INSTITUTE for GENOMICBIOLOGY Nathan Price Department of Chemical & Biomolecular Engineering Center...

INSTITUTE INSTITUTE forfor

GENOMIC GENOMIC BIOLOGYBIOLOGY

Nathan Price

Department of Chemical & Biomolecular Engineering

Center for Biophysics & Computational Biology

Institute for Genomic Biology

University of Illinois, Urbana-Champaign

Metabolic Pathways Workshop

Edinburgh, Scotland

April 7, 2011



Interactions between metabolic and regulatory networks

Milne, Eddy, Kim, Price, Biotechnology Journal, 2009



Biochemical Reaction Networks Statistical Inference Networks

Constraint-Based Model

Interaction Networks

Statistical Inference Network

Application of Constraints Network Inference

•Transcriptomics

•Proteomics

•Metabolomics

Reaction Stoichiometry

Enz A Enz-A B Enz-A-B C DRxn. 1 -1 -1 +1 0 0 0 0

Rxn. 2 0 0 -1 -1 +1 0 0

Rxn. 3 +1 0 0 0 -1 +1 +1

Protein-MetaboliteProtein-ProteinDNA-ProteinDNA-DNA

ActivationInhibitionIndirect

C = f(A,B,D)

LiteratureGenome Annotation

Data Sources

Interactomics

IntegratedNetwork

Data

MathematicalModel

v3

v1

v2S · v = 0v ≤ vmax

PhylogeneticData

PhysiologicalData

More detail (biochemistry, etc.) Less detail

Eddy and Price, Encyclopedia of complexity and systems science (2009)



Need for automated reconstruction methods

1

10

100

1000

1995 1997 1999 2001 2003 2005 2007 2009

# C

om

ple

ted

Year

GenomesGEMs

C Milne, JA Eddy, PJ Kim, ND Price, Biotechnology Journal, 2009



Automated reconstruction of metabolic networks

Automated reconstruction of computable metabolic network models

Demonstrated on 130 genomes

Provide advanced starting point for virtually any organism

Accuracy from genomics: 65%

With biolog and optimization: 87%

Henry, C. DeJongh, M, Best, AA, Frybarger, PM, and Stevens, RL, Nature Biotechnology, 2010



Integrated automated reconstructions

Integration of automatically learned statistics-based regulatory networks and biochemistry-based metabolic networks

Sriram Chandrasekaran

Amit Ghosh

Bozena Sawicka



Example of Current State-of-the-Art: rFBA

Motivated by data limitations Regulatory network

represented by Boolean rules

Rules taken from literature curation

Only subset of network available under different environmental conditions

Metabolic flux analysis performed with available reactions

Covert, MW et al., Nature, 2004



PROM models integrating TRN and metabolic network

Automated Comprehensive Probabilistic Boolean vs Boolean Higher accuracy

Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010



PROM MODEL - PROBABILITIES

PROM's novelty lies in the introduction of probabilities to represent gene states and gene - transcription factor (TF) interactions.

P(A = 1|B = 0) - The probability of gene A being ON when its transcription factor B is OFF

P(A = 1|B = 1) - probability of A being ON when B is ON.




CONSTRAINING FLUXES USING PROBABILITIES




PROM: Basis is a constraint-based metabolic model

Constraint-based analysis involves solving the linear optimization problem:

max wTv

subject to constraints

S.v = 0

lb ≤ v ≤ ub

where S is the stoichiometric matrix, v is a flux vector representing a particular flux configuration, wTv is the linear objective function, and lb,ub are vectors containing the minimum and maximum fluxes through each reaction.



PROM Approach

PROM finds a flux distribution that satisfies the same constraints as FBA plus additional constraints due to the transcriptional regulation -

min (κ.α + κ.β)

subject to constraints

lb’ – α ≤ v ≤ ub’ + β

α, β ≥ 0

Where lb’, ub’ are constraints based on transcriptional regulation ( the flux bound cues), α,β are positive constants which represent deviation from those constraints and κ represents the penalty for such deviations.

α

β



Data used for the E. coli PROM model

E. coli

Metabolic Model IAF1260

MetabolicReactions

2382

Regulatory data RegulonDB

Regulatory Interactions 1773

Microarrays 907

Total Genes in the model 1400

Validation Data set 1875 growth phenotypes

Feist A et al, Molecular Systems Biology, 2007Chandrasekaran, S., and Price, N.D., PNAS, 2010



Automated PROM model has similar accuracy to RFBA

Covert MW et al, Nature, 2004Chandrasekaran S, and Price ND, PNAS, 2010

COMPARISON WITH RFBA

Non Lethal, both PROM,RFBA are

right

Lethal, both PROM,RFBA are right

PROM wrong ,RFBA right PROM right, RFBA wrong Lethal, both wrong Non lethal, both wrong

PROM – 85% , RFBA – 81%

AUTOMATED (PROM) Vs MANUAL (RFBA)



Increased comprehensiveness to previous RFBA model

Covert MW, Nature, 2004Chandrasekaran, S, and Price, ND, In review, 2010

Automated learning from high-throughput data improves comprehensiveness



Results: Quantitative Growth Prediction

Experimental data taken from MW Covert et al, Nature, 2004Chandrasekaran, S., and Price, N.D., PNAS, 2010

Growth rate prediction by PROM

Culture Actual PROM

WT + O2 0.71 0.7382

WT - O2 0.49 0.385

ΔarcA + O2 0.69 0.7651

ΔarcA - O2 0.38 0.3224

Δfnr + O2 0.63 0.5635

Δfnr - O2 0.41 0.2181

Δfnr/ΔarcA + O2 0.65 0.6596

Δfnr/ΔarcA - O2 0.3 0.204

ΔappY + O2 0.64 0.7152

ΔappY - O2 0.48 0.3287

ΔoxyR + O2 0.64 0.7876

ΔoxyR - O2 0.48 0.3287

ΔsoxS + O2 0.72 0.7687

ΔsoxS - O2 0.46 0.379

Overall correlation with experimental data: R = 0.95

Function of both oxygen switch (dominant) and regulation

Experimental growth rate

Pre

dict

ed g

row

th r

ate



PROM Model Inputs for M. tuberculosis

M. tuberculosis

Metabolic Model iNJ661

MetabolicReactions

1028

Regulatory data Balazsi et al

Regulatory Interactions 218

Microarrays 437

Total Genes in the model 691

Validation Data set 30 TF knockout

Jamshidi NJ, and Palsson, BO, BMC Systems Biology, 2007Balazsi G et al, Molecular Systems Biology, 2008; Boshoff HI et al, JBC, 2004



Accuracy in predicting essentiality of TF for optimal growth

Accuracy 95%

Sensitivity % 83

Specificity % 100

TFPredicted Growth

rate

dnaA 0.03Rv0485 0.042

crp 0.03sigD 0.05kdpE 0.052ideR 0.038

Rv1395 0.028argR 0.047sigC 0.024sigH 0.05lrpA 0.032

Rv3575c 0.026oxyS 0.052nadR 0.052hspR 0.052regX3 0.052

Rv0586 0.052narL 0.052sigE 0.052

furA 0.052

Rv1931c 0.052furB 0.052lexA 0.052pknK 0.052dosR 0.052birA 0.052sigF 0.052kstR 0.052

cyp143 0.052embR 0.052

Essential gene

Non essential gene

Candidate essential

LegendCorrect Prediction

Incorrect Prediction

Non essential gene

Essential gene




PROM Model Inputs for S. cerevisiae

S. cerevisiae

Metabolic Model iMM904

MetabolicReactions

1577

Regulatory data YEASTRACT

Regulatory Interactions

4200

Microarrays 904, M3D

Total Genes in the model

904

Validation Data set 136 TF knockout

Duarte NC et al BMC Genomics 2004

Steinmetz LM et al. Nature Genetics 2002

Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)



Increased comprehensiveness to previous RFBA model

Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)Herrgard et al., Genome Res, 2006

0 1000 2000 3000 4000 5000

TranscriptionFactors

RegulatedMetabolic Genes

Interactions

PROM model

RFBA model iMH805/775

RFBA model iMH805/775 PROM model

Transcription Factors 55 136

Regulated Metabolic Genes 348 904

Interactions 775 4200



Accuracy in predicting essentiality of TF for optimal growth

Predicts correctly 135/136 of lethal/non-lethal calls

Identifies 8 lethal TF KOs, with only 1 false positive

Lone miss (Gcn4) is a very slow grower (multiple days)




Validation: Quantitative Growth Prediction

Experimental data taken from MJ Herrgard et al, Genome Res 2006

Overall correlation with experimental data: R = 0.96

Driven by both substrate (dominant) and regulation

Experimental growth rate

Pre

dict

ed g

row

th r

ate

Growth rate prediction by PROM

Culture GlucoseSUR = 6.3; OUR = 2.5

GalactoseSUR = 2.1, OUR = 3.9

FructoseSUR = 2.6, OUR =

6.2

Actual PROM Actual PROM Actual PROM

WT 0.21 0.22 0.13 0.15 0.2 0.23

adr1 0.21 0.22 0.13 0.15 0.2 0.23

cat8 0.21 0.22 0.13 0.15 0.2 0.23

mig2 0.21 0.22 0.13 0.15 0.2 0.23

sip4 0.21 0.22 0.13 0.12 0.2 0.19

gal4 0.21 0.22 0.03 0.01 0.2 0.23

rtg1 0.21 0.22 0.08 0.07 0.2 0.23

mth1 0.21 0.21 0.11 0.15 0.2 0.23

nrg1 0.21 0.20 0.13 0.14 0.2 0.22

mig1 0.21 0.21 0.13 0.15 0.2 0.21

gcr2 0.17 0.15 0.13 0.15 0.16 0.17

0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2 0.25




0

0.05

0.1

0.15

0.2

0.25

0 0.05 0.1 0.15 0.2 0.25

Experimental Growth Rate

Pre

dic

ted

Gro

wth

Rat

e

Quantitative Growth Prediction for 77 TF knockout Phenotypes with Galactose

Overall correlation with experimental data: R = 0.90(based only on regulation – metabolic model alone would be flat line)

Experimental data taken from SM Fendt et al, Molecular Systems Biology 2010Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)



ReactionWT(expt)

GCN4 (expt)

WT(model)

GCN4 (model)

flux (expt)

flux(model)

G6P <=> F6P (net) 492.89 362.99 500 500

PEP -> 1280.46 1290.54 17.72 18.94

P5P <=> EC2 + G3P (net) 127.82 213.63 0.263 0.217

F6P <=> EC2 + E4P (net) -60.99 -103.85 -0.182 -0.19

S7P <=> EC3 + E4P (net) 66.82 109.79 8.661 9.19

PYR -> ACA + CO2 1162.09 1035.51 17.78 15.94

ETH -> ETHOUT 662.32 666.92 15.82 17.72

ACE -> ACCOA 504.22 371.79 0.147 0.067

OAAMIT+ACCOAMIT-> CITMIT 515.08 496.43 0.529 0.811

OAAMIT <=> OAA (net) -243.97 -156.85 -498.08 -464.36

CITMIT <=> CIT (net) 51.48 72.45 0.035 0.076

SER -> CYS 2.84 2.07 0.230 0.0019

SER <=> GLY + METTHF (net) 8.92 14.02 0.09709 0.0448

OAA -> ASP 27.41 21.02 500 474.91

PYR -> 4.32 3.32 0.67622 0.1471

AKG -> GLU 27.48 25.89 0.0285 0.0131

GLU -> ORN 5.81 3.72 0.04626 0.0258

CHOR -> PPHN 5.83 5.94 0.03111 0.0679

Prediction of Metabolic flux for ∆Gcn4 mutant strain

Experimental data taken from SM Fendt et al, Moxley et al, PNAS 2009Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)



PROM Highlights

PROM is a new approach for integrating the transcriptional network with metabolism Automated and comprehensive

We compared it with state-of-the art metabolic-regulatory models of E. coli Comparable accuracy More comprehensive (automated from HT data)

We constructed the first genome-scale integrated regulatory-metabolic model for M. tuberculosis

We compared it with state-of-the art metabolic-regulatory models of S. cerevisiae Much more accurate Much more comprehensive (automated from HT data)

PROM can accurately predict the effect of perturbations to transcriptional regulators and subsequently be used to predict microbial growth phenotypes quantitatively


INSTITUTE forGENOMIC BIOLOGY

Constraint-based Reconstruction and Analysis Conference

Confirmed Speakers

Eivind AlmaasRonan FlemingVassily Hatzimanikatis Christopher HenryHermann-Georg HolzhütterCostas MaranasJens NielsenBernhard Palsson

Jason PapinBalázs PappNathan PriceEytan RuppinUwe SauerStefan SchusterDaniel SegreInes Thiele

Key Dates

April 7, 2011 - Abstract Deadline for oral & poster presentations (WILL EXTEND)

June 24-26, 2011 - COBRA conference

Nathan D. Price Lab @ the University of Illinois, Urbana-ChampaignPostdocsNick ChiaCory FunkAmit GhoshPan-Jun KimCharu Gupta KumarYounhee KoVineet Sangar

Graduate StudentsDaniel BakerMatthew BenedictSriram Chandrasekaran John Earls James Eddy Matthew Gonnerman Seyfullah Kotil Piyush Labhsetwar Shuyi Ma Andrew Magis Caroline Milne Matthew Richards Bozena Sawicka Jaeyun Sung Chunjing Wang Yuliang Wang

Acknowledgments

Funding SourcesNIH / National Cancer InstituteHoward Temin Pathway to Independence AwardNSF CAREERDepartment of Defense – TATRCDepartment of EnergyEnergy Biosciences Institute (BP)Luxembourg-ISB Systems Medicine ProgramRoy J. Carver Charitable Trust Young Investigator Award

INSTITUTE for GENOMICBIOLOGY Nathan Price Department of Chemical & Biomolecular Engineering Center...

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