From genomes to Pathway tools and from Pathway Tools to

Metabolic ModelsJeremy Zucker

Broad Institute of MIT and Harvard

Outline

• Enzyme prediction with EFICAz• TBCyc curation• FungiCyc curation• Network debugging tools

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

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

NeurosporaCyc curation

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

Acknowle

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

Outline

• e354f1709c• ICS Guest network