1. Steady-state flux optima AB RARA x1x1 x2x2 RBRB D C Feasible flux distributions x1x1 x2x2 Max Z=3 at (x 2 =1, x 1 =0) RCRC RDRD Flux Balance Constraints: R A < 1 molecule/sec (external) R A = R B (because no net increase) x 1 + x 2 < 1 (mass conservation) x 1 >0 (positive rates) x 2 > 0 Z = 3R D + R C (But what if we really wanted to select for a fixed ratio of 3:1?)

2. FBA - Linear Program For growth, define a growth flux where a linear combination of monomer (M) fluxes reflects the known ratios (d) of the monomers in the final cell polymers. A linear programming finds a solution to the equations below, while minimizing an objective function (Z). Typically Z= growth (or production of a key compound). i reactions

3. ACCOA COA ATP FAD GLY NADH LEU SUCCOA metabolites coeff. in growth reaction Biomass Composition

4. Flux ratios at each branch point yields optimal polymer composition for replication x,y are two of the 100s of flux dimensions

5. Minimization of Metabolic Adjustment (MoMA)

6. Flux Data

7. 050100150200 0 20 40 60 80 100 120 140 160 180 200 1 2 3 4 56 7 8 9 10 11 12 1314 15 16 1718 -50050100150200250 -50 0 50 100 150 200 250 1 2 3 4 56 7 8 9 10 11 12 1314 15 16 17 18 Experimental Fluxes Predicted Fluxes -50050100150200250 -50 0 50 100 150 200 250 1 2 3 4 56 7 8 9 10 11 12 13 14 15 16 1718  pyk (LP) WT (LP) Experimental Fluxes Predicted Fluxes Experimental Fluxes Predicted Fluxes  pyk (QP)  =0.91 p=8e-8  =-0.06 p=6e-1  =0.56 P=7e-3 C009-limited

8. Competitive growth data: reproducibility Correlation between two selection experiments Badarinarayana, et al. Nature Biotech.19: 1060

9. Competitive growth data  2 p-values 4x10 -3 1x10 -5 Position effects Novel redundancies On minimal media negative small selection effect Hypothesis: next optima are achieved by regulation of activities. LP QP

10. Lab evolution optimization C.phTolonenAlcohol resistance E.coReppas/LinTrp/Tyr exchange E.coLenskiCitrate utilization E.coPalssonGlycerol utilization E.coEdwards Radiation resistance E.coIngramLactate production E.coStephanopoulosEthanol resistance E.coMarliereThermotolerance M.tbJ&JDiarylquinoline resistance E.coDuPont1,3-propanediol production

11. Non-optimal evolves to optimal Ibarra et al. Nature. 2002 Nov 14;420(6912):186-9. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

12. Cross-feeding symbiotic systems: aphids & Buchnera obligate mutualism nutritional interactions: amino acids and vitamins established 200-250 million years ago close relative of E. coli with tiny genome (641kb) Aphids Internal view of the aphid. (by T. Sasaki) Bacteriocyte (Photo by T. Fukatsu) Buchnera (Photo by M. Morioka) http://buchnera.gsc.riken.go.jp

13. Shigenobu et al. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp.APS. Nature 407, 81-86 (2000).

14. 14 Trp & Tyr (key pharma precursors) Cross- feeding synthetic ecosystem (syntrophic co-culture)

15.  trp/  tyrA pair of genomes shows the best co-growth Reppas, Lin & Church ; Shendure et al. Accurate Multiplex Polony Sequencing of an Evolved Bacterial Genome(2005) Science 309:1728 Second Passage First Passage Covariance in lab evolution

16. Sequence monitoring of evolution (optimize transport & drug resistance) Sequence Reppas, Lin & Church

17. 17 Evolved syntrophic strain pairs Trp  Tyr 

18. 18 Reading lab-evolved genomes sequenced across time & within each time-point Independent lines of Trp  & Tyr  co-culture 5 OmpF: (pore: large,hydrophilic > small) 42R-> G,L,C, 113 D->V, 117 E->A 2 Promoter: (cis-regulator) -12A->C, -35 C->A 5 Lrp: (trans-regulator) 1b , 9b , 8b , IS2 insert, R->L in DBD. Reppas, Shendure, Porecca -12 -11 -10 -9 -8 -7 -6 At late times Tyr  becomes prototrophic!

19. 19 Resynthesis of mutant combinations --- Additive effects insensitive to order of mutation