1. Biological Network Analysis: Metabolic Optimization Methods Tomer Shlomi Winter 2008

2. Linear Programming c, l, A, b, α, β are parameters Problem may be either feasible or infeasible If the problem has an unique optimal value: –It may either have a single optimal solution –Or a space of optimal solutions Alternatively, the problem may be unbounded

3. CBM Example (I)

4. CBM Example (II)

5. CBM Example (III)

6. Flux Balance Analysis Searches for a steady-state flux distribution v: Satisfying thermodynamic and capacity constraints: S∙v=0 v min ≤v ≤v max With maximal growth rate Max v biomass

7. Lecture Outline 1. Growth rate predictions a.Phenotypic Phase Plane (PPP) analysis 2. Gene knockout lethality predictions a.FBA b.Minimization of Metabolic Adjustment (MOMA) c.Regulatory On/Off Minimization (ROOM) 3. Predicting knockout strategy for metabolic production a.OptKnock b.OptStrain 4. Gene function prediction

8. 1. Growth rate predictions

9. Flux Balance Analysis (reminder) Searches for a steady-state flux distribution v with maximal growth rate: S∙v=0 v min ≤v ≤v max Max v biomass Requires bounds on metabolite uptake rates (b1)

10. Phenotype Phase Planes (PPP) (I) X axis – Succinate uptake rate Y axis – Oxygene uptake rate Z axis - Growth rate (maximal value of the objective function as function of succinate and oxygen uptake) Line of optimality

11. Phenotype Phase Planes (PPP) (II) Observations: Schilling 2001 Metabolic network is unable to utilize succinate as sole carbon source in anaerobic conditinos. Region 1: oxygen excess – this region is wasteful – (less carbon is available for biomass production since it is oxidized to eliminate the excess oxygen.) Succinate Oxygene Growth rate Region 3- the uptake of additional succinate has a negative effect. Cellular resources are required to eliminate excessive succinate.

12. Does E. coli behave according to Phenotype Phase Planes? (I) E. coli was grown with malate as sole carbon source. A range of substrate concentrations and temperatures were used in order to vary the malate uptake rate (MUR). Oxygen uptake rate (OUR) and growth rate were measured

13. Does E. coli behave according to Phenotype Phase Planes? (II) Malate/oxygen PPP Ibarra et al., Nature 2002 The experimentally determined growth rate were on the line of optimality of the PPP !

14. Does E. coli behave according to Phenotype Phase Planes? (III) Malate/oxygen PPP Ibarra et al., Nature 2002 Is the optimal performance on malate stable over prolonged periods of time? Evolution of E. coli on malate was studied for 500 generations in a single condition… 2- An adaptive evolution was observed with an increase of 19%in growth rate! 3- Same adaptive evolution was observed for succinate and Malate!

15. Does E. coli behave according to Phenotype Phase Planes? (III) Same experiments were made using glycerol as sole carbon source Day 0 – Sub optimal growth Day 1-40 – evolution toward optimal growth Day 40 –optimal growth Day 60 –optimal growth (no change) Why?

16. 2. Gene Knockout Lethality

17. Predicting Knockout Lethality (I) A gene knockout is simulated by setting the flux through the corresponding reaction to zero The corresponding reactions are identified by evaluating the Boolean gene-to-reaction mapping in the model

18. Predicting Knockout Lethality (II) A gene is predicted essential if it’s knockout yields a significant drop in the maximal possible growth rate v1 is essential for growth v6 is not essential for growth

19. Gene knockout lethality: E. coli in glycerol minimal media In total, 819 out of the 896 mutants (91%) showed growth behaviors in glycerol minimal medium in agreement with computational predictions 69% correct prediction out of the experimental essential genes

20. 2. Gene essentiality prediction Gene knockout lethality: Resolving Discrepancies (I)

21. Gene knockout lethality: Resolving Discrepancies (II)

23. 3. MOMA and ROOM

24. Minimization of Metabolic Adjustment (MOMA) (I) FBA assumes optimality of growth for wild type – evolution drives the growth rate towards optimality This assumption is not necessarily correct following a gene knockout! What other objective can capture the biological essence of these mutations? (hint – the title of this slide)

25. Minimization of Metabolic Adjustment (MOMA) (II) Assumption: following the knockout, the mutant remains as close as possible to the wild-type strain The flux distribution of mutant should also satisfy all constraints as in FBA

26. Minimization of Metabolic Adjustment (MOMA) (III) Formally: w – the wild-type optimal growth vector (obtained via FBA). v – a vector in mutant flux space. Find V m which minimizes the Euclidian distance to V wt : Min (w -v)², - minimize Euclidian distance s.t S∙v = 0, - mass balance constraints v min  v  v max - capacity constraints v j = 0, j  G - knockout constraints Solved using Quadratic Programming (QP) w v

27. Validating MOMA: Gene essentiality prediction

28. Validating MOMA: Experimental fluxes

29. Regulatory On/Off Minimization (ROOM) (I) Assumption: The organism adapts by minimizing the set of flux changes (via the regulatory system) Search for a feasible flux distribution with minimal number of changes from the wild-type ABC D E byp cof byp cof Wild-type solution Knockout solution

30. Regulatory On/Off Minimization (ROOM) (II) Min  y i - minimize changes s.t v – y ( v max - w)  w- distance constraints v – y ( v min - w)  w- distance constraints S∙v = 0,- mass balance constraints v j = 0, j  G - knockout constraints Integer variables are required to track the ‘number of changes in flux’ from the wild-type Use Boolean auxiliary variables y to reflect changes in flux between the wild-type and mutant y i =0if and only if v i = w i Formulate a MILP problem to find a pair of v and y with a minimal sum of y i ’s.

31. Validating ROOM: Alternative pathways ROOM identifies short alternative pathways to re-route metabolic flux following a gene knockout, in accordance with experimental data

32. Validating ROOM: Experimental fluxes (I) Intracellular fluxes measurements in E. coli central carbon metabolism Obtained using NMR spectroscopy in C labelling experiments Knockouts: pyk, pgi, zwf, and gnd in Glycolysis and Pentose Phosphate pathways Glucose limited and Ammonia limited medias FBA wild-type predictions above 90% accuracy 13 Emmerling, M. et al. (2002), Hua, Q. et al. (2003), Jiao, Z et al. (2003) (*) Based on a figure from Jiao, Z., et al.

33. Validating ROOM: Experimental fluxes (II) ROOM flux predictions are significantly more accurate than MOMA and FBA in 4 out of 8 experiments ROOM growth rate predictions are significantly more accurate than MOMA

34. 4. Metabolite Production

35. Constraint-based Modeling: Biotechnological Applications Design bacteria that produces chemicals of interest Bacteria Objective: Grow Fast Vanillin The major compound in Vanilla Bioengineering Objective: Produce Vanillin Bioengineering Objective Produce Vanillin

36. OptKnock Designing microbial organisms for efficient production of metabolites Finds reactions whose removal increases the production of metabolite of interest

37. OptKnock: Optimization problem (I) A nested (bi-level) optimization problem is needed

38. OptKnock: Optimization problem (I) A nested (bi-level) optimization problem is needed Reactions to remove Cells have to grow Removed reactions have zero flux The max number of reactions to remove

40. Succinate Production Strains

41. OptStrain An integrated framework for redesigning microbial production systems Step 1: Creation of universal reactions DB Step 2: Compute maximal theoretical metabolite production yield Step 3: Identifying the minimal number of required to be added to an organism to achieve the maximal production yield. Step 4: Adding the identified reactions and finding gene deletions that ensure metabolite secretion (OptKnock)

42. OptStrain: Step 1 Creation of universal reactions DB Download set of known reactions from KEGG (Kyoto Encyclopedia of Genes and Genomes) Validate reaction data consistency – remove unbalanced reaction Define a universal stoichiometric matrix S.

43. OptStrain: Step 2 Determination of maximal theoretical yield of a metabolite of interest Yield – metabolite production rate per unit of substrate uptake Use LP to find the maximal yield for different substrates, denoted R

44. OptStrain: Step 3 Identification of minimum number of non-native reactions for a host organism MILP formulation – y i represented whether reaction i should be added to the organism

45. OptStrain: Step 4 Incorporating the non-native reactions into the host organism’s stoichiometric model Eliminate genes such that biomass production is coupled with the production of the metabolite of interest OptKnock

46. Case study: Hydrogen production The highest hydrogen yield (0.126 g/g substrate consumed) is obtained for methanol

47. Case study: Hydrogen production (I) Testing E. coli on glucose media Step 3 reveals that new reactions are needed for E. coli on glucose

48. Case study: Hydrogen production (II) C. acetobutylicum - the "Weizmann Organism", after Chaim Weizmann, who in 1916 helped discover how C. acetobutylicum culture could be used to produce acetone, butanol, and ethanol from starch The knockout of 2 reactions tightly couple biomass production and metabolite hydrogen secretion

49. Case study: Vanillin production (I) Vanillin is an important flavor and aroma molecule (found in vanilla pods) Maximal theoretical production rate: 0.63 (g/g glucose) E. coli needs 3 new reactions to achieve this vanillin yield Previous bioengineering experiments have already involved the extraction of these 3 reactions from Neurospora crassa and their addition to E. coli However, the resulting vanillin production rate was only 0.15

50. Case study: Vanillin production (II) OptStrain predicts knockout sets that provide a vanillin yield of 0.57 (g vanillin/g glucose) in E. coli This is close to the maximal theoretical production rate

51. 4. Gene function prediction

52. Refining Genome Annotation A substantial fraction of the genes have unknown function An integrated computational/experimental approach for predicting gene function: –Identify discrepancies between model predictions and growth phenotyping in E. coli –An algorithm then identifies missing reactions whose addition could reconcile model predictions and experimental observations –Search for ORFs that might be responsible for these missing activities based on literature searches, sequence-homology, etc –experimental verification of the algorithm’s predictions via growth phenotypes of single-deletion strains

53. Refining Genome Annotation

54. Refining E.coli’s Annotation Identify 50 minimal medium conditions in which the model cannot explain the observed (experimental) growth Identify reactions whose addition enables growth for 26 of the environemnts 6 cases are investigated in depth

55. New Transporter Genes (I) Growth on propionate and 5-keto-D-gluconate require the addition of relevant transporters Currently, such transporters are unknown 8 potential transporters are identified via literature searches, sequence-homology, etc Only putP deletion showed reduced growth in propionate Only idnT deletion showed reduced growth in 5- keto-D-gluconate Both genes show increased expression level on these media

56. New Transporter Genes (II) The algorithm found a missing reaction that secretes a byproduct in thymidine metabolism – thymine Experimental inspection of the growth media support this finding The identity of the transporter gene remained unclear

57. Growth on D-Malate The algorithm finds two missing reactions: –D-Malate transporter –Decarboxilation of D-Malate to Pyruvate

58. References: Edwards JS, Ramakrishna R, Palsson BO. 2002. Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 77(1):27-36. Ibarra RU, Edwards JS, Palsson BO. 2002. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186-9. Segre D, Vitkup D, Church GM. 2002. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99(23):15112-7. Shlomi T, Berkman O, Ruppin E. 2005. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A 102(21):7695-700. Burgard AP, Pharkya P, Maranas CD. 2003. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647-57. Pharkya P, Burgard AP, Maranas CD. 2004. OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14(11):2367-76. Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO. 2006. Systems approach to refining genome annotation. Proc Natl Acad Sci U S A 103(46):17480-4.