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
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.
1. Science and Knowledge 1
2. Causality and the Three Pillars of Aristotelian Science 11
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