Issues for metabolomics and

Issues for metabolomics and systems biology

Douglas KellDouglas KellSchool of Chemistry, University of Manchester, School of Chemistry, University of Manchester,

MANCHESTER M60 1QD, U.K.MANCHESTER M60 1QD, U.K.

[email protected]@manchester.ac.ukhttp://http://dbkgroup.orgdbkgroup.org//

http://www.mib.ac.uk www.mcisb.org

What I can’t do now and would like to

Some facts I ‘know’ (i.e. think I can remember…)

• Epidemiologically, statins enhance longevity

• Cholesterol is barely a risk factor when within the normal range of 120-240 mg%

• Statins supposedly act (only) via HMG-CoA reductase to lower cholesterol

• Actually many have (and from the above logically must have) off-target effects

More ‘facts’• Although originating as natural products,

many/most statins can bear comparatively little structural relationships to them or to each other

• Are there QSAR-type relations between the various off-target effects and the drugs that cause them?

atorvastatinlovastatin

The software tool I want would integrate all of those questions by:

• Finding the facts from the literature (and the Web) by reading the articles ‘intelligently’

• Displaying and setting out the facts sensibly• Allowing the QSARs directly from the papers

as the structures and substructures would be ‘known’ (or knowlable via PubChem, DrugBank etc)

• Classify/cluster the off-target effects and the papers that described them (via TM and ML)

• Without me having to write any actual code

Westerhoff & Palsson NBT 22, 1249-52 (2004)

But despite everything science is in some ways becoming LESS effective in an applied context

Declining numbers of drug launches

Leeson & Springthorpe, NRDD 6, 881-890 (2007)

Drug Discovery/Development Pipeline

• Multifaceted, complicated, lengthy process

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Peter S. Dragovich, Pfizer

Attrition

Kola & Landis, NRDD 3, 711-5 (2004)

Issues of attrition

• PK/PD less of an issue in last decade • Now mostly due to (i) lack of efficacy, (ii) toxicity• Both problems are underpinned by the fact that

drugs are typically first developed on the basis of molecular assays before being tested in the intact system

• These failures turn drug discovery – if it was not already – into a problem of systems biology

Nature Rev Drug Disc 7, 205-220 (March 2008)

Poor correlation between different artificial membrane (Corti & PAMPA) assays

Corti et al EJ Pharm Sci 7, 354-362 (2006)

Poor correlation between Caco-2 cells and artificial membrane (PAMPA) assays

Note axis scales

Balimane et al., AAPS J 8, e1-e13 (2006)

Poor relationship between PAMPA permeability and log Ko/w

Corti et al. EJ Pharm Sci 7, 354-362 (2006)

Poor relationship between Caco-2 permeability and log Ko/w

Corti et al. EJ Pharm Sci 7, 354-362 (2006)

r2 = 0.097

THESE THEORIES OF DRUG UPTAKEWERE BIOPHYSICAL, ‘LIPID-ONLY’

THEORIES

Narcotics (‘general anaesthetics’)

• Potency also correlates with log P (up to a cut-off) (Meyer & Overton)

• Negligible structure-activity relationships• Was assumed that they also act by a

‘biophysical’ mechanism by partitioning ‘nonspecifically’ into membrane and e.g. ‘squeezing’ nerve channels

• This too was a ‘lipid-only’ theory• None of this now stands up

Anaesthetic potency does largely correlate with partitioning into membrane, suggesting (to

many) a ‘lipid-only’ mechanism

P. Seeman, Pharmacol Rev 24, 583-655 (1972)

But…narcotics inhibit luciferase, a soluble protein, with the same potency with which

they anaesthetise animals, over 5 logs!

Franks & Lieb, Nature 310, 599-601 (1984)

No lipid involved!

The structural basis is known

Franks et al, Biophys J 75, 2205-11 (1998)

Binding of bromoform to luciferase

Halothane affects narcosis in part via a TREK-1 K+ channel

Heurteaux et al. EMBO J 23, 2684-95 (2004)

How to integrate all this information with biological and

physiological networks?

• One strategy is Integrative Systems Biology

One view of systems biology

Computation/Modelling

Experiment

TechnologyTheory

Bringing together metabolomics and systems biology models

Drug Discovery Today 11, 1085-1092 (2006)

There is a convergence between systems biology models from whole-genome reconstruction and the number of

experimental metabolome peaks (ca 3000 for human serum)

The human metabolic network (1)

• 8 cellular compartments• 2,712 compartment-specific metabolites• ~ 1,500 different chemical entities• 1,496 genes• 2,233 metabolic reactions (1,795 unique)• 1,078 transport reactions (32.6%)

PNAS 104, 1777-1782 (2007)

The human metabolic network (2)

• Not yet compartmentalised• 2,823 reactions (incl 300 ‘orphans’), of which 2,215

have disease associations, plus 1189 transport reactions and 457 exchange reactions

• 2,322 genes (1069 common with Palsson model)

Molecular Systems Biology 3, 135 (2007)

Systems biology and modelling are all about representation

The main representation for systems biology models is SBML

www.sbml.org

BIOCHEMICAL MODEL (assumed to

be in SBML)

Store in dB

create

Compare with other models

VISUALISE

Layouts and views

SBGN

Overlays, dynamics

LINK WORKFLOWS

Soaplab, Taverna,

Web services, etc.

Store results of manipulations

Compare with and fit to real data (parameters and

variables) with constraints

How to deal with fitting, including as f(globalparameters like pH)

Integrate various levels

Run, analyse (sensitivities, etc)

Automatic characterisation of parameter space and

constraint checking

Model merging: (not) LEGO blocks

Optimal DoE for Sys Identification, incl identifiability

edit

Network Motif discovery

Literature mining

Cheminformatic analysesTHERE ARE MANY POSSIBLE THINGS THAT ONE

MIGHT DO WITH THIS REPRESENTATION, AND THESE ACTIONS CAN BE SEEN AS MODULES

BIOCHEMICAL MODEL (assumed to

be in SBML)Compare with and fit to real

data (parameters and variables) with constraints

Compare with and fit to real data (parameters and

variables) with constraints

FEBS J 274, 5576-5585

4, 74-97

The Data Management Infrastructure of the Manchester Centre for Integrated

Systems Biology

Norman PatonUniversity of Manchester

Capabilities

• We require software to support:– Data capture: Pedro.– Data access: Pierre.– Integration of data and analyses: Taverna.

Pipeline Pilot workflow

etc…

METABOLIC MODEL IN SBML

CREATE MODEL

VISUALISE

STORE MODEL IN

DB

RUN BASE MODEL

SENSITIVITY ANALYSES

SCAN PARAMETER

SPACE

COMPARE WITH ‘REAL’ DATA

DIFFERENT METABOLIC

MODEL IN SBML

LITERATURE MINING ANNOTATE

STORE NEW MODEL IN DB

COMPARE MODELS

STORE DIFFERENCES AS

NEW MODEL IN DBSYSTEMS BIOLOGY WORKFLOWS

Scientists Decoupled suppliers & consumers

Collaboration

Knowledge

Management

Science

‘Warehouse’ vs distributed workflows

• Different ‘modules’ developed in different labs can reside on different computers anywhere, and expose themselves as Web Services

• Labs can then specialise in what they are best at• All that is then needed is an environment for enacting

bioinformatic workflows by coupling together these service-oriented architectures

• One such is Taverna• This is arguably the best way to combine metabolomic

SBML models with metabolomic data, and is what are using at MCISB

Overall Architecture

Experiment1 Experimentn…

Repository1 Repositoryn…

Model

Repository

Analysis1

Analysisn

Consistent Web Interfaces

Consistent Web Service Interfaces

Data Integration

Using Workflows

Workflow

Repository

The Taverna API consumer along with libSBML allows many of these

transformations to be performed

Details: http://www.mcisb.org/software/taverna/libsbml/index.html

Relating Models to Expression Read gene names of enzymes from SBML model

Query maxd transcriptome database using gene names

Compute colour for expression readings

Create new SBMLmodel

Visualise Models Using Cell Designer

JC_C-0.07-1_Measurement JC_N-0.07-1_Measurement

Potential Solutions• Semantic annotation• Chemical and bio-text mining• RDF annotations – that can also be included

within the SBML• Integrated reasoning engine• Allowing literature-based discovery• But we still lack a proper and useful

(bio)chemical ontology integrating roles, pathways, diseases, chemical (sub)structures, targets, etc.

• This last is probably the most damaging lack and thus most important need

Issues for metabolomics and systems biology

Douglas KellDouglas KellSchool of Chemistry, University of Manchester, School of Chemistry, University of Manchester,

MANCHESTER M60 1QD, U.K.MANCHESTER M60 1QD, U.K.

[email protected]@manchester.ac.ukhttp://http://dbkgroup.orgdbkgroup.org//

http://www.mib.ac.uk www.mcisb.org