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Systems biology – dynamic pathway modeling – Olaf Waulkenhour

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Abstract
Review: Dynamic Pathway Modelling . . . . . . . . . . . . . . . . . 149 5.6 Metabolism-Repair Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 6 System Identification 154 6.1 Vital Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 iv CONTENTS 6.1.1 Visualizing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.1.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.2 Finding Associations: Correlation . . . . . . . . . . . . . . . . . . . . . . . . 154 6.3 Parameter Estimation: Regression . . . . . . . . . . . . . . . . . . . . . . . 154 6.3.1 Least Squares Estimation . . . . . . . . . . . . . . . . . . . . . . . . 154 6.3.2 Example: Rate Constants from Experimental Data . . . . . . . . . . 154 6.3.3 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . 154 6.4 Multiple Shooting Parameter Estimation . . . . . . . . . . . . . . . . . . . . 154 6.5 Example: The JAK-STAT Pathway . . . . . . . . . . . . . . . . . . . . . . 154 6.6 Untangling the Wires of a Network . . . . . . . . . . . . . . . . . . . . . . . 154 6.6.1 Network Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.6.2 Perturbation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 A Glossary 155 B Notation 162 1 Preface The areas of genomics and bioinformatics have identified and characterized many of the components that make up a living cell and maintain its function. A primary aim of bioinformatics has been to link genome sequences or genes with RNA products and proteins, i.e., to determine whether in a particular experimental context there exist a relationship between genes and proteins, amongst…

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