Model-data integration . Issues of flux optimality & polymer mechanics of 4D cell models

1. Model-data integration . Issues of flux optimality & polymer mechanics of 4D cell models DARPA BIOCOMP 23-May-2002 Thanks to Harvard/MIT Team: Jake Jaffe, Kyriacos Leptos, Matt Wright, Daniel Segre, Martin Steffen

2. gggatttagctcagttgggagagcgccagactgaa gat ttg gag gtcctgtgttcgatccacagaattcgcacca Post- 300 genomes & 3D structures

3. DoD Relevance: Accurate Bio I/O Engineering Over-determined Calculable Protein folding vs. crystallography Accurate Comprehensive/Quantitative Bio-Systems Embrace outliers Analytic & Synthetic Useful Computer-Aided-Design (CAD) >>INTEGRATION<<

4. Technical challenge: Integrating Measures & Models Environment Metabolites RNAi Insertions SNPs Protein: in vivo & in vitro interactions RNA DNA Replication rate Microbes Cancer & stem cells Darwinian In vitro replication Small multicellular organisms

6. Human Red Blood CellODE model200 measured parameters ADP ATP 1,3 DPG NADH 3PG NAD GA3P 2PG 2,3 DPG FDP DHAP ADP PEP ATP ADP F6P ATP PYR R5P GA3P F6P NADH G6P GL6P GO6P RU5P NAD LACi LACe X5P S7P E4P ADP NADP NADP NADPH NADPH ATP GLCe GLCi Cl- 2 GSH GSSG GA3P F6P ADP K+ NADPH NADP pH ATP Na+ ADP HCO3- ADO AMP ADE ADP ATP PRPP INO IMP ATP ADOe AMP PRPP ATP INOe Jamshidi, Edwards, Fahland, Church, Palsson, B.O. (2001) Bioinformatics 17: 286. R5P R1P ADEe HYPX (http://atlas.med.harvard.edu/gmc/rbc.html)

7. Linear Programming Flux Balance Analysis Normalized optimal growth (vko=0) Gene deletions

8. Challenge #1: Suboptimality of mutants --integrating growth rate and flux data MinimalPerturbationAnalysis for the analysis of non-optimal metabolic phenotypes Daniel Segre

10. This is a Quadratic Programming (QP) problem: Minimize Dist=i(xi-ai)2 given Sx=b ; x  0 Standard form: Minimize (xTQx)/2 + aTx given Sx=b ; x  0

11. c2 test for prediction of essential genes: p = 4·10-3 Optimal (FBA) p = 10-5 Suboptimal(MPA)

13. C009-limited 200 WT (LP) 180 7 8 160 140 9 120 10 Predicted Fluxes r=0.91 p=8e-8 100 11 14 13 12 3 1 80 60 40 16 20 2 6 5 15 4 17 18 0 0 50 100 150 200 Experimental Fluxes 250 250 Dpyk (LP) Dpyk (QP) 200 200 18 7 r=0.56 P=7e-3 8 r=-0.06 p=6e-1 150 150 7 8 2 Predicted Fluxes Predicted Fluxes 10 9 13 100 9 100 11 12 3 1 14 10 14 13 11 12 3 50 50 6 5 4 16 16 2 15 6 5 18 17 15 17 0 0 4 1 -50 -50 -50 0 50 100 150 200 250 -50 0 50 100 150 200 250 Experimental Fluxes Experimental Fluxes

14. Technical challenge: Integrating Measures & Models Environment Metabolites RNAi Insertions SNPs Protein: in vivo & in vitro interactions RNA DNA Replication rate Microbes Cancer & stem cells Darwinian In vitro replication Small multicellular organisms

15. Challenge #1: Suboptimality of mutants --integrating growth rate and flux data MinimalPerturbationAnalysis for the analysis of non-optimal metabolic phenotypes

16. Challenge #2: integrating proteomics & in vivo crosslinking data Polymer mechanics of 4D cell models (Automating integration of data)

17. Mapping genome foldingDNA:DNA, DNA:protein, protein:protein in vivo crosslinks Dekker etal. Science 2002 295:1306-11 Capturing chromosome conformation.

18. In vivo crosslinking DNA-binding proteins

19. Multidimensional protein and peptide separations for MS quantitation R A A P S V G F M W L C C E C K T K D Q G [Optional 1st & 2nd Protein dimensions: Subcellular fractions, Sizing of native protein complexes 1st peptide Dimension:Strong Cation Exchange Charge 2nd peptide Dimension:Reverse Phase ChromatographyHydrophobicity m/z Retention time min 3rd peptide Dimension:Mass Spectrometry Mass per charge

20. A. Β. MS1 MS1 MS1 MS1 MS1 MS1 MS1 MS1 MS1 MS1 MS1 MS1 rt1 rt2 rt3 C. D.

21. Minimal Cell Projects • The first FULL proteome model would benefit from a small number of natural cell states & genes. • 3D-structure of a cell during replication & motility. • Genome engineering / complete synthesis.

22. Small sequenced genomes (excludes organelle/symbionts) • Mollicutes = cell-wall-less bacteria, a subgroup of Clostridia “gram-positive” • Acholeplasmataceae • Acholeplasma, Anaeroplasma, Phytoplasma • Mycoplasmatales • Entomoplasmataceae(florum) • Mycoplasmataceae pulmonis urealyticum pneumoniae genitalium (mobile) • Spiroplasmataceae Megabases

23. Motility Species nm/ sec Replicate Temp M. mobile 3000 5 hr 25 M. pneumoniae 300 8 37 M. florum 0 1.5 30 U. urealyticum 0 >10 37 E.coli 20000 0.4 37 H. sapiens 1000 >10 37 RNA Pol / ribosome 20 (=50 nt/s) E.coli DNA Pol3 300 (=1000 nt/s)

24. Attachment organelle replication Seto S, Layh-Schmitt G, Kenri T, Miyata M. J Bacteriol 2001 183:1621 Visualization of the attachment organelle and cytadherence proteins of Mycoplasma pneumoniae by immunofluorescence microscopy.

25. Mycoplasma pneumoniae Regula, et al, Microbiology 147:1045-57, scale bar = 100 nm

26. Hypothetical mechanisms

27. Proteo-genomic mapping(of peptide datain 3 forward & 3 reverse frames)

28. Use of proteogenomic mapping to discover B. a new ORF. C. a new ORF & delete an inaccurately predicted ORF. D. N-terminal extension of an existing ORF.

29. Constraints • Replication • Membrane-bound polyribosomes • Other RNA and/or protein complexes • Metabolism • DNA Structural Forces

30. Genome folding & cell 3D structure Seto & Miyata (1999) Partitioning, movement, and positioning of nucleoids in Mycoplasma capricolum J. Bact. 181:6073 Cell = 0.5 m 500-800 kbp genome Extended diameter = 80 m ~200 transverses with each membrane encoding gene anchored to the cell surface. How to segregate this?

31. Paired fork model Dingman CW. Bidirectional chromosome replication: some topological considerations. J Theor Biol 1974 Jan;43(1):187-95. Sundin O, Varshavsky A. Terminal stages of SV40 DNA replication proceed via multiply intertwined catenated dimers. Cell. 1980 Aug;21(1):103-14. Hearst JE, Kauffman L, McClain WM. A simple mechanism for the avoidance of entanglement during chromosome replication. Trends Genet. 1998 Jun;14(6):244-7. Bouligand, Y, Norris V (2000) “Both replication forks appear to be part of a single complex or factory, as first proposed by Dingman.” http://wwwmc.bio.uva.nl/texel/tekst/norris.html Roos M, Lingeman R, Woldringh CL, Nanninga N. Biochimie 2001 Jan;83(1):67-74 Experiments on movement of DNA regions in Escherichia coli evaluated by computer simulation.

32. Constraints • Replication • Membrane-bound polyribosomes • could anchor the RNA polymerase and hence the gene’s DNA to within 20 nm of the cell surface. • Other RNA and/or protein complexes • Metabolism • DNA Structural Forces

33. Side view, no replication (gene#) Origin Blue: First MPN gene# Green : Mid gene # 344 (ter) Red: Last gene# 688

34. Off-axial view, no replicated segments,unoptimizedmembrane Yellow: Membrane Pink: Ribosomal White: Hypothetical & abundant Green : Misc. abundant Blue: Weak

35. Axial view, no replicated segments Yellow: Membrane Pink: Ribosomal White: Hypothetical & abundant Green : Misc. abundant Blue: Weak

36. Side view, no replicated segments Origin Yellow: Membrane Pink: Ribosomal White: Hypothetical & abundant Green : Misc. abundant Blue: Weak

37. Side view, no replication (dis from ori) Origin Blue: Origin of replication Red: Terminus

38. M1 M2 m R2 M3 R1 Simple example cost function for chromosome structure optimization

39. Searching six helical parametersfor chromosomal fold s E_final 2002_5_16_h18_42 31.5783 0.0595431 0.444777 -0.148005 -0.12554 39.676 0.007241 2002_5_16_h19_0 61.4522 0.046929 -0.0010534 -0.37642 0.64887 -7.9804 -0.1281 2002_5_16_h19_19 91.2823 0.075882 0.16159 -0.2373 1.0718 8.0774 0.076364 2002_5_16_h19_34 45.8961 0.10725 0.165795 -0.292295 -0.0370155 46.2283 0.3454 2002_5_16_h19_42 38.601 0.0410951 0.363854 0.154569 0.0889424 24.162 0.1203 2002_5_16_h20_3 35.3927 0.0355828 -0.434093 0.17439 0.0015235 -24.9479 -0.02968 2002_5_16_h20_30 36.5715 0.0495523 0.0201888 0.533363 0.04049 -11.7067 -0.0717 2002_5_16_h20_50 108.2712 -0.03419 0.366322 -0.216694 -1.30726 -23.67 0.0181 2002_5_16_h21_5 45.4948 0.022745 0.44564 -0.26902 -0.18342 -9.5072 0.27189 2002_5_16_h21_50 50.4768 0.172497 -0.282122 -0.285109 0.478558 -46.2911 0.2758 2002_5_16_h21_56 37.6382 0.0304836 0.398325 0.201159 0.0797413 17.013 -0.81 2002_5_16_h23_41 35.4194 0.0445114 0.532795 0.0134364 0.117782 -42.2785 0.451 2002_5_17_h0_2 39.8033 0.11543 -0.006943 -0.426032 -0.128618 -35.8674 -0.03049 2002_5_17_h0_10 62.7409 0.0093794 0.040845 -0.10502 0.35003 3.4834 0.23764 2002_5_17_h4_12 47.0811 0.116387 0.146311 -0.520041 -0.28928 20.3289 0.1700 2002_5_17_h4_20 33.5733 0.096 0.00628 0.547581 0.0413792 22.1782 -0.1598 2002_5_17_h4_29 41.1507 0.167149 0.422391 0.126038 0.59806 38.4758 0.1079 2002_5_17_h4_35 46.4101 0.0765229 0.106407 0.460038 0.350776 12.6997 -0.01097 2002_5_17_h4_50 31.2508 0.0209708 0.484708 -0.131666 0.0525948 17.7536 -0.07883 2002_5_17_h5_41 41.8434 0.0638499 0.411257 0.20358 0.380453 19.9535 -0.04410 2002_5_17_h5_54 31.7824 0.0219507 0.568525 -0.0296989 -0.25155 10.4541 0.01661 2002_5_17_h6_39 42.8122 0.21156 0.003633 -0.502632 0.315238 -61.1441 0.39604 2002_5_17_h6_45 31.5284 0.026136 0.52898 -0.0904436 -0.0902993 -25.0525 0.1101 2002_5_17_h7_17 44.8789 0.069805 -0.00365152 -0.539196 0.179759 -18.5657 0.0189 2002_5_17_h7_26 110.863 0.231782 0.311698 0.218959 -1.51978 11.0336 0.01407 2002_5_17_h7_34 27.5664 0.0463924 0.44446 0.077077 -0.237724 -26.988 -0.0272 2002_5_17_h7_51 43.5492 0.0300962 0.230355 0.293637 0.0425634 12.5355 -0.0275 2002_5_17_h8_15 44.922 0.107868 0.0263435 -0.554559 -0.298406 -18.3352 0.04061

40. Monte carlo minimization of the model fit to constraints.

41. 2002_5_17_h5_54 70.5984 31.7824

42. 2002_5_16_h20_3 95.1449 35.3927

43. 2002_5_17_h4_20 92.7126 33.5733

44. 2002_5_17_h4_50 749.4929 31.2508

45. data_2002_5_19_h0_40

46. data_2002_5_16_h18_42

47. data_2002_5_16_h19_34

48. data_2002_5_16_h21_50

49. data_2002_5_16_h19_42

50. data_2002_5_16_h21_56