Nathan Price Department of Chemical & Biomolecular Engineering

1. Integrated Regulatory-Metabolic Network Construction using Probabilistic Regulation of Metabolism (PROM) 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. Interactions between metabolic and regulatory networks Milne, Eddy, Kim, Price, Biotechnology Journal, 2009

3. Biochemical Reaction Networks Statistical Inference Networks Data Sources Phylogenetic Data Physiological Data Genome Annotation Literature Interactomics More detail (biochemistry, etc.) Less detail Reaction Stoichiometry Interaction Networks • Transcriptomics  • Proteomics  • Metabolomics  Integrated NetworkData Protein-Metabolite Protein-Protein DNA-Protein DNA-DNA Application of Constraints Network Inference Constraint-Based Model Statistical Inference Network v2 S · v = 0 v ≤ vmax C = f(A,B,D) Mathematical Model Activation Inhibition Indirect v1 v3 Eddy and Price, Encyclopedia of complexity and systems science (2009)

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

5. 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. Integrated automated reconstructions

7. Probabilistic Regulation of Metabolism (PROM) Integration of automatically learned statistics-based regulatory networks and biochemistry-based metabolic networks Sriram Chandrasekaran BozenaSawicka AmitGhosh

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

11. CONSTRAINING FLUXES USING PROBABILITIES Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

12. 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.

13. 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. β α

14. Data used for the E. coli PROM model Feist A et al, Molecular Systems Biology, 2007 Chandrasekaran, S., and Price, N.D., PNAS, 2010

15. Automated PROM model has similar accuracy to RFBA PROM – 85% , RFBA – 81% AUTOMATED (PROM) Vs MANUAL (RFBA) Covert MW et al, Nature, 2004 Chandrasekaran S, and Price ND, PNAS, 2010

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

17. Results: Quantitative Growth Prediction Predicted growth rate Experimental growth rate Overall correlation with experimental data: R = 0.95 Function of both oxygen switch (dominant) and regulation Experimental data taken from MW Covert et al, Nature, 2004 Chandrasekaran, S., and Price, N.D., PNAS, 2010

18. PROM Model Inputs for M. tuberculosis Jamshidi NJ, and Palsson, BO, BMC Systems Biology, 2007 Balazsi G et al, Molecular Systems Biology, 2008; Boshoff HI et al, JBC, 2004

19. Accuracy in predicting essentiality of TF for optimal growth Chandrasekaran and Price, Proc. Natil. Acad. Sci. USA, 2010

20. PROM Model Inputs for S. cerevisiae Duarte NC et alBMC Genomics 2004 Steinmetz LM et al.Nature Genetics2002 Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

21. Increased comprehensiveness to previous RFBA model Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation) Herrgard et al., Genome Res, 2006

22. 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. Validation: Quantitative Growth Prediction Experimental data taken from MJ Herrgard et al, Genome Res 2006 Predicted growth rate Experimental growth rate Overall correlation with experimental data: R = 0.96 Driven by both substrate (dominant) and regulation Ghosh, Chandrasekaran, Zhao, and Price, 2010 (in preparation)

24. 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. 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. 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. 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 Key Dates Jason Papin Balázs Papp Nathan Price Eytan Ruppin Uwe Sauer Stefan Schuster Daniel Segre Ines Thiele April 7, 2011 - Abstract Deadline for oral & poster presentations (WILL EXTEND) June 24-26, 2011 - COBRA conference

28. Acknowledgments 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 EarlsJames Eddy Matthew Gonnerman Seyfullah Kotil Piyush Labhsetwar Shuyi Ma Andrew Magis Caroline Milne Matthew Richards Bozena Sawicka Jaeyun Sung Chunjing WangYuliang Wang 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