Systems and Control Theory for Medical Systems Biology

Systems and Control Theory for Medical Systems Biology

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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Abstract

In this chapter the authors describe systems and control theory concepts for systems biology and the corresponding implications for medicine. The context for a systems approach to the life sciences is outlined, followed by a brief history of systems and control theory. The technical aspects of systems and control theory are then described in a way oriented toward their biological and medical application. This description is then used as a reference base against which to indicate specific areas where systems and control theory aspects of systems biology have strong medical implications. Specifically, two systems biology projects are described as examples of where methods from systems and control theory play an important role.
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Introduction

In 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.

Key Terms in this Chapter

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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Table of Contents
Foreword
Ralf Herwig
Preface
Andriani Daskalaki
Acknowledgment
Andriani Daskalaki
Chapter 1
Peter Ghazal
An increasing number of biological experiments and more recently clinical based studies are being conducted using large-scale genomic, proteomic and... Sample PDF
Pathway Biology Approach to Medicine
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Chapter 2
Peter Wellstead, Sree Sreenath, Kwang-Hyun Cho
In this chapter the authors describe systems and control theory concepts for systems biology and the corresponding implications for medicine. The... Sample PDF
Systems and Control Theory for Medical Systems Biology
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Chapter 3
S. Nikolov
In this chapter we investigate how the inclusion of time delay alters the dynamic properties of (a) delayed protein cross talk model, (b) time delay... Sample PDF
Mathematical Description of Time Delays in Pathways Cross Talk
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Chapter 4
Elisabeth Maschke-Dutz
In this chapter basic mathematical methods for the deterministic kinetic modeling of biochemical systems are described. Mathematical analysis... Sample PDF
Deterministic Modeling in Medicine
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Chapter 5
Andrew Kuznetsov
Biologists have used a reductionist approach to investigate the essence of life. In the last years, scientific disciplines have merged with the aim... Sample PDF
Synthetic Biology as a Proof of Systems Biology
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Chapter 6
Tuan D. Pham
Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent... Sample PDF
Computational Models for the Analysis of Modern Biological Data
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Chapter 7
Vanathi Gopalakrishnan
This chapter provides a perspective on 3 important collaborative areas in systems biology research. These areas represent biological problems of... Sample PDF
Computer Aided Knowledge Discovery in Biomedicine
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Chapter 8
Thomas Meinel
The function of proteins is a main subject of research in systems biology. Inference of function is now, more than ever, required by the upcoming of... Sample PDF
Function and Homology of Proteins Similar in Sequence: Phylogenetic Profiling
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Chapter 9
Nikolaos G. Sgourakis, Pantelis G. Bagos, Stavros J. Hamodrakas
GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of... Sample PDF
Computational Methods for the Prediction of GPCRs Coupling Selectivity
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Chapter 10
Pantelis G. Bagos, Stavros J. Hamodrakas
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Bacterial ß-Barrel Outer Membrane Proteins: A Common Structural Theme Implicated in a Wide Variety of Functional Roles
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Chapter 11
L.K. Flack
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Clustering Methods for Gene-Expression Data
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Chapter 12
George Sakellaropoulos, Antonis Daskalakis, George Nikiforidis, Christos Argyropoulos
The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be... Sample PDF
Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods
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Chapter 13
Wasco Wruck
This chapter describes the application of the BeadArrayTM technology for gene expression profiling. It introduces the BeadArrayTM technology, shows... Sample PDF
Gene Expression Profiling with the BeadArrayTM Platform
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Chapter 14
Djork-Arné Clevert, Axel Rasche
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The Affymetrix GeneChip® Microarray Platform
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Chapter 15
Jacek Majewski
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Chapter 16
Prerak Desai
The use of systems biology to study complex biological questions is gaining ground due to the ever-increasing amount of genetic tools and genome... Sample PDF
Gene Expression in Microbial Systems for Growth and Metabolism
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Chapter 17
Heike Stier
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Alternative Splicing and Disease
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Chapter 18
Axel Kowald
Aging is a complex biological phenomenon that practically affects all multicellular eukaryotes. It is manifested by an ever increasing mortality... Sample PDF
Mathematical Modeling of the Aging Process
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Chapter 19
Evgenia Makrantonaki
This chapter introduces an in vitro model as a means of studying human hormonal aging. For this purpose, human sebaceous gland cells were maintained... Sample PDF
The Sebaceous Gland: A Model of Hormonal Aging
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Chapter 20
R. Seigneuric, N.A.W. van Riel, M.H.W. Starmans, A. van Erk
Complex diseases such as cancer have multiple origins and are therefore difficult to understand and cure. Highly parallel technologies such as DNA... Sample PDF
Systems Biology Applied to Cancer Research
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Chapter 21
Matej Orešic, Antonio Vidal-Puig
In this chapter the authors report on their experience with the analysis and modeling of data obtained from studies of animal models related to... Sample PDF
Systems Biology Strategies in Studies of Energy Homeostasis In Vivo
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Chapter 22
Axel Rasche
We acquired new computational and experimental prospects to seek insight and cure for millions of afflicted persons with an ancient malady. Type 2... Sample PDF
Approaching Type 2 Diabetes Mellitus by Systems Biology
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Chapter 23
Alia Benkahla, Lamia Guizani-Tabbane, Ines Abdeljaoued-Tej, Slimane Ben Miled, Koussay Dellagi
This chapter reports a variety of molecular biology informatics and mathematical methods that model the cell response to pathogens. The authors... Sample PDF
Systems Biology and Infectious Diseases
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Chapter 24
Daniela Albrecht, Reinhard Guthke
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Systems Biology of Human-Pathogenic Fungi
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Chapter 25
Jessica Ahmed
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Chapter 26
Paul Wrede
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Chapter 27
Ferda Mavituna, Raul Munoz-Hernandez, Ana Katerine de Carvalho Lima Lobato
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Applications of Metabolic Flux Balancing in Medicine
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Chapter 28
Roberta Alfieri, Luciano Milanesi
This chapter aims to describe data integration and data mining techniques in the context of systems biology studies. It argues that the different... Sample PDF
Multi-Level Data Integration and Data Mining in Systems Biology
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Chapter 29
Hendrik Hache
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Methods for Reverse Engineering of Gene Regulatory Networks
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Chapter 30
Alok Mishra
This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data... Sample PDF
Data Integration for Regulatory Gene Module Discovery
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Chapter 31
Elizabeth Santiago-Cortés
Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their... Sample PDF
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Chapter 32
A. Maffezzoli
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Chapter 33
Paolo Vicini
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The System for Population Kinetics: Open Source Software for Population Analysis
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Chapter 34
Julia Adolphs
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Photosynthesis: How Proteins Control Excitation Energy Transfer
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Chapter 35
Michael R. Hamblin
Photodynamic therapy (PDT) is a rapidly advancing treatment for multiple diseases. PDT involves the administration of a nontoxic drug or dye known... Sample PDF
Photodynamic Therapy: A Systems Biology Approach
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Chapter 36
Andriani Daskalaki
Photodynamic Therapy (PDT) involves administration of a photosensitizer (PS) either systemically or locally, followed by illumination of the lesion... Sample PDF
Modeling of Porphyrin Metabolism with PyBioS
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Chapter 37
Alexey R. Brazhe, Nadezda A. Brazhe, Alexey N. Pavlov, Georgy V. Maksimov
This chapter describes the application of interference microscopy and double-wavelet analysis to noninvasive study of cell structure and function.... Sample PDF
Interference Microscopy for Cellular Studies
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Chapter 38
Cathrin Dressler, Olaf Minet, Urszula Zabarylo, Jürgen Beuthan
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Fluorescence Imaging of Mitochondrial Long-Term Depolarization in Cancer Cells Exposed to Heat-Stress
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Chapter 39
Athina Theodosiou, Charalampos Moschopoulos, Marc Baumann, Sophia Kossida
In previous years, scientists have begun understanding the significance of proteins and protein interactions. The direct connection of those with... Sample PDF
Protein Interactions and Diseases
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Chapter 40
Bernard de Bono
From a genetic perspective, disease can be interpreted in terms of a variation in molecular sequence or expression (dose) that impairs normal... Sample PDF
The Breadth and Depth of BioMedical Molecular Networks: The Reactome Perspective
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Chapter 41
Jorge Numata
Thermodynamics is one of the best established notions in science. Some recent work in biomolecular modeling has sacrificed its rigor in favor of... Sample PDF
Entropy and Thermodynamics in Biomolecular Simulation
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Chapter 42
Isabel Reinecke, Peter Deuflhard
In this chapter some model development concepts can be used for the mathematical modeling in physiology as well as a graph theoretical approach for... Sample PDF
Model Development and Decomposition in Physiology
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Chapter 43
Mohamed Derouich
Throughout the world, seasonal outbreaks of influenza affect millions of people, killing about 500,000 individuals every year. Human influenza... Sample PDF
A Pandemic Avian Influenza Mathematical Model
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Chapter 44
Mohamed Derouich
Dengue fever is a re-emergent disease affecting more than 100 countries. Its incidence rate has increased fourfold since 1970 with nearly half the... Sample PDF
Dengue Fever: A Mathematical Model with Immunization Program
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Chapter 45
Ross Foley
The field of histopathology has encountered a key transition point with the progressive move towards use of digital slides and automated image... Sample PDF
Automated Image Analysis Approaches in Histopathology
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About the Contributors