The cellular nucleotides, metabolic fatty acids, etc. machine - - PDF document

• Metabolism primer
• Analysis of iJR904 for E. coli

Network topology and simplification Steady-state solutions Growth under varying oxygen levels: flux pattern and regulation

• Summary and conclusions

Lei-Han Tang

Department of Physics, Hong Kong Baptist University

International Workshop on Complex Systems and International Workshop on Complex Systems and Networks 2007, Networks 2007, Guilin Guilin, China , China

Metabolic Networks Metabolic Networks

Organization, Biomass Production, and Other Issues Organization, Biomass Production, and Other Issues

Metabolism

Complex networks Systems biology Synthetic biology Why Metabolism?

Mathematical underpinning Engineering

• utlet

Data integration and modeling

The cellular metabolic machine

Carbon source Other elements Redox agents Biomass/ energy waste

glucose + NH4 amino acids, nucleotides, fatty acids, etc.

Biosynthesis

the flow chart

Nicholson metabolic pathway chart

Full metabolic network of

• S. cerevisiae

Complexity 980 reactions involving 981 compounds catalyzed by 449 different enzymes 1163 yeast ORFs with EC assignment (about 20% of yeast genome)

ADP + Phosphoenolpyruvate <=> ATP + Pyruvate pyk 2-Phospho-D-glycerate <=> Phosphoenolpyruvate + H2O eno E4.2.1.11: E2.7.1.40:

• H. Jeong, B. Tombo, R. Albert, Z.
• N. Oltvai, A.-L. Barabasi, Nature

407, 651 (2000).

Metabolic networks are scale free!

2.2

( ) P k k − ∼ scale free

Evolutionarily conserved across all three branches

• f living organisms

k = connection degree

Biologist/biochemist: every reaction matters! Statistical physicist: another proof that scale free

networks are ubiquitous!

More refined characterization

• f network topology

Biological function

…

John Doyle: Organized complexity http://www.cds.caltech.edu/~doyle/ Cruise control Electronic ignition Temperature control Electronic fuel injection Anti-lock brakes Electronic transmission Electric power steering (PAS) Air bags EGR control Active suspension Basic elements: proteins Functional units: network motifs/ pathways Systems level organization: protocols Origin of complexity: Robustness!

Basic biochemistry of metabolism

Energy carbon flow

Precursor molecules Synthetic efficiency controlled by energy and redox power

Network Organization: Scale Rich!

Reiko Tanaka and John Doyle, q-bio: 0410009

• E. Coli, iJR904

Simplifying network topology Analyze flux pattern under varying growth conditions Study regulatory interactions

in silico

• rganisms

constructed by Palsson’s group at UCSD

Collection of

• rganism-specific

reactions leading to biomass production Allow for simulation of different growth conditions (nutrient uptake, O2 availability, etc.) Outcome: growth rate and flux pattern

Yeast iND750 750 ORFs with 1149 reactions and 646 metabolites, fully compartmentalized

• H. pylori

iIT341 341 ORFs with 476 reactions and 412 metabolites E.coli iJR904 904 ORFs with 931 reactions and 625 metabolites

NetSim: Specific objectives

• 1. Highlight carbon flow for easy comparison with relevant

experimental flux measurements (clearly marked main roads, small streets, roundabouts, market place, etc.)

• 2. A quantitative understanding of the horizontal coupling

between pathways (physico-chemical constraints such as energy/redox balance)

• 3. Incorporation of regulatory interactions with a clear understanding
• f their physiological role (traffic control and related issues)
• 4. A framework to integrate protein abundance, enzyme activity, and

flux measurements for dynamic simulation

Flux pattern: aerobic growth on glucose minimal medium

282 out of 1149 reactions with nonzero flux

Thickness of arrows proportional to reaction flux

amino acids glucose TCA cycle phospholipids nucleotides

Flux pattern, glucose-minimal

Core network from iJR904

purine pyrimidine arginine proline urea cycle sugar amino acids

Metabolism in action: network traffic

consider Steady State Flow (e.g. exponential growth phase) flux through th reaction

i

v i = Network state specified by flux vector

1 2

( , , , )

N

v v v = v …

ADP + Phosphoenolpyruvate <=> ATP + Pyruvate Example: E2.7.1.40:

pvk

v

pyr ,pyr ',pyr ' producing rxn consuming rxn i i i i

dC m v m v dt = − =

∑ ∑

Law of mass action: Or more generally,

= S v i

stoichiometric matrix = S

i

v Constrained by i) thermodynamics (70% irreversible) ii) enzyme abundance and activity Regulation and control in a living cell, but the mechanisms are extremely complex Current models: find flux solution through a physiologically meaningful objective function (FBA)

flux-balance analysis with realistic network topology

carbon source (e.g. glucose)

provides carbon skeleton and energy

freely available compounds

Na+, K+, NH4

+, SO4

• 2,

HPO4

• 2, H2O, CO2

aerobic/anaerobic (oxygen)

biomass

= S v i

waste

Maximize using LP

Two recent studies in our group

a) Independence in biosynthesis

Glucose + NH4 i) Dedicated flow: Optimize production of individual amino acids ii) Combined flow: Optimize production of the basket of AA

Q: can we achieve ii) from linear superposition of i)?

1.3512 Superposition yield 4.5074 dAMP 1.4158 All above 9.2308 L-Valine 0.96685 zymosterol 5.5835 L-Tyrosine 0.0064781 triglyceride 4.0731 L-Tryptophan 0.0084474 phosphatidyl-1D-myo- inositol 12.75 L-Threonine 0.0089428 phosphatidylserine 17.143 L-Serine 0.0089537 phosphatidylethanolamine 9.5397 L-Proline 0.0079944 Phosphatidylcholine 5.3353 L-Phenylalanine 0.0093685 Phosphatidate 6.1824 L-Methionine 0.91971 Ergosterol 6.5896 L-Lysine 4.6053 Trehalose 6.6667 L-Leucine 8.9744 Mannan 7.3118 L-Isoleucine 8.9744 glycogen 6.7653 L-Histidine 8.9744 1,3-beta-D-Glucan 20 Glycine 5.9658 UMP 10 L-Glutamate 4.5952 GMP 10 L-Glutamine 5.6006 CMP 8.9135 L-Cysteine 4.6833 AMP 17.143 L-Aspartate 4.8963 dTMP 12.577 L-Asparagine 4.4553 dGMP 6.6849 L-Arginine 5.512 dCMP 17.143 L-Alanine Optimal yield Biomass constituent Optimal yield Biomass constituent

Result from in silico study

The ratio of superposition yield to

• ptimal yield is

95.54%! Synthesis of individual biomass components are only weakly coupled!

Coupling through complementary needs in energy/redox potential

Decoupling of amino acid pairs by relaxation on energy requirement and redox balance

16 31 (36)

• H. pylori

5 5 65 160 (190)

• E. coli

37 61 61 158 (190)

• S. cerevisiae

Free NADPH Free NADH Free ATP Original model Organism

Syntheses of two amino acids are coupled if one is abundant in certain redox/energy potential pair and the other is deficient of such pair. Free supply of the potential pair would thus eliminate coupling.

Two recent studies in our group

b) Aerobic/anaerobic growth and regulation

• E. coli has three metabolic modes to grow

depending on the availability of electron acceptors: Aerobic respiration uses TCA cycle for pyruvate

• xidation, while ATP is produced via the electron

transport chain with O2 being the terminal electron acceptor Anaerobic respiration uses alternative terminal electron acceptors, such as NO3- in the electron transport chain Fermentation occurs when ATP is produced through substrate level phosphorylation.

Q: Does FBA yield the same description? And how is the regulation of traffic achieved in bacteria?

Biomass yield under varying oxygen levels (in silico simulation, glucose minimal medium)

Branch point key to regulation

Transcriptional regulatory interaction in aerobic/anaerobic switch

The aerobic/anaerobic response regulation pathways in E.

• coli. Adapted from (Sawers G, The aerobic/anaerobic

interface, 1999)

Main Regulators: The

aerobic/anaerobic response regulation system includes the

• ne component furmarate &

nitrate reduction (Fnr) protein and the two-component anoxic redox control (Arc) system.

Enzymes regulated by FNR and ArcA/B

ACKr PFL PDH PTAr

FNR arcA/B

• Consistent with flow pattern
• btained from FBA
• Detailed dynamic modeling to be

carried out

Further downstream: The TCA Cycle

Anaerobiosis Aerobiosis [4.2.1.3] [4.1.3.6] [1.3.99.1] [4.2.1.2] [2.3.3.1] [1.1.1.42]

[1.2.4.2] [2.3.1.61] [1.8.1.4]

[1.1.1.37] [1.1.99.16] [6.2.1.5]

FNR ArcA/B

Metabolism summary:

Complexity in exchange for economy and robustness

Over a 1000 metabolic intermediates

NH4+

Provides support for cell’s need for building material and energy Bacteria: efficient usage of nutrients crucial for species propagation

Many entry and exit points Many alternative pathways Flow modulated by enzyme abundance and activity Supported by a complex regulatory system at various levels

• Metabolic network topology can be greatly simplified when viewed

in terms of vertical links (pathways) and horizontal couplings (currencies, carriers, etc.)

• Synthesis of biomass components is highly independent. The

horizontal independence is due to the efficiency of individual synthesis, while waste is mostly associated with energy/redox requirements to drive a synthetic pathway. Hence cell is able to produce varying amount of amino acids in response to different external or internal needs, with little influence to the production of

• ther amino acids.
• Branch points of flow are key to metabolic regulation.

Global properties of the metabolic network

Acknowledgements

HKBU

• Prof. Terry Hwa

UCSD

• Prof. Hao Li

UCSF

Tony Hui Shenghua Liang Wang Chao Chunhui Cai

Supported by The RGC of the HKSAR Yang Zhu

Thank You!

谢谢