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

2. gggatttagctcagtt gggagagcgccagact gaa gat ttg gag gtcctgtgttcgatcc acagaattcgcacca 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. DNA RNA Protein: in vivo & in vitro interactions Metabolites Replication rate Environment Technical challenge: Integrating Measures & Models Microbes Cancer & stem cells Darwinian In vitro replication Small multicellular organisms RNAi Insertions SNPs

6. Human Red Blood Cell ODE model 200 measured parameters GLC e GLC i G6P F6P FDP GA3P DHAP 1,3 DPG 2,3 DPG 3PG 2PG PEP PYR LAC i LAC e GL6PGO6PRU5P R5P X5P GA3P S7P F6P E4P GA3PF6P NADP NADPH NADP NADPH ADP ATP ADP ATP ADP ATP NADH NAD ADP ATP NADH NAD K+K+ Na + ADP ATP ADP ATP 2 GSHGSSG NADPHNADP ADO INO AMP IMP ADO e INO e ADE ADE e HYPX PRPP R1P R5P ATP AMP ATP ADP Cl - pH HCO 3 - Jamshidi, Edwards, Fahland, Church, Palsson, B.O. (2001) Bioinformatics 17: 286. (http://atlas.med.harvard.edu/gmc/rbc.html)

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

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

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

11. Optimal (FBA) Suboptimal(MPA) p = 4·10 -3 p = 10 -5 22 test for prediction of essential genes:

13. 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 C009-limited -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

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

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

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

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

20. Β.Β.A. C. rt 1 rt 2 rt 3 MS1 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” o Acholeplasmataceae Acholeplasma, Anaeroplasma, Phytoplasma o Mycoplasmatales Entomoplasmataceae (florum) Mycoplasmataceae pulmonis urealyticum pneumoniae genitalium (mobile) Spiroplasmataceae Megabases

23. Motility Species  nm/ secReplicateTemp M. mobile30005 hr25 M. pneumoniae 300837 M. florum 01.530 U. urealyticum 0>1037 E.coli200000.437 H. sapiens 1000 >1037 RNA Pol / ribosome20 (=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 data in 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  500-800 kbp genome Extended diameter = 80  ~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 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. Origin Blue: First MPN gene# Green : Mid gene # 344 (ter) Red: Last gene# 688 Side view, no replication ( gene#)

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

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

38. R1R1 R2R2 M1M1 M2M2 M3M3  Simple example cost function for chromosome structure optimization

39. 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 E_final s Searching six helical parameters for chromosomal fold

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

51. data_2002_5_16_h20_3

52. data_2002_5_16_h19_0

53. data_2002_5_16_h20_30

54. data_2002_5_16_h21_5

55. Origin Blue: Left replicated segment (yelgr=high gene#) Red: Right (i.e. middle) segment Aqua: unduplicated segment of the circular genome Avoidance of entanglement throughout cell cycle

56. M. pneumoniae genes generally point away from Ori More significant if abundance data are integrated Alignment of known motors: Polymerases,b ribosomes, F1 ATPase

57. Biospice 2.0 Deliverables: toolsets for data integration & optimality assessment #1QP MPA flux & growth modeling #2: 4D-model current plan: Chromosome segregation Membrane-bound polysomes Ribosomal protein/rRNA assembly Motility (coordination with replication origin) Next few months: Other protein complexes Space filling metric Replication entanglement metric In vivo crosslinking