Post on 03-Jan-2016
From genomes to Pathway tools and from Pathway Tools to
Metabolic ModelsJeremy Zucker
Broad Institute of MIT and Harvard
Pathway predictor
Enzyme predictor
Model
Predictions
Omics data
From genome sequences to Metabolic flux models
Enzyme Prediction
• EFICAz: – Predicts Enzyme Commision Numbers– Functionally Discriminating Residues
• Homofunctional MSA• Heterofunctional MSA
– Support Vector Machine– Sequence Identity Threshold– Integrates 9 independent methods to achieve
maximal accuracy• KEGG-BLAST
Enzyme Prediction
• EficazTool incorporated into Calhoun web interface
• Runs on LSF cluster for all Transcripts in an AnalysisRun
• Automatically assigns EC number OntologyTerm and ToolEvidence to Transcript
TBCyc curation
• TBDB TBCyc integration• Palsson Flux models => curated pathways• McFadden flux model => curated pathways• Literature => curated pathways• Expression data => Omics viewer• Chip-Seq => curated gene regulatory network• Coming soon: – annotation jamboree for TB– Connect to Decode Open source drug discovery for TB
Neurospora curation
• Resources:– Model organism– Active Community Annotation Project– Recent genome assembly and annotation– Systems Biology grant• Chip-seq• KO library• Biofuels
Biofuels from Neurospora?
• Growing interest for obtaining biofuels from fungi
• Neurospora crassa has more cellulytic enzymes than Trichoderma reesei
• N. crassa can degrade cellulose and hemicellulose to ethanol [Rao83]
• Simultaneous saccharification and fermentation means that N. crassa is a possible candidate for consolidated bioprocessing
Xylose
Ethanol
Effects of Oxygen limitation on Xylose fermentation in Neurospora crassa
Zhang, Z., Qu, Y., Zhang, X., Lin, J., March 2008. Effects of oxygen limitation on xylose fermentation, intracellular metabolites, and key enzymes of Neurospora crassa as3.1602. Applied biochemistry and biotechnology 145 (1-3), 39-51.
Xylose
Pyruvate
TCA Ethanol
Respiration Fermentation
Glycolysis
0 2 4 6 8 10 12 140
10
20
30
40
50
60
70
Ethanol production vs Oxygen level
Oxygen level (mmol/L*g)
Etha
nol c
onve
rsio
n (%
)Low O2
Intermediate O2
High O2
Glycolysis
Xylose degradationPentose phosphate
Aerobic respirationFermentation
TCA Cycle
Model of Xylose Fermentation
Xylose
Oxygen
Ethanol
ATP
Two paths from xylose to xylitol
Glycolysis
Xylose degradationPentose phosphate
Aerobic respirationFermentation
TCA Cycle
Oxygen=5
ATP=16.3
NADPHRegeneration
NADPH &NAD+
Utilization
HighOxygen
NAD+ Regeneration
Glycolysis
Xylose degradationPentose phosphate
Aerobic respirationFermentation
TCA CycleEthanol
LowOxygen
Oxygen=0
Glycolysis
Xylose degradationPentose phosphate
Aerobic respirationFermentation
TCA CycleEthanol
IntermediateOxygen
OptimalEthanol
NADPH &NAD
Utilization
Oxygen=0.5
ATP=2.8
NAD Regeneration
NADPHRegeneration
All O2 used to regenerate
NAD used in first step
Glycolysis
Xylose degradationPentose phosphate
Aerobic respirationFermentation
TCA CycleEthanol
IntermediateOxygen
OptimalEthanol
NADPH &NAD
Utilization
Oxygen=0.5
ATP=2.8
NAD Regeneration
NADPHRegeneration
All O2 used to regenerate
NAD used in first step
BottleneckPyruvate
decarboxylase
Improve NADHenzyme
Xylose Lipids
• col-2 (Glucose-6-phosphate dehydrogenase) mutant produces lipids from xylose and glucose– 35x more TAGS than WT– 12% biomass is lipid
• Mapping out the metabolic pathways to explain this phenomenon.
• Lipids for NADPH regeneration?
Algorithms for Debugging Metabolic Networks
• Metabolic network is too complicated.• The metabolic network is infeasible.• E-flux results in dead model.
Minimal Organism
• Given a feasible model under the given nutrient conditions, find the fewest number of nonzero fluxes that still results in a viable organism.
minimize card(v)subject to: Sv = 0, l <= v <= u
Minimal Reaction Adjustment
• Given an infeasible model, find a reaction with the smallest number of reactants and products that results in a feasible model.
minimize card( r )subject to Sv + r = 0l <= v <= u
Minimal Limit Adjustment
• Given a set of (feasible) baseline limits, and an (infeasible) set of expression-constrained flux limits, find the smallest number of adjustments to the flux limits that results in feasible model (without exceeding the baseline constraints).
minimize card(dl) + card(du)subject to: Sv = 0,l – dl <= v <= u+dul-dl >= l_0, u+ du <= u_0dl >= 0, du >= 0.
Minimum Cardinality
• Each of these problems is a special case of the minimum cardinality problem
Minimize card( x )Subject to Ax + By >= fCx + Dy >= g• Caveat: the minimum cardinality problem is
NP-Hard!
Sparse Optimization to the rescue!
• Recent results from Compressed Sensing have shown that minimizing the L1-norm is a decent heuristic
• Iterative methods can improve the results• Instead of minimize card( x ), Minimize norm(diag(w) x, 1) = sum(w_i |x_i| )Update w_i = 1/(epsilon + |x_i|), i=1,…,k
Implementations
• 3 software packages in matlab– Cvx (Sedumi and spdt3)– Glpkmex (GLPK)– Cplexmex (CPLEX)
• min cardinality– MILP and L1 heuristic version
• min limit adjustment– MILP and L1 heuristic version
• Min limit adjustment => min cardinality
AcknowledgementsBroad InstituteJames GalaganBruce Birren
Brian HaasAaron BrandesMatt HennLi Jun MaChristina Cuomo
Carsten Russ
Broad Genome Sequencing Platform
Program ProjectHeather Hood
SRIPeter KarpMarkus KrumenackerSuzanne PaleyMario LatendresseTomer Altman…
Finishing TeamMargaret PriestHarindra ArachchiLynne AftuckMike Fitzgerald
Genome AssemblySarah YoungSean Sykes
Annotation TeamBrian HaasMike KoehrsenQian ZengTom Walk