The Subliminal Toolbox: automating steps in the reconstruction of metabolic networks

The Subliminal Toolbox: automating steps in the reconstruction of metabolic networks Neil Swainston Manchester Centre for Integrative Systems Biology Mendes meeting 13 January 2011

Metabolic reconstructions Computational and mathematical representation of the metabolic capabilities of a given organism Consists of… Metabolic reactions Gene–protein–reaction relationships Compartmentalisation Reaction directionality Objective function(s)

Metabolism

Uses of metabolic reconstructions Metabolic engineering Genome-annotation Evolutionary studies Network property analysis Interpretation of ‘ omics datasets Compendium / source for smaller, kinetic models

Requirements Comprehensive “ Genome-scale ” Connected Minimise gaps , blocked reactions Predictive Produce biologically relevant results Gene-essentiality studies

How are they generated?

How are they generated? Start from… Existing reconstructions (generate a consensus) Genome sequence Infer metabolic reactions through gene homology Existing resources KEGG MetaCyc Provides a first-draft

Next steps Ensure consistent naming of metabolites / enzymes Allows merging Assign genes / proteins to reactions Ensure mass / charge balancing Add reaction directionality Add compartmentalisation Add annotation EC terms, PubMed references, confidence scores

Jamborees

Automation Many of these steps can be automated Subliminal Toolbox Goal is to generate a metabolic reconstruction automatically Manual curation still necessary BUT reduce what needs to be done Investigation Can we automate the generation of a metabolic network in yeast?

KEGG MetaCyc Merge pathways Balance reactions Format for COBRA Add transport reactions Draft (De)protonate metabolites Balance reactions (De)protonate metabolites Merge Add transport proteins Add biomass reaction

Initial draft Both KEGG and MetaCyc allow export of pathways / networks in SBML KEGG2SBML BUT these are representations of the database, NOT computational models Merging issue: Components are named inconsistently

Naming Glucose, glc, D-glucose, alpha-D-glucose? Need to be reconciled Use semantic annotations ChEBI terms for metabolites UniProt terms for enzymes Apply MIRIAM standard

Merging Standard identifiers: job done? Inconsistent charge states Pyruvic acid and pyruvate

Charge state determination Annotated ChEBI terms provides programmatic access to structural data InChI , SMILES strings InChI=1/C3H4O3/c1-2(4)3(5)6/h1H3,(H,5,6)/p-1/fC3H3O3/q-1 CC(=O)C([O-])=O Cheminformatics software (ChemAxon MARVIN) can be used to predict charge state at given pH Consistency ✓ ✗

Stereochemistry KEGG and MetaCyc are inconsistent in their definition of stereochemical precision Apparently minor but can cause gaps in the network beta-D-glucose D-glucose

Stereochemistry-induced gaps X Y

ChEBI ontology ChEBI contains relationships between metabolites

Stereochemistry-induced gaps X Y

Reaction balancing Reaction elemental and charge balancing Aids merging Requirement of Flux Balance Analysis Prevents inconsistencies arriving from “ magical ” production or disappearance of matter KEGG and MetaCyc reactions don ’ t always balance Incorrect stoichiometry Missing protons , water, etc. Solution: use linear programming

Reaction balancing carbon dioxide + 2-Acetolacetate  Pyruvate CO 2 + C 5 H 7 O 4 -  C 3 H 3 O 3 - Ab = 0 A = Reactants Products Optional reactants Optional products CO2 C5H7O4 C3H3O3 H+ H20 H+ H20 CO2 C 1 5 -3 0 0 0 0 -1 O 2 4 -3 0 1 0 -1 -2 H 0 7 -3 1 2 -1 -2 0 charge 0 -1 1 1 0 -1 0 0 b min 1 1 1 0 0 0 0 0

Reaction balancing Linear programming solver solves Ab = 0 b is a vector of stoichiometries carbon dioxide + 2-Acetolacetate  2 Pyruvate + H + CO 2 + C 5 H 7 O 4 -  2 C 3 H 3 O 3 - + H + Reactants Products Optional reactants Optional products CO2 C5H7O4 C3H3O3 H+ H20 H+ H20 CO2 C 1 5 -3 0 0 0 0 -1 O 2 4 -3 0 1 0 -1 -2 H 0 7 -3 1 2 -1 -2 0 charge 0 -1 1 1 0 -1 0 0 b min 1 1 1 0 0 0 0 0 b 1 1 2 0 0 1 0 0

Transporters Transporters are required to transport metabolites into and out of the cell TransportDB is a source of transporter proteins BUT not comprehensive enough to assign these to individual reactions Approach taken is a pragmatic one Add all transport proteins from TransportDB Generate transport reactions for ALL metabolites Map the proteins to the reactions manually

Biomass function Flux Balance Analysis requires an objective function to maximise Traditionally, a biomass function is specified Subliminal adds a generic biomass function Amino acids, nucleotides, lipids, ATP Formats model such that it can be loaded into the COBRA Toolbox

KEGG MetaCyc Merge pathways Balance reactions Format for COBRA Add transport reactions Draft (De)protonate metabolites Balance reactions (De)protonate metabolites Merge Add transport proteins Add biomass reaction

Analysis Goal: can biomass be generated from growth medium? Next question: what is the growth medium? By default, ALL metabolites can be transported into the cell Approach: using FBA to analyse biomass generation and iteratively knock out transporters Generates a minimum required growth medium

Analysis biomass glc PO4 3- y … z x

Analysis biomass glc PO4 3- y … z x ✗

Analysis Minimum biomass producing growth medium: Phosphate, histidine and methionine Amino acids being used as C- N- and S- sources Wrong! Suggests insufficient directionality constraints in the model Second approach specified a “ sensible ” growth medium Only histidine had to be added to the medium Suggests good connectivity BUT suggests gap(s) in histidine synthesis pathways

Subliminal generated model Many more metabolites and enzymes Better coverage? Poor merging? Few unreachable metabolites Good connectivity Many blocked reactions Insufficient sink / export reactions? Components Subliminal Manual Compartments 2 2 Unique metabolites 1385 728 Unique enzymes 1229 939 Metabolic reactions 1440 947 Unreachable metabolites 238/2953 (8.1%) 75/758 (9.9%) Blocked reactions 831/1538 (54%) 140/1102 (13%)

Future developments Directionality Use thermodynamic predictions of reaction reversibility Possible to automate due to our mapping to chemical structures Compartmentalisation Use protein localisation prediction to infer intra-cellular compartmentalisation Possible to automate due to our mapping to UniProt identifiers and protein sequences

Conclusion Many steps can be automated in generating genome-scale metabolic reconstructions Additional modules would be useful Manual curation still necessary…but… Subliminal Toolbox is modular Can be used in manual curation phase Approach is far better than starting from scratch

The Subliminal Toolbox: automating steps in the reconstruction of metabolic networks Neil Swainston Manchester Centre for Integrative Systems Biology Mendes meeting 13 January 2011

The Subliminal Toolbox: automating steps in the reconstruction of metabolic networks

by Neil Swainston on Jan 13, 2011

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