model-data integration . issues of flux optimality & polymer mechanics of 4d cell models

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

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Post- 300 genomes &

3D structures

DoD Relevance: Accurate Bio I/O Engineering

Over-determinedCalculable Protein folding vs. crystallographyAccurate Comprehensive/Quantitative Bio-Systems Embrace outliers Analytic & Synthetic Useful Computer-Aided-Design (CAD)

>>INTEGRATION<<

DNA RNA Protein: in vivo & in vitro interactions

Metabolites

Replication rate

Environment

Technical challenge: Integrating Measures & Models

Microbes Cancer & stem cells DarwinianIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

Human Red Blood CellODE model200 measured parameters

GLCe GLCi

G6P

F6P

FDP

GA3P

DHAP

1,3 DPG

2,3 DPG

3PG

2PG

PEP

PYR

LACi LACe

GL6P GO6P RU5PR5P

X5P

GA3P

S7P

F6P

E4P

GA3P F6P

NADPNADPH

NADPNADPH

ADPATP

ADPATP

ADP ATPNADHNAD

ADPATP

NADHNAD

K+

Na+

ADP

ATPADP

ATP

2 GSH GSSGNADPH NADP

ADO

INO

AMP

IMPADOe

INOe

ADE

ADEeHYPX

PRPP

PRPP

R1P R5PATP

AMPATP

ADP

Cl-

pH

HCO3-

Jamshidi, Edwards, Fahland, Church, Palsson, B.O. (2001) Bioinformatics 17: 286.(http://atlas.med.harvard.edu/gmc/rbc.html)

http://atlas.med.harvard.edu/gmc/rbc.html











Gene deletions

Nor

mal

ized

opt

imal

gro

wth

Linear Programming Flux Balance Analysis

(vko=0)

Minimal Perturbation Analysisfor the analysis of non-optimalmetabolic phenotypes

Daniel Segre

Challenge #1: Suboptimality of mutants --integrating growth rate

and flux data

This is a Quadratic Programming (QP) problem:

Minimize Dist=i(xi-ai)2

given Sx=b ; x 0

Minimize (xTQx)/2 + aTx given Sx=b ; x 0

Standard form:

Optimal (FBA)

Suboptimal(MPA)

p = 4·10-3

p = 10-5

2 test for prediction of essential genes:

0 50 100 150 2000

20

40

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80

100

120

140

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200

1

2

3

456

78

9

10

11121314

15

16

17 18

C009-limited

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3456

78

910

11121314

1516

17

18

Experimental Fluxes

Pre

dic

ted

Flu

xes

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3

456

78

910

111213

14

15

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1718

pyk (LP)

WT (LP)

Experimental Fluxes

Pre

dic

ted

Flu

xes

Experimental Fluxes

Pre

dic

ted

Flu

xes

pyk (QP)

=0.91p=8e-8

=-0.06p=6e-1

=0.56P=7e-3

DNA RNA Protein: in vivo & in vitro interactions

Metabolites

Replication rate

Environment

Technical challenge: Integrating Measures & Models

Microbes Cancer & stem cells DarwinianIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

Minimal Perturbation Analysisfor the analysis of non-optimalmetabolic phenotypes

Challenge #1: Suboptimality of mutants --integrating growth rate

and flux data

Polymer mechanics of 4D cell models

(Automating integration of data)

Challenge #2: integrating proteomics & in vivo crosslinking data

Mapping genome foldingDNA:DNA, DNA:protein, protein:protein in vivo crosslinks

Dekker etal. Science 2002 295:1306-11 Capturing chromosome conformation.

In vivo crosslinking DNA-binding proteins

Comparison of Quantification Methods

0.001

0.01

0.1

1

10

100

0.0001 0.001 0.01 0.1 1 10 100

Fractional Composition (percent - total intensity all peptides)

Fra

cti

on

al

Co

mp

os

itio

n (

pe

rce

nt)

dps

rpoc

rpob

hns

dbha

ssb

gyrb

ihfalon

ihfb

top1uvra

crp

argr

nusahrpa

sspa

fur

Retention time min

PS

W CM

VAR

C C T KD

Q GAG

LF E K

[Optional 1st & 2nd Protein dimensions: Subcellular fractions, Sizing of native protein complexes

1st peptide Dimension: Strong Cation Exchange Charge

2nd peptide Dimension: Reverse Phase Chromatography Hydrophobicity

3rd peptide Dimension: Mass Spectrometry Mass per charge

Multidimensional protein and peptide separations for MS quantitation

m/z

Β.A.

C.

rt1

rt2

rt3

KCL_MS0002_BSA3 #900 RT: 26.79 AV: 1 NL: 1.93E4T: + p ESI Full ms [ 400.00-2000.00]

600 800 1000 1200 1400 1600 1800 2000

m/z

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Re

lative

Ab

un

da

nce

666.7488.7

595.9

579.0

686.9

897.2 943.9720.1 1259.3

815.9 1075.5 1695.9

1184.8 1626.71415.2

1335.11047.1

1563.71888.0

1736.01506.5

MS1KCL_MS0002_BSA3 #903 RT: 26.88 AV: 1 NL: 2.44E4T: + p ESI Full ms [ 400.00-2000.00]

600 800 1000 1200 1400 1600 1800 2000

m/z

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lative

Ab

un

da

nce

424.2

666.8488.7

573.5

686.6

740.7

1249.9 1329.1979.5 1157.5 1941.8

1026.8877.3 1588.8

748.11376.7 1504.9

1640.9 1931.11807.2

1888.3


600 800 1000 1200 1400 1600 1800 2000

m/z

0

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lative

Ab

un

da

nce

488.8

538.9

641.5

610.1813.9684.1 1133.5

1303.4833.7

1384.8901.1 996.9

1524.1 1769.61158.2 1605.81424.1

1670.51839.6

1847.0


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

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75

80

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100

Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

35

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50

55

60

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

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10

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30

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

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10

15

20

25

30

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50

55

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

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10

15

20

25

30

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

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lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

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50

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4


600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

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Re

lative

Ab

un

da

nce

488.8

578.7

741.9

683.9

1195.5

985.5 1179.5856.0

893.9 1249.9825.5

1067.31279.9

1577.01349.01901.51470.4

1605.01688.9

1919.4

MS1

D.

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.

199419951996199719981999200020012002

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

Small sequenced genomes (excludes organelle/symbionts)

Mollicutes = cell-wall-less bacteria, a subgroup of Clostridia “gram-positive”o Acholeplasmataceae

•Acholeplasma, Anaeroplasma, Phytoplasmao Mycoplasmatales

•Entomoplasmataceae (florum)•Mycoplasmataceae pulmonis urealyticum pneumoniae genitalium (mobile)•Spiroplasmataceae

Megabases

Motility

Species nm/ sec Replicate Temp

M. mobile 3000 5 hr 25M. pneumoniae 300 8 37M. florum 0 1.5 30U. urealyticum 0 >10 37

E.coli 20000 0.4 37H. sapiens 1000 >10 37

RNA Pol / ribosome 20 (=50 nt/s)E.coli DNA Pol3 300 (=1000 nt/s)

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.

Mycoplasma pneumoniae

Regula, et al, Microbiology 147:1045-57,scale bar = 100 nm

Hypothetical mechanisms

Protein filament

Anchor to membrane

Linear motor

Adhesion Protein

Cell membrane

Direction of cellmovement

Adhesion moleculesdiscarded

Adhesion moleculesrecycled

Motors recycled

?

?

?

Solid surface

A

B C D

D B

C

Proteo-genomic mapping

(of peptide data

in 3 forward & 3 reverse

frames)

A

B C D

D B

C

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.

Constraints

•Replication

•Membrane-bound polyribosomes

•Other RNA and/or protein complexes

•Metabolism

•DNA Structural Forces

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?

Paired fork modelDingman 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 chromosomereplication. 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.

http://wwwmc.bio.uva.nl/texel/tekst/norris.html

Constraints

•Replication

•Membrane-bound polyribosomescould 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

Origin

Blue: First MPN gene#Green : Mid gene # 344 (ter)Red: Last gene# 688

Side view, no replication (gene#)

Off-axial view, no replicated segments,unoptimizedmembrane

Yellow: MembranePink: Ribosomal White: Hypothetical & abundantGreen : Misc. abundant Blue: Weak

Axial view, no replicated segments


Origin


Side view, no replicated segments

Origin

Blue: Origin of replicationRed: Terminus

Side view, no replication (dis from ori)

ji

jii

i RRwMwC,

21 ),d(),d(

R1

R2

M1 M2

M3

Simple example cost function for chromosome structure optimization

2002_5_16_h18_42 31.5783 0.0595431 0.444777 -0.148005 -0.12554 39.676 0.0072412002_5_16_h19_0 61.4522 0.046929 -0.0010534 -0.37642 0.64887 -7.9804 -0.12812002_5_16_h19_19 91.2823 0.075882 0.16159 -0.2373 1.0718 8.0774 0.0763642002_5_16_h19_34 45.8961 0.10725 0.165795 -0.292295 -0.0370155 46.2283 0.34542002_5_16_h19_42 38.601 0.0410951 0.363854 0.154569 0.0889424 24.162 0.12032002_5_16_h20_3 35.3927 0.0355828 -0.434093 0.17439 0.0015235 -24.9479 -0.029682002_5_16_h20_30 36.5715 0.0495523 0.0201888 0.533363 0.04049 -11.7067 -0.07172002_5_16_h20_50 108.2712 -0.03419 0.366322 -0.216694 -1.30726 -23.67 0.01812002_5_16_h21_5 45.4948 0.022745 0.44564 -0.26902 -0.18342 -9.5072 0.271892002_5_16_h21_50 50.4768 0.172497 -0.282122 -0.285109 0.478558 -46.2911 0.27582002_5_16_h21_56 37.6382 0.0304836 0.398325 0.201159 0.0797413 17.013 -0.812002_5_16_h23_41 35.4194 0.0445114 0.532795 0.0134364 0.117782 -42.2785 0.4512002_5_17_h0_2 39.8033 0.11543 -0.006943 -0.426032 -0.128618 -35.8674 -0.030492002_5_17_h0_10 62.7409 0.0093794 0.040845 -0.10502 0.35003 3.4834 0.237642002_5_17_h4_12 47.0811 0.116387 0.146311 -0.520041 -0.28928 20.3289 0.17002002_5_17_h4_20 33.5733 0.096 0.00628 0.547581 0.0413792 22.1782 -0.15982002_5_17_h4_29 41.1507 0.167149 0.422391 0.126038 0.59806 38.4758 0.10792002_5_17_h4_35 46.4101 0.0765229 0.106407 0.460038 0.350776 12.6997 -0.010972002_5_17_h4_50 31.2508 0.0209708 0.484708 -0.131666 0.0525948 17.7536 -0.078832002_5_17_h5_41 41.8434 0.0638499 0.411257 0.20358 0.380453 19.9535 -0.044102002_5_17_h5_54 31.7824 0.0219507 0.568525 -0.0296989 -0.25155 10.4541 0.016612002_5_17_h6_39 42.8122 0.21156 0.003633 -0.502632 0.315238 -61.1441 0.396042002_5_17_h6_45 31.5284 0.026136 0.52898 -0.0904436 -0.0902993 -25.0525 0.11012002_5_17_h7_17 44.8789 0.069805 -0.00365152 -0.539196 0.179759 -18.5657 0.01892002_5_17_h7_26 110.863 0.231782 0.311698 0.218959 -1.51978 11.0336 0.014072002_5_17_h7_34 27.5664 0.0463924 0.44446 0.077077 -0.237724 -26.988 -0.02722002_5_17_h7_51 43.5492 0.0300962 0.230355 0.293637 0.0425634 12.5355 -0.02752002_5_17_h8_15 44.922 0.107868 0.0263435 -0.554559 -0.298406 -18.3352 0.04061

E_finals

Searching six helical parametersfor chromosomal fold

0 50 100 150 200 250 300 350 400 450 500

0

200

400

600

800

1000

1200

1400

1600

1800

time steps

Ene

rgy

Monte carlo minimization of the model fit to constraints.

2002_5_17_h5_54 70.5984 31.7824

2002_5_16_h20_3 95.1449 35.3927

2002_5_17_h4_20 92.7126 33.5733

2002_5_17_h4_50 749.4929 31.2508

data_2002_5_19_h0_40

data_2002_5_16_h18_42

data_2002_5_16_h19_34

data_2002_5_16_h21_50

data_2002_5_16_h19_42

data_2002_5_16_h21_56

data_2002_5_16_h20_3

data_2002_5_16_h19_0

data_2002_5_16_h20_30

data_2002_5_16_h21_5

Origin

Blue: Left replicated segment (yelgr=high gene#)Red: Right (i.e. middle) segmentAqua: unduplicated segment of the circular genome

Avoidance of entanglement throughout cell cycle

M. pneumoniae genes generally point away from Ori

More significant if abundance

data are integrated

Alignment of known motors:Polymerases,b

ribosomes, F1 ATPase

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

model-data integration . issues of flux optimality & polymer mechanics of 4d cell models

Documents

flux datathis

flux datapolymer mechanics

d cell modelspost

growth rate

suboptimality of mutants

optimal fbasuboptimalmpap

ixiai2given sx

modeldata integration