Biological pathway and systems analysis

An introduction

Molecular basis of disease

Biomedicine ‘after the human genome’

Current disease models

Patient

Molecular building blocks

proteinsgenes

very data-rich about genes, genome organisation, proteins, biochemical function of individual biomolecules

Molecular basis of disease

Patient

Molecular building blocks

proteinsgenes

Current disease models

Physiology

Clinical data

Disease manifestation inorgans, tissues,

cells

Molecular organisation

?

Computational

modelling

Complex disease models

Patient

Molecular building blocks

proteinsgenes

Disease manifestation inorgans, tissues,

cells

Molecular organisation

physiology, clinical data

tissues

organs

Living cell

“Virtual cell”

Perturbation Dynamic response

•Basic principles

•Applied uses, e.g. drug design

Global approaches: Systems Biology

Bioinformatics

Mathematical modelling

Simulation

cell network modelling

Dynamic biochemistry

• Biomolecular interactions• Protein-ligand interactions• Metabolism and signal transduction• Databases and analysis tools

• Metabolic and signalling simulation• Metabolic databases and simulation• Dynamic models of cell signalling

Dynamic Pathway Models

• Forefront of the field of systems biology• Main types

Metabolic networksGene networksSignal 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

…from pathway interaction and molecular data

…to dynamic models of pathway function

Schoeberl et al., 2002

Dynamic models of cell signalling

Erk1/Erk2 Mapk Signaling pathway

Simulations: Dynamic Pathway Models

• These have recently come to the forefront due to emergence of high-throughput technologies.

• Composed of theorised/validated pathways with kinetic data attached to every biochemical reaction

- this enables one to simulate the change in concentrations of the components of the pathway over time given initial parameters.

• These concentrations underlie cell behaviour.

Schoeberl et al (2002) Nat. Biotech 20: 370

Epidermal growth factor (EGF) pathway

The epidermal growth factor receptor (EGFR) pathway

The effect of the number of active EGFR molecules on ERK activation

Schoeberl et al., 2002, Nat. Biotech. 20: 370

500,000 active receptors

50,000 active receptors =

Inhibition by one order of magnitude

EGFR

PLC Ras PI3K

PKC MAPK PKB/Akt

TFs Functional targets

CELL GROWTH AND PROLIFERATION

ERK

The effect of active EGFR number on ERK activation

500,000 active receptors

50,000 active receptors

Can this be achieved by receptor inactivation alone?

The effect of active EGFR number on ERK activation

50,000 active receptors

with normal levels of ERK

or

ERK overexpression and cross-activation

Hunter and Borg (2003)

Virtual Physiological Human

Simulation of complex models of cells, tissues and organs

www.vph-noe.eu

•Heart modelling: 40+ years of mathematical modeling of electrophysiology and tissue mechanics

•New models integrate molecular mechanisms and large-scale gene expression profiles

Multi-level modelling

cell

organ

patient

Anatomy and integrative function, electrical dynamics

Vessels, circulatory flow, exchanges, energy metabolism

Cell models, ion fluxes, action potential, molecules, functional genomics

integration across scales through computational modelling

Spatial distribution of key proteins

• Transmural expression differences of an ion channel protein leads to different action potential profiles at the epicardium, midwall and endocardium

• Arrhythmias

Hunter et al (2005) Mechanisms of Ageing and Development 126:187–192.

Virtual Physiological Human Projectwww.vph-noe.eu/

The Virtual Physiological Human

https://www.youtube.com/watch?v=CM76-mS84Xs

The hallmarks of systems biology formulate a general or specific question define the components of a biological system collect previous relevant datasets integrate them to formulate an initial model of the system generate testable predictions and hypotheses systematically perturb the components of the system

experimentally or through simulation study the results compare the responses observed to those predicted by the

model refine the model so that its predictions fit best to the

experimental observations conceive and test new experimental perturbations to

distinguish between the multiple competing hypotheses iterate the process until a suitable response to the initial

question is obtained