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Transcript
Biological systems and
pathway analysis
An introduction
Protein-Protein Interactions
How does the organisation of living matter in space and time give rise to
biological processes?
Post-genome
informatics:
Prediction of all interactions
Synthesis
gagccagcgagccag
cgcgcgcgggcgggc
ggacagatcggagcc
gagcggggccgggcg
gggcgctccctgcag
+
Systems Biology
Emergence
a behaviour or property
resulting from interaction
and self-organisation
Biological systems are
•modular (sub-systems)
System
•hierarchical
•multiscale (space and
time)
Self-organisation
Agent interactions
Agents
interacting components
(modules), ongoing
relationships, can evolve
molecular complexes, pathways,
networks, tissues
transcription, catalysis, P-P
interaction, cross-talk, cellcell signalling
molecules pathways cells
Global approaches: Systems Biology
Perturbation
Living cell
Dynamic response
time!
Global approaches: Systems Biology
Perturbation
Living cell
Dynamic response
time!
Biological organisation (e.g.,
gene families, structural
genomics, pathways)
“Virtual cell”
-omics time
series
Global approaches: Systems Biology
Perturbation
Living cell
Dynamic response
time!
Biological organisation (e.g.,
gene families, structural
-omics time
series
genomics, pathways)
Bioinformatics
Mathematical
modelling
Simulation
•Basic principles
“Virtual cell”
•Practical
applications
Dynamic Pathway Models
• Forefront of the field of systems biology
• Types
– Inter/intra-cellular
– Metabolic networks
– Gene networks
– Signal transduction networks
• Two types of formalism appearing in the literature:
– data mining
 e.g. genome expression at gene or protein level
 contribute to conceptualisations of pathways
– simulations of established conceptualisations
Dynamic models of cell
signalling
…from pathway interaction
and molecular data
Erk1/Erk2 Mapk
Signaling pathway
…to dynamic models of pathway
function
Schoeberl
et al., 2002
Simulations: Dynamic Pathway Models
Schoeberl et al., 2002, Nat. Biotech. 20: 370
• These have recently come
to the forefront due to
emergence of highthroughput technologies.
• Composed of
theorised/validated
pathways with kinetic data
attached to every
connection - this enables
one to simulate the change
in concentrations of the
components of the pathway
over time given initial
parameters.
Response Models × Signalling Pathways Models?
• Charasunti et al. (2004)
– ODE model of the action of
Gleevec on the Crk-1
pathway in Chronic Myeloid
Leukaemia
Dynamic biochemistry
• Biomolecular interactions
• Protein-ligand interactions
• Metabolism and signal transduction
• Databases and analysis tools
• Metabolic simulation
• Metabolic databases and simulation
• Dynamic models of cell signalling
Types of Modelling Methods
• Stochastic approaches
– Simple statistics
– Bayesian Networks
• Deterministic
– Boolean networks
• ODE approach
– Iterations in a system
• Classification/Clustering
approaches
– Support Vector Machines
– Neural Networks
• Hybrid Models – mixture of the
above
Ideker & Lauffenberger, 2003, TiB 21(6): 255-262
Pathway simulation and analysis software
accessible from http://sbml.org/index.psp
BASIS
BioCharon
Bio Sketch Pad
BioSpreadsheet
BioUML
BSTLab
CADLIVE
CellDesigner
Cellerator
Cellware
Cytoscape
DBsolve
Dizzy
E-CELL
ESS
Gepasi
Jarnac
JDesigner
JigCell
JSIM
JWS
Karyote*
libSBML
MathSBML
MOMA
Monod
NetBuilder
PathArt
PathScout
ProcessDB*
SBW
SCIpath
SigPath
Simpathica
StochSim
STOCKS
TeraSim
Trelis
Virtual Cell
WinSCAMP
www.ucl.ac.uk/oncology/MicroCore/
microcore.htm
Microarrays, Protein Pathways and SCIpath
Microarray Technology
Measure mRNA levels
as approximation of
genome expression
Pathway Information
mRNA
https://www.ucl.ac.uk/oncology/MicroCore/microcore.htm
•Pathway builder
SBML
BioPAX
•MicroExpress maps microarray and
proteomic data to pathway nodes
•External database links
Dynamic information processing in the cancer cell
‘Pathway signatures’
Molecular signatures for pathways
are needed (not only for single
molecules)
A p53 Pathway
A protein in the pathway
Upregulation of the protein’s gene
e.g. this protein is upregulated
in prostate cancer compared to
normal cells
Greater upregulation…
Downregulation of the protein’s gene
Greater downregulation…
Leukaemia types
Leukaemia types
Biomedicine ‘after the human genome’
Patient
Molecular basis of
disease
Current disease
models
Molecular building
blocks
genes
proteins
Biomedicine ‘after the human genome’
Patient
Physiology
Clinical data
Molecular basis of
disease
Current disease
models
Molecular building
blocks
genes
proteins
Biomedicine ‘after the human genome’
Patient
Complex
disease models
Disease
manifestation in
organs, tissues,
cells
Computational
modelling
Molecular building
blocks
genes
proteins
Molecular
organisation
Tumour Systems Modelling
• Four major areas of
focus:
GROWTH
RESPONSE
ANGIOGENESIS
PATHWAYS
delivery
flow
–
–
–
–
Growth
Treatment Response
Pathway
Angiogenesis
• Levels of granularity
and scope
– Phenomenological 
mechanistic
Growth Models
• Stamatakos et al.
(1998)
Proliferating:
– 3D simulation
– Satisfactory
agreement with in vitro
experiments
Mitosis:
Necrotic
Products:
t = 240h
G0 Phase:
Necrosis or
Apoptotic
Irradiation starts at t = 168h
Cellular Automata (CA) approaches
Determination of local
interaction rules
G 1 S G2 M
G0
N
Cell
death
Stamatakos et al.
1998, 2001
Stamatakos et al…
Flowchart for the
response of a single
tumour cell to irradiation.
Stamatakos et al., 2001, IEEE Trans. Inf. Tech. Biomed. 5(4)
Physiome project
“Virtual human”
Simulation of complex models of cells, tissues and organs
•40 years of mathematical modeling of electrophysiology and tissue
mechanics
•New models will integrate large-scale gene expression profiles
http://www.physiome.org/
Physiome project
patient
organ
Anatomy and integrative
function, electrical dynamics
Vessels, circulatory flow,
exchanges, energy metabolism
cell
Cell models, ion fluxes, action
potential, molecules, functional
genomics