Peter Wellstead (The Hamilton Institute, National University of Maynooth, Ireland), Sree Sreenath (Case Systems Biology Initiative, Case Western Reserve University, USA) and Kwang-Hyun Cho (Korea Advanced Institute of Science and Technology (KAIST), Kor)

Copyright: © 2009
|Pages: 16

DOI: 10.4018/978-1-60566-076-9.ch002

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TopIn this chapter the authors give their experiences gained working at the interface between the biological/medical sciences and the physical/engineering systems sciences. In doing so we attempt to convey the contributions that the physical, mathematical and engineering sciences have made, and will continue to make, to innovations in biology and medicine. In this context we stress the role played by systems and control theory in the development of general principles for biological systems, and in particular the understanding of dynamical phenomena in biology and medicine. According to our experiences, systems methods are influencing the biology research sector through a series of evolutionary scientific steps, as follows:

*•***Stage 1:**High-throughput biochemical instrumentation was (and continues to be) developed to provide rapid measurement and generation of*data*.*•***Stage 2:**To meet the need to process data generated in stage 1, data processing methods are being developed to extract*information*from very large data records.*•***Stage 3:**The information from stage 2 is used to calibrate mathematical models with which to*visualise*an underlying biological process. This is the current evolutionary state in systems biology.*•***Stage 4:**Control and systems theory are applied to the mathematical models of stage 3 to provide*understanding*of biological behaviour and underlying principles.

In summary, the sequence goes from:

measurement → data → information → visualisation → understanding.

The current state of the art is that the value of *in-silico* simulation of biological phenomena is becoming appreciated. Even so, most biological measurement techniques are designed to collect static data, whereas time course data is required to develop mathematical models for visualising system dynamics by *in-silico* simulation. It is not always appreciated that, as a result of poor data, the calibration and structural correctness of mathematical models is often suspect. Likewise, there is currently little appreciation of the fundamental importance of control and systems theory in understanding biological and physiological phenomena and principles.

On the other hand, the role of systems and control theory is clearly established in the medical community through the understanding that it gives to physiological function. Under the historical influence of Claude Bernard’s ideas, as embodied in Cannon’s concept of homeostasis (Bayliss, 1966, Cannon, 1932), feedback control is central to many aspects of current medical understanding, although this is usually intuitive and non-theoretical in nature (Tortora, 2003). Since Cannon’s work in the 1930’s, other researchers have expanded upon the homeostatic feedback principle (Sterling, 2004) in its specific medical and physiological contexts. In the meantime however, systems and control theory has expanded scientifically and progressed to become a mature scientific discipline with fundamental relevance to all areas of scientific endeavour. Throughout this 70-year period of separate development, the medical concepts of control systems and the mathematical tools of control and systems theory have diverged. The aim of this chapter is to reconnect the medical ideas of feedback with mainstream theory by explaining areas where control and systems theory can contribute. We consider this to be vitally important to our scientific futures. For, as indicated above and documented in the recent report *Systems Biology: a vision for engineering and medicine* (Royal Academy, 2007), the use of systems theory and control concepts will be essential to our understanding of biological systems for medicine.

Closed Loop Feedback Control: This is the process of continuously measuring the output of a system and using a modified version of the measured output at the systems input so as to alter the overall performance of the system.

Matlab: The name of a widely used proprietory software package that is especially suited to the simulation of dynamical system models and their analysis. It is produced by MathWorks Inc. It is adapted from a public domain package of the same name – public domain equivalents are available as Octave and Scilab.

Transfer Function: The name given to the frequency domain representation of a functional system module with distinct input and output points.

Dynamical System: An assembly of components or sequence of reactions whose performance can only be completely described by a study of its behaviour over time.

State Space: The name given to the mathematical space into which mathematical models are put for systems and control studies using temporal analysis of the time course data. State space (or time domain) analysis is suitable for linear or non-linear systems analysis. This is therefore highly suited to medical and systems biological analysis.

Systems Theory: The set of mathematical techniques used to analyse and understand the (dynamical) behaviour of systems.

Feedback: The technique of monitoring information from one part of a system and using it to modify a system element at some point prior to the monitoring point. If the monitored information is used to add to the system element it is positive feedback , if it is used to subtract from the system element it is negative feedback.

Data Analysis: The analysis of time course data from a system in order to understand the nature of the signal generating mechanisms associated with a system. These are often unwanted noise or errors in the process and are used to modify or correct the mathematical model.

Mathematical Model: A set of equations, usually ordinary differential equations, the solution of which gives the time course behaviour of a dynamical system. The set of equations for example 1 is an example of a mathematical model.

Linearity: Is the property of a system where if two inputs sequences X a and X b produce responses Y a and Y b , then X a +X b will produce the response Y a +Y b . The system is said to be linear – most biological and medical systems do not satisfy this criteria and are said to be non-linear.

Pharmacodynamics: This refers to the analysis of the biochemical and physiological effects of drugs and the mechanisms in which they work.

Control Theory: The set of mathematical techniques used to analyse and design control systems.

Stability Analysis: That part of systems and control theory which is used to study and predict the stability or instability characteristics of a system from a knowledge of the mathematical model.

System Identification: The analysis of time course data from a system in order to deduce the nature of the system and the values of parameters that could be used in a mathematical model to reproduce the time course data in simulation.

Pharmacokinetics: This refers to the dynamical mechanism by which a drug is absorbed, and processed by the body

In-silico Simulation: The use of a special computer programme to solve the equations of a mathematical model and produce a set of plots of model parameters over time.

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Editorial Advisory Board

Table of Contents

Foreword

Ralf Herwig

Preface

Andriani Daskalaki

Acknowledgment

Andriani Daskalaki

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Chapter 2

Systems and Control Theory for Medical Systems Biology
(pages 11-26)

Peter Wellstead, Sree Sreenath, Kwang-Hyun Cho

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Chapter 3

S. Nikolov

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Chapter 6

Computational Models for the Analysis of Modern Biological Data
(pages 117-125)

Tuan D. Pham

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Chapter 8

Thomas Meinel

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Chapter 9

Nikolaos G. Sgourakis, Pantelis G. Bagos, Stavros J. Hamodrakas

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Chapter 10

Bacterial ß-Barrel Outer Membrane Proteins: A Common Structural Theme Implicated in a Wide Variety of Functional Roles
(pages 182-207)

Pantelis G. Bagos, Stavros J. Hamodrakas

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Chapter 12

Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods
(pages 221-238)

George Sakellaropoulos, Antonis Daskalakis, George Nikiforidis, Christos Argyropoulos

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Chapter 14

The Affymetrix GeneChip® Microarray Platform
(pages 251-261)

Djork-Arné Clevert, Axel Rasche

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Chapter 16

Gene Expression in Microbial Systems for Growth and Metabolism
(pages 278-290)

Prerak Desai

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Chapter 20

Systems Biology Applied to Cancer Research
(pages 339-353)

R. Seigneuric, N.A.W. van Riel, M.H.W. Starmans, A. van Erk

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Chapter 21

Matej Orešic, Antonio Vidal-Puig

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Chapter 23

Systems Biology and Infectious Diseases
(pages 377-402)

Alia Benkahla, Lamia Guizani-Tabbane, Ines Abdeljaoued-Tej, Slimane Ben Miled, Koussay Dellagi

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Chapter 24

Systems Biology of Human-Pathogenic Fungi
(pages 403-421)

Daniela Albrecht, Reinhard Guthke

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Chapter 27

Applications of Metabolic Flux Balancing in Medicine
(pages 458-474)

Ferda Mavituna, Raul Munoz-Hernandez, Ana Katerine de Carvalho Lima Lobato

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Chapter 28

Multi-Level Data Integration and Data Mining in Systems Biology
(pages 476-496)

Roberta Alfieri, Luciano Milanesi

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Chapter 29

Methods for Reverse Engineering of Gene Regulatory Networks
(pages 497-515)

Hendrik Hache

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Chapter 31

Elizabeth Santiago-Cortés

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Chapter 32

Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches
(pages 541-555)

A. Maffezzoli

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Chapter 33

Paolo Vicini

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Chapter 34

Photosynthesis: How Proteins Control Excitation Energy Transfer
(pages 573-587)

Julia Adolphs

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Chapter 37

Interference Microscopy for Cellular Studies
(pages 656-672)

Alexey R. Brazhe, Nadezda A. Brazhe, Alexey N. Pavlov, Georgy V. Maksimov

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Chapter 38

Fluorescence Imaging of Mitochondrial Long-Term Depolarization in Cancer Cells Exposed to Heat-Stress
(pages 673-692)

Cathrin Dressler, Olaf Minet, Urszula Zabarylo, Jürgen Beuthan

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Chapter 39

Protein Interactions and Diseases
(pages 694-713)

Athina Theodosiou, Charalampos Moschopoulos, Marc Baumann, Sophia Kossida

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Chapter 40

Bernard de Bono

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Chapter 42

Model Development and Decomposition in Physiology
(pages 759-797)

Isabel Reinecke, Peter Deuflhard

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Chapter 44

Dengue Fever: A Mathematical Model with Immunization Program
(pages 809-824)

Mohamed Derouich

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About the Contributors

Index