The Systems Biology of metabolism:Computational challenges

The Manchester Centre for Integrative Systems Biology

Vangelis Simeonidis

Universität für Bodenkultur Wien10 April 2023

Systems Biology

• Molecular biology, genetics and bioinformatics have made great advances

• Systems biology studies the interactions of the system, not just the components

• The interactions are what give the system its immerging properties; the sum is greater than its parts

Goals

• Assemble a comprehensive set of strategies and methodologies for Systems Biology, in this case specifically metabolism

• Test and validate strategies and methodologies in the baker’s yeast S. Cerevisiae

• To implement the methodologies in various other systems in local, national and international collaborations

Workflow

Keeping data alive

Experimental Workflow

PROTEIN PURIFICATION

ENZYME KINETIC ASSAYS

EFFECTOR MAP / ENZYME REGULATION

ASSAYS

CONTINUOUS STEADY-STATE CULTURES

(TURBIDOSTAT)

ABSOLUTE PROTEIN

QUANTITATION

ABSOLUTE METABOLITE

QUANTITATION

EXPERIMENT DESIGN (FBA, GROWTH RATE, MEDIA COMPONENTS, MODEL VALIDATION)

PRIDE

MEMO

TEXT MINING

SABIO-RK

Cell growth Protein purification Enzyme kinetics

Quantitativeproteomics

Quantitativemetabolomics

SBML model

Parameters(KM, Kcat)

Variables(metabolite and protein concentrations)

Overview

Challenge:

Reliable reaction list

Consensus yeast model

Nature Biotechnology 26, 1155 - 1160 (2008)

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Markus J Herrgård, Neil Swainston et al.

• SBML – widely supported, many tools• Use MIRIAM standards

http://www.ebi.ac.uk/miriam/

- Unambiguous, unique identifiers with CV terms and external database identifies (Uniprot, CHEBI)

Consensus yeast model

http://www.comp-sys-bio.org/yeastnet/

iIN800(Nielsen)

Yeast 1.0

Yeast 3.0

Change

Reactions 1557 1477 2216 +50%

Metabolites 814 820 930 +13%

Enzymes 708 832 938 +13%

Compartments 5 15 16 +7%

The main representation for biological models is SBML

Challenge:

Which enzymes?

General strategy

Brute-force approach:• Study all enzymes • Create complete map

But:• Gaps in the network

• Might end up with >90% of enzymes, but <10% of flux

General strategy

Flux-centric approach:• Identify where the

carbon flux goes• Prioritize pathways by

ranking higher ones that carry the most flux

• Will end up with 20-30% of enzymes, but >90% of flux

A

E

B

C

D

G

H

I

K

L MN

O

F

L1 ≤ v1 ≤ U1

L2 ≤ v2 ≤ U2

…..............Ln ≤ vn ≤ Un

max M

S . v = 0

Chasing the flux: Flux Balance Analysis

1

1

1

1

1

1

1

1

X

Y

1

1

1

How does FBA work?A

E

B

C

D

G

H

I

K

L M

1

1

1

1

1

X

Y

1

1

1

How does FBA work?A

E

B

C

D

L N

1

XX

YY

ZZ WW

UU

Genome scale network

Experiment 1 2 3 AVE C% of input flux for FBA

Carbon input flux as glucose (mmoles/hr/g DW) 67.5 42.1 74.1 61.2 100.00 1.0000

qBiomass - C4H7O2N (mmoles carbon/hr/g DW) 10.3 9.4 8.7 9.5 15.46 0.0234

qCO2 (offgas) (mmoles carbon/hr/g DW) 16.0 12.3 20.9 16.4 26.78 1.6070

qEthanol (exometabolome, mmoles carbon/hr/g DW) 29.5 16.9 34.8 27.1 44.20 1.3261

qAcetate (exometabolome, mmoles carbon/hr/g DW) 0.5 0.4 0.5 0.5 0.76 0.0229

qAcetaldehyde (exometabol., mmoles carbon/hr/g DW) 0.2 0.1 0.2 0.2 0.27 0.0082

qGlycerol (exometabolome, mmoles carbon/hr/g DW) 5.9 6.2 8.1 6.7 11.00 0.2199

qTrehalose (exometabolome, mmoles carbon/hr/g DW) 0.2 0.2 0.2 0.2 0.33 0.0016

Exometabolome measurements

Results biomass production 15.55% D-Glucose exchange 100.00% Glucose-6-phosphate isomerase 100.00% Glucokinase 100.00% glucose-6-phosphate isomerase 95.67% fructose bisphosphate aldolase 93.09% phosphofructokinase 93.09% glyceraldehyde-3-phosphate dehydrogenase 82.32% phosphoglycerate kinase 82.32% enolase 71.93% phosphoglycerate mutase 71.93% pyruvate kinase 71.30% pyruvate decarboxylase 67.39% alcohol dehydrogenase 41.99% Ethanol exchange 41.99% triose phosphate isomerase 36.07% CO2 exchange 32.16% glycine cleavage complex lipoamide 30.70%  .........................................................  ............  .........................................................  ............

GlycolysisPyruvate metabolism

Glycine metabolismSerine metabolismAlanine metabolism

Aspartate metabolism

Pentose phosphate

TCA cycleFructose

Manose

Oxidative Phosphorylation

Fatty acid biosynthesisTrehalose cycle

Purine biosynthesis

Phosphorylation

biosynthesis

Elementary flux mode analysis

the smallest sub-networks that allow a metabolic reconstruction network to function (in steady state)

Pathways from EFM results

What pathways? GLC

DHAP

G6P

F6P

FDP

G3P

13PG

3PG

2PG

PYR

PEP

ACALDCO2

ETOH

AKG

3PHP

PSEP

GLU SER

GLY

CO2

GLYC3P

GLYC

OAA

ASP

G1P

UDPG

13BDGLCN

AC

MAN6P

MAN1P

GDPMANN

DOLMANP MANNAN

14GLUN

GLYCOGEN

Challenge:

Modelling system behaviour

Flux Balance Analysis (FBA)

easy to solve

only stoichiometry required

no insight into substrate concentrations

Stoichiometric Matrix: signifies if and how a metabolite takes part in a certain reaction

AB…G

r1 r2 …. rn

a1

b1

….g1

a2

b2

….g2

….….….….

an

bn

….gn

Flux Vector: Each component represents the flux through the corresponding reaction

v1

v2

….vn

v

dA/dtdB/dt

….dG/dt

=

Steady State condition

00….0

=

Rn space Rm SpaceKer(S)

null vector 0

L1 ≤ v1 ≤ U1

L2 ≤ v2 ≤ U2

…..............Ln ≤ vn ≤ Un

Kinetic modelling

Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000)

aims to characterize fully the mechanics of each enzymatic reaction

( ) GLK GLTind GLC tv v

dt

6 ( )2

..............................................................

GLK GLYCOGEN PGI TREHALOSEd G P tv v v v

dt

226 16

26 16

6 62 2

26 16 ( )0

6

6 ( )6 ( )6 ( ) 1

1 1 1PFK PFK PFK AK PFKF bP F bP AMP ATP

PFK PFK PFKF bP F bP AMP

PFK RR PFK PFK PFK PFK

F P ATP ATP F PPFK

C F bP C F P t C Keq Ci

K K KPFK PFKATP F P

g F P tF P tg Vm F P t

Km Km Km Kmv

LKm Km

2

26 16 6 6

2 2

2 22 26 ( )16 ( ) 6 ( )26

1

1 1 1 1

PFKATP

PFK PFKATP ATP

AKR

PFK PFK PFK PFK PFK PFK PFK PFKF bP F bP AMP ATP F P ATP ATP F P

C

Ki Km

g F P tF P t Keq F P tF bPK K K Ki K Km Km Km

2 2 2( ) 4 ( ) 2 ( ) 8 ( ) ( ) 4 ( )

2 8

AK AK AKAXP AXP AXP AXP

AK

P t Keq P t P t Keq P t P t Keq P t

Keq

2 2 22 ( ) 8 ( ) ( ) 4 ( )

1 4

AK AKAXP AXP AXP AXP

AK

P t Keq P t P t Keq P t

Keq

Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000)

aims to characterize fully the mechanics of each enzymatic reaction

full detail

costly; time-consuming

unknown mechanics

Kinetic modelling

linlog kinetics

0 01 lnx

v x

v x

v: reaction rate

x: internal metabolite concentration

εx: elasticity

v0, x0: reference state

good approximation of MM kinetics

Goodness of fitin most cases linlog is very good approximation

even when not so good, the approximation remains valid for at least a region around the reference point

linlog:Teusink: o

How to estimate without experimental data?

00

1 lnx

xv v

x

FBA solution Stoichiometric considerations

Metabolite concentrations with changes in ethanol

linlog (with correct elasticities):

Teusink: o

linlog (with estimated elasticities):

• Good fit in most cases

• Can easily incorporate experimental information to improve the fit

Scaling up to genome-scaleReactions exerting most control over biomass production

Reaction CJ

glucosamine-6-phosphate deaminase 0.532

glutamine-fructose-6-phosphate transaminase 0.441

glutamine synthetase 0.358

H2O transport via diffusion 0.212

inorganic diphosphatase -0.193

glycerol-3-phosphate dehydrogenase (NAD) 0.189

L-asparaginase -0.146

adenylate kinase (GTP) -0.142

glucose transport (uniport) -0.132

ribonucleoside-triphosphate reductase (UTP) -0.104

Challenge:

Constraint-based, genome-scale modelling

Constraint-based modelling

Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000)

based on stoichiometry and steady-state assumption

( ) GLK GLTind GLC tv v

dt

6 ( )2

..............................................................

GLK GLYCOGEN PGI TREHALOSEd G P tv v v v

dt

maximise

Flux Balance Analysis (FBA)

Stoichiometric Matrix: signifies if and how a metabolite takes part in a certain reaction

AB…G

r1 r2 …. rn

a1

b1

….g1

a2

b2

….g2

….….….….

an

bn

….gn

Flux Vector: Each component represents the flux through the corresponding reaction

v1

v2

….vn

v

dA/dtdB/dt

….dG/dt

=

Steady State condition

00….0

=L1 ≤ v1 ≤ U1

L2 ≤ v2 ≤ U2

…..............Ln ≤ vn ≤ Un

S . v = 0

Some of the problems with FBA

no substrate concentrations

not always realistic

solution degeneracy

FBA and metabolite concentrations

linlog (with correct elasticities):

Teusink: o

linlog (with estimated elasticities):

• Good fit in most cases

• Can easily incorporate experimental information to improve the fit

In general an FBA problem can have more than one optimal solution.

FBA and solution degeneracy

FBA and unrealistic solutions

Computational hypotheses

1. Test different conditions

2. Test different evolutionary pressures

3. Test the effect of unknown “costs”

Hypoxic conditions

• Under anaerobic conditions, yeast ferments

• We tested if fermentation is also a response to relative O2 limitation

• O2 transport was given an upper bound and FBA solved for increasing uptakes of glucose

Hypoxic conditions

• NOT a switch from respiration to fermentation

• fermentation activated on top of respiration to compensate for increase in glucose

Resource preservation

• Minimisation of the number of active reactions

• Resource preservation as the objective of optimisation, instead of optimal growth

• A value for biomass production was chosen and fixed, then a new formulation solved

Resource preservation

• For low levels of glucose uptake respiration was chosen

• For higher levels of uptake the model switched to fermentation to conserve resources

Energy (ATP) cost

• More respiration requires the synthesis of more mitochondria and/or a number of enzymes involved in the TCA cycle, respiratory chain, ATP synthesis

• An energy cost related to mitochondria synthesis is added to the FBA formulation

Energy (ATP) cost

• Sensitivity analysis revealed the existence of a bifurcation point

• Above this there was a sharp switch to fermentation

One last challenge:

Getting the message across

Systems Biology

Conclusions I outlined some computational challenges in the study of metabolism

Solving such problems improves our biological understanding, and gives us the systematic tools necessary

Results get us closer and closer to simulating experimental observations

Such crucial improvements are a necessary stepping stone for the creation of realistic genome-scale models

We can guide experimental design to verify predictions

Acknowledgments The MCISB team

financial support from BBSRC/EPSRC via “The Manchester Centre for Integrative Systems Biology” grant (BB/C008219/1)