Simulation of signal transduction pathways [Elektronische Ressource] / Marco Weismüller

Sujets

• Informatik
• Biologie

Informations

• Publié par: universitat_ulm
• Publié le: 01 janvier 2004
• Nombre de lectures: 8
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Deutsches Krebsforschungszentrum Heidelberg
Abteilung Theoretische Bioinformatik
Simulation of
Signal Transduction
Pathways
DKFZ, Heidelberg Universitat Ulm
Dissertation
zur Erlangung des Doktorgrades Dr.rer.nat.
der Fakultat fur Informatik der Universitat Ulm
Marco Weismuller
aus Heidelberg
2004Amtierender Dekan: Prof. Dr. Friedrich W. von Henke
1. Gutachter: Prof. Dr. Uwe Sch oning
2. Gutachter: Prof. Dr. Peter Durre
Externer Gutachter: Prof. Dr. Edgar Wingender
Tag der Promotion: 16. Juli 2004Abstract
Signal transduction is about how molecular mechanisms in cells are used to receive,
process and respond to signals from outside and inside of the cell. The term signal is
de ned in a dual way in this context: being the molecular state of a molecule, e.g. a
protein, and at the same time an abstract logical state (active/inactive).
This thesis focuses on intracellular signalling: i.e. receiving some kind of stimuli (e.g.
hormones) from outside, integrating this information with the state of the cell, and
passing this information into the nucleus to regulate gene expression by transcription
factors as the response to the stimulus.
In recent years a huge amount of data has been accumulated in various biological
databases. These data are used for quantitative analysis of small, well-de ned signal
networks.
The dissertation follows the new approach of qualitative simulation of signal transduction
on whole-cell scale. In collaboration with the company Biobase/Wolfenbuttel, Germany
the signal transduction database TRANSPATH Professional is used to gain access to
qualitative, manually annotated textual information from the scienti c literature.
For qualitative simulation of signal transduction pathways a model of interacting sig-
nal molecules is established, similar to the information structure of TRANSPATH .
Signalling behaviour is modelled in a process-oriented, as well as in an object-oriented
way. Signal molecules are represented as entities that have variables to keep their con-
formation status or biochemical alterations, and as parameterised procedures to com-
municate with each other.
In a rst approach the established symbolic signal model was implemented in the
-calculus. A simulation software, based on this theory and called PsiFCP, is used to
simulate the well characterised Egf-Mapk signal pathway.
As intermediate result of the simulation with thePsiFCP system, software requirements
for simulation of signal transduction networks are formulated.
In a second attempt the object-oriented signal model was implemented in an agent-
based simulation environment, called Swarm system. An extended Egf-Mapk signal
pathway is reimplemented in the programming language Objective C to feed theSwarmsimulation system with local, binary interaction data of TRANSPATH 4.2. Swarm
then dynamically processes the signal o w using a stochastic scheduling mechanism,
which was developed to this end. Simulated models can be analysed by inspecting
signal o w and scheduling traces, and the shortest path between two signal molecules
can be computed.
As nal result, shortest paths are computed between varying molecules of the manually
imported Egf-Mapk pathway.Contents
1 Introduction 1
2 Mechanisms of Biological Networks in Cells 6
2.1 Basic Concepts of Biological Networks . . . . . . . . . . . . . . . . . . . 6
2.2 Mechanisms of Metabolic Networks . . . . . . . . . . . . . . . . . . . . . 10
2.3 The Biology of Signal Transduction . . . . . . . . . . . . . . . . . . . . . 12
2.3.1 Types of Signal Processes . . . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Types of Signal Molecules . . . . . . . . . . . . . . . . . . . . . . 14
2.3.3 Types of Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.4 Observed Signal Concepts . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Genetic Network Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Biology of the Egf-Mapk Prototype Pathway . . . . . . . . . . . . . . . . 22
3 Databases for Signal Transduction and Protein-Protein Interactions 28
3.1 Why Databases? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2 Evaluation of Available Databases . . . . . . . . . . . . . . . . . . . . . . 29
3.3 TRANSPATH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Information Structure of TRANSPATH . . . . . . . . . . . . . . 34
4 Modelling and Simulating Biological Networks 42
4.1 Introduction into Simulation Methods . . . . . . . . . . . . . . . . . . . . 42
4.2 Modelling Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Simulation Approaches and Systems . . . . . . . . . . . . . . . . . . . . . 47
4.3.1 Qualitative Simulation . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.2 Quantitative Sim . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.3 Quane Stochastic Approaches . . . . . . . . . . . . . . . . . 52
4.3.4 Simulation Models of ST Networks . . . . . . . . . . . . . . . . . 57
4.4 Models for ST Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.1 General Concepts of the ST Model . . . . . . . . . . . . . . . . . 59
iContents
5 Simulation of Biological Networks Using Calculi 69
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.1.1 The Chemical Abstract Machine . . . . . . . . . . . . . . . . . . . 69
5.1.2 Bio-calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.1.3 The -calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.4 PsiFCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 The Symbolic ST Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.3 Implementing the Symbolic ST Model into the -calculus . . . . . . . . . 79
5.4ting the Egf-Mapk Pathway in PsiFCP . . . . . . . . . . . . . 82
5.5 Results and Discussion of PsiFCP Simulation Runs . . . . . . . . . . . . 83
5.6 Requirements for Simulating the ST Model . . . . . . . . . . . . . . . . . 85
6 Object-oriented Simulation of ST Networks 87
6.1 Object-oriented and Agent-based Modelling . . . . . . . . . . . . . . . . 87
6.2 Obted and Agen Model Approaches for Biological Networks 89
6.3 Implementation Considerations . . . . . . . . . . . . . . . . . . . . . . . 91
6.4 The Object-oriented ST Models . . . . . . . . . . . . . . . . . . . . . . . 93
6.4.1 The SignalRate Model . . . . . . . . . . . . . . . . . . . . . . . 96
6.4.2 The SignalFlowParticle Model . . . . . . . . . . . . . . . . . 98
6.5 Introduction to the Swarm Simulation System . . . . . . . . . . . . . . 101
6.6 Implementing the Object-oriented ST Models in Swarm . . . . . . . . . 103
6.6.1 De ned Classes and Protocols . . . . . . . . . . . . . . . . . . . . 104
6.6.2 Signal Flow Algorithms and Inference . . . . . . . . . . . . . . . . 111
6.7 Simulations with the Object-oriented ST Models in Swarm . . . . . . . . 118
6.7.1 The Extended Egf-Mapk Pathway . . . . . . . . . . . . . . . . . . 120
6.7.2 Results and Discussion of Swarm Simulation Runs . . . . . . . . 120
7 General Discussion 124
7.1 Application of Simulating Signal Transduction Networks . . . . . . . . . 124
7.2 State of the Art of Simulating Signal T Pathways . . . . . . . 126
7.3 Discussion of Developed ST Models . . . . . . . . . . . . . . . . . . . . . 127
7.4 General Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8 Zusammenfassung (Abstract in German) 133
A TRANSPATH 135
A.1 TRANSPATH Molecule and Reaction Database Entries . . . . . . . . . 135
B PsiFCP 143
B.1 EGF Signalling Example in PsiFCP . . . . . . . . . . . . . . . . . . . . 143
B.2 Sketch of a Simulation Run of the Egf-Mapk Pathway in PsiFCP . . . . 147
B.3 PsiFCP Program Code . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
iiContents
C Swarm 159
C.1 Simulation Run of the SignalRate Model in Swarm . . . . . . . . . . 160
C.2 Sim Runs of the SignalFlowParticle Model in Swarm . . . . 166
C.2.1 Molecule and Reaction Objects of the Extended Egf-Mapk Pathway166
C.2.2 Inferred Signal Flow of the SignalFlowParticle Model . . . . 171
Glossary 178
Bibliography 195
Acknowledgements 206
iii