1. INSTITUTE for GENOMICBIOLOGY 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

2. INSTITUTE for GENOMICBIOLOGY Interactions between metabolic and regulatory networks Milne, Eddy, Kim, Price, Biotechnology Journal, 2009

3. INSTITUTE for GENOMICBIOLOGY Biochemical Reaction NetworksStatistical Inference Networks Constraint-Based Model Interaction Networks Statistical Inference Network Application of ConstraintsNetwork Inference Transcriptomics Proteomics Metabolomics Reaction Stoichiometry Protein-Metabolite Protein-Protein DNA-Protein DNA-DNA Activation Inhibition Indirect C = f(A,B,D) Literature Genome Annotation Data Sources Interactomics Integrated Network Data Mathematical Model v3v3 v1v1 v2v2 S · v = 0 v v max Phylogenetic Data Physiological Data More detail (biochemistry, etc.)Less detail Eddy and Price, Encyclopedia of complexity and systems science (2009)

4. INSTITUTE for GENOMICBIOLOGY Need for automated reconstruction methods C Milne, JA Eddy, PJ Kim, ND Price, Biotechnology Journal, 2009

5. INSTITUTE for GENOMICBIOLOGY 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

6. INSTITUTE for GENOMICBIOLOGY Integrated automated reconstructions

7. Integration of automatically learned statistics-based regulatory networks and biochemistry-based metabolic networks Sriram Chandrasekaran Amit Ghosh Bozena Sawicka

8. INSTITUTE for GENOMICBIOLOGY 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

9. INSTITUTE for GENOMICBIOLOGY PROM models integrating TRN and metabolic network Automated Comprehensive Probabilistic Boolean vs Boolean Higher accuracy Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

10. INSTITUTE for GENOMICBIOLOGY 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. IF (B) THEN AP(A|B) = 0.95 Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

11. INSTITUTE for GENOMICBIOLOGY CONSTRAINING FLUXES USING PROBABILITIES TFp(mRNA|TF) Flux Bound p*Vmax Optimal Flux State Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

12. INSTITUTE for GENOMICBIOLOGY PROM: Basis is a constraint-based metabolic model Constraint-based analysis involves solving the linear optimization problem: max w T v 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, w T v is the linear objective function, and lb,ub are vectors containing the minimum and maximum fluxes through each reaction.

13. INSTITUTE for GENOMICBIOLOGY 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

14. INSTITUTE for GENOMICBIOLOGY Data used for the E. coli PROM model E. coli Metabolic ModelIAF1260 Metabolic Reactions 2382 Regulatory dataRegulonDB Regulatory Interactions1773 Microarrays907 Total Genes in the model1400 Validation Data set 1875 growth phenotypes Feist A et al, Molecular Systems Biology, 2007 Chandrasekaran, S., and Price, N.D., PNAS, 2010

15. INSTITUTE for GENOMICBIOLOGY Automated PROM model has similar accuracy to RFBA Covert MW et al, Nature, 2004 Chandrasekaran 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 tdc R crp ma lT glp R gnt R xyl R as nC rbs R ilv Y gln G rha S cp xR cyt R sox R me lR 1,2Propanediol 2Deoxy Adenosine aDGlucose aDLactose aKetoGlutaric Acid Acetic Acid Acetoacetic Acid Adenosine Citric Acid D,LMalic Acid DAlanine DFructose DGalactose DGalacturonic Acid DGluconic Acid DGlucose6Phosphate DGlucuronic Acid DMannitol DMannose DMelibiose DRibose DSerine DSorbitol DTrehalose DXylose Formic Acid Fumaric Acid Glycerol Glycolic Acid Inosine LAlanine LArabinose LAsparagine LAspartic Acid LFucose LGlutamic Acid LGlutamine LLactic Acid LMalic Acid LProline LRhamnose LSerine LThreonine Maltose Maltotriose NAcetylbDMannosamine NAcetylDGlucosamine Pyruvic Acid Succinic Acid Sucrose Thymidine Uridine Butyric Acid D,LCarnitine Dihydroxy Acetone gAmino Butyric Acid Glycine LArginine LHistidine LIsoleucine LLeucine LLysine LMethionine LOrnithine LPhenylalanine LTartaric Acid LValine NAcetylNeuraminic Acid Putrescine Adenine Adenosine AlaAsp AlaGln AlaGlu AlaGly AlaHis AlaLeu AlaThr Allantoin Ammonia Cytidine Cytosine DAlanine DGlucosamine DSerine GlyAsn GlyGln GlyGlu GlyMet Glycine Guanine Guanosine Inosine LAlanine LArginine LAsparagine LAspartic Acid LCysteine LGlutamic Acid LGlutamine LHistidine LIsoleucine LLeucine LLysine LMethionine LOrnithine LPhenylalanine LProline LSerine LThreonine LTryptophan LTyrosine LValine MetAla NAcetylDGlucosamine NAcetylDMannosamine Nitrate Nitrite Putrescine Thymidine Uracil Urea Uridine Xanthine Xanthosine PROM – 85%, RFBA – 81% AUTOMATED (PROM) Vs MANUAL (RFBA)

16. INSTITUTE for GENOMICBIOLOGY Increased comprehensiveness to previous RFBA model Covert MW, Nature, 2004 Chandrasekaran, S, and Price, ND, In review, 2010 Automated learning from high-throughput data improves comprehensiveness

17. INSTITUTE for GENOMICBIOLOGY Results: Quantitative Growth Prediction Experimental data taken from MW Covert et al, Nature, 2004 Chandrasekaran, S., and Price, N.D., PNAS, 2010 Growth rate prediction by PROM CultureActualPROM WT + O20.710.7382 WT - O20.490.385 ΔarcA + O20.690.7651 ΔarcA - O20.380.3224 Δfnr + O20.630.5635 Δfnr - O20.410.2181 Δfnr/ΔarcA + O20.650.6596 Δfnr/ΔarcA - O20.30.204 ΔappY + O20.640.7152 ΔappY - O20.480.3287 ΔoxyR + O20.640.7876 ΔoxyR - O20.480.3287 ΔsoxS + O20.720.7687 ΔsoxS - O20.460.379 Overall correlation with experimental data: R = 0.95 Function of both oxygen switch (dominant) and regulation Experimental growth rate Predicted growth rate

18. INSTITUTE for GENOMICBIOLOGY PROM Model Inputs for M. tuberculosis M. tuberculosis Metabolic ModeliNJ661 Metabolic Reactions 1028 Regulatory dataBalazsi et al Regulatory Interactions218 Microarrays437 Total Genes in the model691 Validation Data set30 TF knockout Jamshidi NJ, and Palsson, BO, BMC Systems Biology, 2007 Balazsi G et al, Molecular Systems Biology, 2008; Boshoff HI et al, JBC, 2004

19. INSTITUTE for GENOMICBIOLOGY Accuracy in predicting essentiality of TF for optimal growth Accuracy95% Sensitivity %83 Specificity %100 TF Predicted Growth rate dnaA 0.03 Rv0485 0.042 crp 0.03 sigD 0.05 kdpE 0.052 ideR 0.038 Rv1395 0.028 argR 0.047 sigC 0.024 sigH 0.05 lrpA 0.032 Rv3575c 0.026 oxyS 0.052 nadR 0.052 hspR 0.052 regX3 0.052 Rv0586 0.052 narL 0.052 sigE 0.052 furA 0.052 Rv1931c 0.052 furB 0.052 lexA 0.052 pknK 0.052 dosR 0.052 birA 0.052 sigF 0.052 kstR 0.052 cyp143 0.052 embR 0.052 Essential gene Non essential gene Candidate essential Legend Correct Prediction Incorrect Prediction Non essential gene Essential gene Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

20. INSTITUTE for GENOMICBIOLOGY PROM Model Inputs for S. cerevisiae S. cerevisiae Metabolic ModeliMM904 Metabolic Reactions 1577 Regulatory dataYEASTRACT Regulatory Interactions 4200 Microarrays904, M3D Total Genes in the model 904 Validation Data set136 TF knockout Duarte NC et al BMC Genomics 2004 Steinmetz LM et al. Nature Genetics 2002 Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

21. INSTITUTE for GENOMICBIOLOGY Increased comprehensiveness to previous RFBA model Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation) Herrgard et al., Genome Res, 2006 RFBA model iMH805/775PROM model Transcription Factors55136 Regulated Metabolic Genes348904 Interactions7754200

22. INSTITUTE for GENOMICBIOLOGY 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) Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

23. INSTITUTE for GENOMICBIOLOGY 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 Predicted growth rate Growth rate prediction by PROM Culture Glucose SUR = 6.3; OUR = 2.5 Galactose SUR = 2.1, OUR = 3.9 Fructose SUR = 2.6, OUR = 6.2 ActualPROMActualPROMActualPROM WT0.210.220.130.150.20.23 adr1 0.210.220.130.150.20.23 cat8 0.210.220.130.150.20.23 mig2 0.210.220.130.150.20.23 sip4 0.210.220.130.120.20.19 gal4 0.210.220.030.010.20.23 rtg1 0.210.220.080.070.20.23 mth1 0.21 0.110.150.20.23 nrg1 0.210.200.130.140.20.22 mig1 0.21 0.130.150.20.21 gcr2 0.170.150.130.150.160.17 Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

24. INSTITUTE for GENOMICBIOLOGY 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 2010 Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

25. INSTITUTE for GENOMICBIOLOGY Reaction WT (expt) GCN4 (expt) WT (model) GCN4 (model) flux (expt) flux (model) G6P F6P (net)492.89362.99500 PEP ->1280.461290.5417.7218.94 P5P EC2 + G3P (net)127.82213.630.2630.217 F6P EC2 + E4P (net)-60.99-103.85-0.182-0.19 S7P EC3 + E4P (net)66.82109.798.6619.19 PYR -> ACA + CO21162.091035.5117.7815.94 ETH -> ETHOUT662.32666.9215.8217.72 ACE -> ACCOA504.22371.790.1470.067 OAAMIT+ACCOAMIT-> CITMIT515.08496.430.5290.811 OAAMIT OAA (net)-243.97-156.85-498.08-464.36 CITMIT CIT (net)51.4872.450.0350.076 SER -> CYS2.842.070.2300.0019 SER GLY + METTHF (net)8.9214.020.097090.0448 OAA -> ASP27.4121.02500474.91 PYR ->4.323.320.676220.1471 AKG -> GLU27.4825.890.02850.0131 GLU -> ORN5.813.720.046260.0258 CHOR -> PPHN5.835.940.031110.0679 Prediction of Metabolic flux for Gcn4 mutant strain Experimental data taken from SM Fendt et al, Moxley et al, PNAS 2009 Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

26. INSTITUTE for GENOMICBIOLOGY 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 Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

27. INSTITUTE for GENOMIC BIOLOGY Constraint-based Reconstruction and Analysis Conference Confirmed Speakers Eivind Almaas Ronan Fleming Vassily Hatzimanikatis Christopher Henry Hermann-Georg Holzhütter Costas Maranas Jens Nielsen Bernhard Palsson Jason Papin Balázs Papp Nathan Price Eytan Ruppin Uwe Sauer Stefan Schuster Daniel Segre Ines Thiele Key Dates April 7, 2011 - Abstract Deadline for oral & poster presentations (WILL EXTEND) June 24-26, 2011 - COBRA conference

28. Nathan D. Price Lab @ the University of Illinois, Urbana-Champaign Postdocs Nick Chia Cory Funk Amit Ghosh Pan-Jun Kim Charu Gupta Kumar Younhee Ko Vineet Sangar Graduate Students Daniel Baker Matthew Benedict Sriram Chandrasekaran John Earls James Eddy Matthew Gonnerman Seyfullah Kotil Piyush Labhsetwar Shuyi Ma Andrew MagisCaroline Milne Matthew Richards Bozena Sawicka Jaeyun SungChunjing WangYuliang Wang Acknowledgments Funding Sources NIH / National Cancer Institute Howard Temin Pathway to Independence Award NSF CAREER Department of Defense – TATRC Department of Energy Energy Biosciences Institute (BP) Luxembourg-ISB Systems Medicine Program Roy J. Carver Charitable Trust Young Investigator Award