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Cobra phylogeny paper slides

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Systems biology modeling using constraint-based reconstruction and modeling.

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Cobra phylogeny paper slides

  1. 1. Constraining the metabolic genotype- phenotype relationship using a phylogeny of in silico methods Dr. Nathan Lewis Beng 212 Feb 17, 2015
  2. 2. Constraint-based modeling Metabolism: a network of chemical reactions… … with extra complexities Lewis, et al. Nat Rev Microb, 2012
  3. 3. Modeling cellular objectives Natural selection… – selects traits that enhance growth, given the environment Biomass objective Flux balance analysis – optimizing the objective Lewis, et al. Nat Rev Microb, 2012
  4. 4. The growing toolbox of constraint-based methods for computational modeling FBA: popular/biased Unbiased Methods Lewis, et al. Nat Rev Microb, 2012
  5. 5. Flux balance analysis and the addition of constraints Optimization of a “biological objective” Many solutions Geometric FBA Lewis, et al. Nat Rev Microb, 2012
  6. 6. Constraints on flux FBAwMC – constraints based on enzyme crowding pFBA – minimizes enzyme catalyzed flux
  7. 7. Accounting for changes in media DFBA
  8. 8. Exploring a variety of solutions and coupled reactions Flux variability analysis Bayesian FBA Flux coupling finder Lewis, et al. Nat Rev Microb, 2012
  9. 9. Simulating genetic perturbations Metabolite essentiality MOMA ROOM Lewis, et al. Nat Rev Microb, 2012
  10. 10. Metabolite essentiality for drug discovery Kim, et al. Mol Syst Bio 2011
  11. 11. Considerations in strain design Coupling production to a cell objective or selective marker (growth? Enzymes?) Is the perturbation realistic? Lewis, et al. Nat Rev Microb, 2012
  12. 12. Adding reactions for strain design OptStrain – Test to see if a product can be made using a universal reaction database and host reactions – Minimize the number of reactions you must add from a universal reaction database – Growth couple the product by reaction removal, if possible
  13. 13. Constraining directionality with thermodynamic constraints Network refinement Filling in gaps and extending network
  14. 14. Thermodynamic constraints Based on metabolite http://www.ncbi.nlm.nih.gov/pubmed/21281568 Based on network topology http://en.wikipedia.org/wiki/Group_contribution_method
  15. 15. Gap filling Reed, PNAS, 2006
  16. 16. Adding regulatory constraints Different paradigms Lewis, et al. Nat Rev Microb, 2012
  17. 17. Expression data as a constraint: Constraining flux E-flux Colijin, et al. Plos Comp Bio, 2009 + Uses continuous values for expression levels - Requires arbitrary function mapping expression to upper bound of reaction flux
  18. 18. Expression data as a constraint: Constraining flux E-flux Colijin, et al. Plos Comp Bio, 2009
  19. 19. Expression data as a constraint: Context-specific model construction Objectives: * Flux objective function (e.g. biomass) – GIMME – GIM3E Add reactions with an expression-based penalty * Minimize addition of low expression reactions – iMAT * Maximize model consistency with data – MBA – mCADRE * Pathway addition from differential expression – MADE
  20. 20. GIMME
  21. 21. http://journal.frontiersin.org/article/10.3389/fphys.2012.00299/full
  22. 22. Does GIMME work?
  23. 23. Pathways evolved on a new substrate Lewis, et al., unpublished
  24. 24. iMAT MILP framework generates a context- specific model No biomass objective function needed Maximizes the number of highly expressed reactions that are active and the number of lowly expressed reactions that are inactive Shlomi, et al., Nat Biotech, 2009
  25. 25. Metabolic Adjustment by Differential Expression (MADE) Adds/removes pathways based on differential expression Gives a view on how metabolism changes between states Jensen and Papin, Bioinformatics, 2011
  26. 26. Probabilistic Regulation of Metabolism (PROM) Chandrasekaran and Price, PNAS, 2010
  27. 27. Model construction methods Identify high expression/confidence “core” reactions Ensure that all “core” reactions are active Eliminate as many others as possible http://journal.frontiersin.org/article/10.3389/fpls.2014.00491/full
  28. 28. Machado and Herrgård, PLoS Comp Bio, 2014
  29. 29. Which to use? http://journal.frontiersin.org/article/10.3389/fpls.2014.00491/full
  30. 30. APPLYING THE METHODS TO STUDYING CANCER METABOLISM
  31. 31. Deregulated growth in cancer results from a myriad of molecular changes SNPs, indels, translocations, chromosomal aberrations Aberrant post-translational modifications Changes in DNA and histone modification Altered xenobiotic metabolism Variations in glycans Metabolic rewiring Oncometabolites
  32. 32. Contributions of metabolism to cancer Kroemer and Pouyssegur, Cancer Cell, 2008 Many mutations and changes are connected to metabolism Metabolic alterations are associated with the hallmarks of cancer Lewis and Abdel-Haleem. Front. Phys., 2013
  33. 33. Needless to say, it is not always clear how variations in genomic sequence result in different phenotypes What causes cancer?
  34. 34. Adding regulatory constraints for cancer-specific models Lewis and Abdel-Haleem. Front. Phys., 2013
  35. 35. ZnPP is an inhibitor of Hmox1 Zn2+ Frezza, et al. Nature, 2012
  36. 36. HMOX and FH are synthetically lethal Only killed cells missing FH (i.e., the cancer cells)
  37. 37. Omic analysis: improved resolution of your data Essential knowledge understand causation in biology – Physical laws (mass balance and thermodynamics) – Interactions (genome-scale metabolic pathways) – Components (-omes)
  38. 38. COBRA in Community Metabolism Dynamics of competition and community composition modeled between Geobacter sulfurreducens and Rhodoferax ferrireducens. Under low acetate flux, Rhodoferax dominates when sufficient ammonia is available. Synthetic mutualism modeled with auxotrophic E. coli mutants. The benefit of symbiosis is contrasted with the cost of sharing. Evolution in community modeled by simulating genome reduction from E. coli to Buchnera aphidicola in its aphid host. Minimal gene set was enriched in genome, and simulated gene loss order correlated with phylogenically reconstructed gene loss order Host-pathogen interaction modeled with M. tuberculosis. Internalized Mtb biomass inferred by transcriptomic data and simulation. Simulations showed a decreased glycolytic flux and increase glyoxylate shunt. Lewis, et al. Nat Rev Microb, 2012
  39. 39. Shameless plug for my website There are ~200 COBRA methods out there now… http://cobramethods.wikidot.com/
  • karthikraman_IITM

    Sep. 16, 2017

Systems biology modeling using constraint-based reconstruction and modeling.

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