Metabolic/Subsystem Reconstruction - PowerPoint PPT Presentation

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Metabolic/Subsystem Reconstruction
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Metabolic/Subsystem Reconstruction

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  1. Metabolic/Subsystem Reconstruction And Modeling

  2. Given a “complete” set of genes… • Assemble a “complete” picture of the biology of an organism? • Gene products don’t generally function in isolation • The whole is greater than the sum of the parts? Or can it also be less?

  3. A few examples of higher order entities (multiple gene products and even some additional components) • Protein complexes (ribosomes, enyzmes, secretion systems, etc.) • Pathways • Metabolism (linked pathways) • Processes (chemotaxis, splicing, etc.) • Cellular structures (membrane, cell wall, etc.)

  4. Metabolic Reconstruction • Determination of which metabolic pathways are present in an organism based on the genome content • Can provide insight into organisms as well as environments • But, we can only reconstruct what we recognize

  5. KEGG (Kyoto Encyclopedia of Genes and Genomes)

  6. KEGG-Reference Pathway Overview

  7. KEGG - Escherichia coli MG1655 overview

  8. KEGG- Citrate Cycle

  9. KEGG – Citrate Cycle (E. coli MG1655)

  10. Mouse over EC number (succinatedehydrogenase)

  11. KEGG • Other functionality • Growing • Automated annotation server for assigning genes from a new genome to pathways • Map subsets of genes to pathways (enrichment analyses)

  12. Bacterial chemotaxis – Pectobacterium atrosepticum… but what about the other 33 receptors?

  13. One size doesn’t fit all • Specialized pathways for individual organisms in specialized database resources • Allow for variations on a theme

  14. The SEED - variants

  15. Pathway holes can lead to discovery

  16. Metabolic Model • Computable metabolic reconstruction Five uses: • Contextualization of high-throughput data • Guiding metabolic engineering • Directing hypothesis-driven discovery • Interrogation of multi-species relationships • Network discovery

  17. Contraint-based modeling -A stoichiometric matrix, S (M x N) is constructed for an organism, where M=metabolites (rows) & N=reactions (columns) r1 r2 ……..rk The dynamic mass balance equation m1 m2 m3 … mi 1 0 -1 1 -1 -1 1 0 dmi/dt = Σ sik vk r1 k m1+m2=> m3 m3 <=> m1 + m4 Ex. -sik represent entries in S - vk represents a reaction flux that produce and/or degrade metabolite mi -Concentration of a given metabolite:mi r2 dm/dt =Sv at steady-state there is no accumulation or depletion of metabolites in the network, so the rate of production= rate of consumption, hence this balance of fluxes is represented mathematically as Sv = 0 -bounds that further constrain individual variables can be identified, such as fluxes, concentrations, and kinetic constants. (vmin < v < vmax) Irreversible reactions vmin=0, some metabolites such as O2 or CO2 have vmax=infinity, other metabolites are constrained based on experimental measurements as determined for the biomass reaction for E. coli 1 gm dry cell weight m=a vector that represents a set of metabolites v = flux vector

  18. There are normally more columns (reactions ~2,300) than rows (metabolites ~1,100) there does not exist a single solution but rather a steady-state solution space containing all possible solutions. (Thiele I. et al. 2009 PLOS Comp. Biol.)

  19. Flux Balance Analysis (FBA): FBA calculates the flow of metabolites through this metabolic network, thereby making it possible to predict the growth rate of an organism or the rate of production of a biotechnologically important metabolite. -With no constraints, the flux distribution of a biological network may lie at any point in a solution space. -When mass balance constraints imposed by the stoichiometric matrix S and capacity constraints imposed by the lower and upper bounds (ai and bi) are applied to a network, it defines an allowable solution space. -Through optimization of an objective function, FBA can identify a single optimal flux distribution that lies on the edge of the allowable solution space. (Orth, Thiele, and Palsson Nat. Biotech 2010)

  20. The Iterative reconstruction and history of the E. coli metabolic network (Feist A.F. and B.O Palsson (2008) Nature Biotechnology)

  21. Applications of the RMN of E. coli Feist A.F. and B.O. Palsson (2008) Nature Biotechnology

  22. Validation of metabolic models through comparison of in silico vs. experimental data with or without oxygen Comparison of carbon source utilization Flux Balance Analysis (FBA) Given an uptake rate for key nutrients (such as glucose and oxygen), the maximum possible growth rate of the cells can be predicted in silico. Comparison of batch growth (Becker SA, et al. (2007) Nature Protocols)

  23. Carbon source utilization results Experimental = N In silico = Y Experimental = Y In silico = N In general good agreement of in silico vs experimentalcarbon source utilization for both aerobic (>88% accurate) and anaerobic conditions (>83 % accurate).

  24. Batch growth results in MOPS minimal media + 0.2 % glucose anaerobic

  25. M. tuberculosis • Built a genome-scale model • Predicted essential genes using FBA and compared to saturated transposon-based characterization of essentiality (78% accuracy/agreement) • Compared flux through all pathways under slow and fast growth by changing nutrient uptake flux constraints

  26. Major difference in isocitratelyase and glyoxylate shunt

  27. Yeast deletion mutants • Used quantitative image analysis to measure growth of replica pinned cells on agar under 16 conditions (no growth, slow growth, wt growth) • FBA to predict growth from yeast model • 94% agreement • Refined experiments based on model (checked mutations, secondary mutations, unlinked phenotypes) • Gained insight into glycerol and raffinose catabolism