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Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge.

Bibliographic Details
Main Author: Johansson, Rikard.
Format: eBook
Language:English
Published: Linköping : Linkopings Universitet, 2017.
Edition:1st ed.
Series:Linköping Studies in Science and Technology. Dissertations Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Supervisor
  • Co-Supervisors
  • Faculty Opponent
  • Abstract
  • Svensk sammanfattning
  • Publications and Manuscripts
  • Abbreviations
  • Mathematical symbols
  • Table of Contents
  • 1 Introduction
  • 1.1 Complexity
  • 1.2 The Book of Life: from DNA to protein
  • 1.3 Omics
  • 1.4 Personalized medicine
  • 1.5 Systems biology
  • 1.6 Aim and scope
  • 1.7 Outline of thesis
  • 2 Science Through Hypothesis Testing
  • 2.1 Facts, hypotheses, and theories
  • 2.2 Verifications and falsifications
  • 3 Mathematical Modeling
  • 3.1 Modelling definitions and concepts
  • 3.1.1 Model properties
  • 3.1.2 Modeling frameworks
  • 3.2 Ordinary differential equations
  • 3.3 Black box modeling and regression models
  • 3.4 Networks and data-driven modeling
  • 3.5 Partial differential equations
  • 3.6 Stochastic modeling
  • 4 ODE Modeling Methods
  • 4.1 The minimal model and modeling cycle approach
  • 4.2 Model construction
  • 4.2.1 Hypothesis and data
  • 4.2.2 Scope and simplifications
  • 4.2.3 Reaction kinetics and measurement equations
  • 4.2.4 Units
  • 4.3 Model simulation
  • 4.3.1 Runge-Kutta, forward Euler, and tolerance
  • 4.3.2 Adams-Bashforth
  • 4.3.3 Adams-Moulton
  • 4.3.4 Backward Differentiation Formulas
  • 4.3.5 On Stiffness and software
  • 4.4 Parameter estimation and goodness of fit
  • 4.4.1 Objective function
  • 4.4.2 Cost landscape
  • 4.4.3 Local optimization
  • Steepest descent, Newton, and quasi-Newton
  • Nelder-Mead downhill simplex
  • 4.4.4 Global Optimization
  • Multi-start optimization
  • Simulated annealing
  • Evolutionary algorithms
  • Particle swarm optimization
  • 4.5 Statistical assessment of goodness of fit
  • 4.5.1 The χ2-test
  • 4.5.2 Whiteness, run, and Durbin-Watson test
  • 4.5.3 Interpretation of rejections
  • 4.6 Uncertainty analysis
  • 4.6.1 Model uncertainty
  • 4.6.2 Parameter uncertainty
  • Sensitivity analysis.
  • Fisher information matrix
  • Identifiability and the profile likelihood
  • 4.6.3 Prediction uncertainty
  • 4.7 Testing predictions
  • 4.7.1 Core predictions
  • 4.7.2 Validation data
  • 4.7.3 Overfitting
  • 4.8 Model selection
  • 4.8.1 Experimental design and testing
  • 4.8.2 Ranking methods and tests
  • Information criterion
  • The likelihood ratio test
  • 4.9 Bootstrapping and empirical distributions
  • 5 Model Systems
  • 5.1 Insulin signaling system in human adipocytes
  • 5.2 Cell-to-cell variability in yeast
  • 5.3 Facilitation in murine nerve cells
  • 6 Results
  • 6.1 Modeling of dominant negative inhibition data
  • 6.2 Quantification of nuclear transport rates in yeast cells
  • 6.3 Quantitative modeling of facilitation in pyramidal neurons
  • 6.4 A novel method for hypothesis testing using bootstrapping
  • 7 Concluding Remarks
  • 7.1 Summary of results and conclusions
  • 7.1.1 DN data should be analyzed using mathematical modeling
  • 7.1.2 A single-cell modeling method for quantification of nuclear transport
  • 7.1.3 Facilitation can be explained by a single mechanism
  • 7.1.4 A novel 2D bootstrap approach for hypothesis testing
  • 7.2 Relevancy of mathematical modeling
  • 7.2.1 Hypothesis testing
  • 7.2.2 Mechanistic understanding
  • 7.2.3 Design of experiments
  • 7.2.4 Data analysis
  • 7.2.5 Healthcare
  • 7.3 Outlook
  • Acknowledgements
  • References
  • Endnotes.

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