology, M. D. Anderson CancerCenter at the University of Texas. Dr. Dougherty has published morethan 300 peer-reviewed journal articles and book chapters.
MICHAEL L. BITTNER, PhD, is co-Director and SeniorInvestigator at the Computational Biology Division at theTranslational Genomics Research Institute. Previously, he wasassociate investigator in the Cancer Genetics Branch of theNational Human Genome Research Institute at the National Institutesof Health. Dr. Bittner holds a dozen patents and has published morethan 100 articles.
Table of Contents
1. Science and Knowledge 1
2. Causality and the Three Pillars of Aristotelian Science11
3. Scientific Knowledge 35
4. Cells and Factories 59
5. Translational Science 85
6. Stochastic Validation: Classifi ers 97
7. Stochastic Validation: Networks 129
8. Sola Fides 147
9. Model-based Experimentation in Biology 169
IEEE Press Series on Biomedical Engineering 203