Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- List of abbreviations
- 1 Introduction
- I Network Reconstruction
- II Mathematical Properties of Reconstructed Networks
- III Determining the Phenotypic Potential of Reconstructed Networks
- 15 Dual Causality
- 16 Functional States
- 17 Constraints
- 18 Optimization
- 19 Determining Capabilities
- 20 Equivalent States
- 21 Distal Causation
- IV Basic and Applied Uses
- V Conceptual Foundations
- 29 Epilogue
- References
- Index
15 - Dual Causality
from III - Determining the Phenotypic Potential of Reconstructed Networks
Published online by Cambridge University Press: 05 February 2015
- Frontmatter
- Dedication
- Contents
- Preface
- List of abbreviations
- 1 Introduction
- I Network Reconstruction
- II Mathematical Properties of Reconstructed Networks
- III Determining the Phenotypic Potential of Reconstructed Networks
- 15 Dual Causality
- 16 Functional States
- 17 Constraints
- 18 Optimization
- 19 Determining Capabilities
- 20 Equivalent States
- 21 Distal Causation
- IV Basic and Applied Uses
- V Conceptual Foundations
- 29 Epilogue
- References
- Index
Summary
Nothing in biology makes sense, except in the light of evolution
– Theodosius DobzhanskyThe stoichiometric matrix and the information associated with it fundamentally represent a biochemically, genetically, and genomically structured knowledge base. It can be used to analyze network properties and to relate the components of a network and its genetic bases to network or phenotypic functions. Biology is subject to dual causality, or dual causation [261]. It is governed not only by the physical laws but also by genetic programs. Thus, while biological functions obey the physical laws, their functions are not predictable by the physical laws alone. Biological systems function and evolve under the confines of the physical laws and environmental constraints. How organisms operate within these constraints is a function of their evolutionary history and their survival strategy.
Causation in Physics and Biology
Physics Classically, ‘cause and effect’ is established by formulating mathematical descriptions of conceptual models of fundamental physical phenomena. One example is molecular diffusion (see Figure 15.1). The fundamental process underlying diffusion is the random walk process that a collection of molecules undergoes. The statistical properties of the random walk process can be assessed quantitatively, and its macroscopic consequences are described with Fick's law. This law is described by a simple equation that is used as the basis to describe mass transfer processes from regions of high concentration to regions of low concentration. The established causality is the basis for computations that reliably predict mass transfer processes. The Boltzman and Nernst equations provide other specific cases of causality in physics, and there are many more examples.
Engineering design can be based on such predictions. Thus, in engineering, “there is nothing more practical than a good theory”, as the physical laws can be used for design, often with minimal experimentation and prototyping.
Cause and effect for physical phenomena are often well established and can be described mathematically. Mathematical descriptions are in the form of equations and inequalities.
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- Chapter
- Information
- Systems BiologyConstraint-based Reconstruction and Analysis, pp. 251 - 263Publisher: Cambridge University PressPrint publication year: 2015