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 1.3.99.1 (succinate dehydrogenase)

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: 1.Contextualization of high-throughput data 2.Guiding metabolic engineering 3.Directing hypothesis-driven discovery 4.Interrogation of multi-species relationships 5.Network discovery

18. Contraint-based modeling -A stoichiometric matrix, S (M x N) is constructed for an organism, where M=metabolites (rows) & N=reactions (columns) 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. (v min < v < v max ) Irreversible reactions v min =0, some metabolites such as O 2 or CO 2 have v max =infinity, other metabolites are constrained based on experimental measurements as determined for the biomass reaction for E. coli 1 gm dry cell weight Ex. m1 m2 m3 … m i m 1 +m 2 => m 3 m 3 m 1 + m 4 r1r1 r2r2 r 1 r 2 ……..r k -1 -1 1 0 1 0 -1 1 dm i /dt = Σ s ik v k -s ik represent entries in S - v k represents a reaction flux that produce and/or degrade metabolite m i -Concentration of a given metabolite: m i The dynamic mass balance equation m=a vector that represents a set of metabolites v = flux vector k

19. 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.)

20. 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 (a i and b i ) 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)

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

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

23. Validation of metabolic models through comparison of in silico vs. experimental data with or without oxygen (Becker SA, et al. (2007) Nature Protocols) Comparison of batch growth 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.

24. Carbon source utilization results E. coli K-12E. coli O157:H7 (EHEC)E. coli (UPEC)Salmonella StrainMG1655W3110EDL933SakaiCFTO73UTI89LT2 O2No O2O2No O2O2No O2O2No O2O2No O2O2No O2O2No O2 Tested compounds included in the model 76 55 In silico and experimental Agreement 7066716469636864676371655248 False positives 41102221301020 False negatives 294125116 61341117 In general good agreement of in silico vs experimental carbon source utilization for both aerobic (>88% accurate) and anaerobic conditions (>83 % accurate). Experimental = N In silico = Y Experimental = Y In silico = N

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

29. 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

30. Major difference in isocitrate lyase and glyoxylate shunt

33. 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