Comparative Genome Analysis - day 9Richard.ppt

8/13/2019 Comparative Genome Analysis - day 9Richard.ppt

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Systems theory

The behavior of a system depends on:

(Properties of the) components of the system

The interactions between the components

A system represents a set of components together with the relations

connecting them to form a unity

Defining a system divides reality into the system itself and its

environment


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Systems biology

New? NO and YES

Systems theory and theoretical biology are old

Experimental and computational possibilities are

new


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(publications of von Bartalanffy, 1933-1970)


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Omics-revolution shifts paradigm tolarge systems

- Integrative bioinformatics

- (Network) modeling


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Gene expression networks: based on micro-array data andclustering of genes with similar expression values overdifferent conditions (i.e. correlations).

Protein-protein interaction networks: based on yeast-two-hybrid approaches.

Metabolic networks: network of interacting metabolites

through biochemical reactions.

Reconstruction of networks from ~omicsfor systems analysis


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Genome annotation allows for reconstruction:

If an annotated gene codes for an enzyme it can (in mostcases) be associated to a reaction

How to reconstruct metabolic networks?

genome

transcriptome

proteome

metabolome

Genome-scale

network


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Reconstructed genome-scale networks

Species #Reactions #Genes Reference

Escherichia coli 2077 1260 Feist AM. et al. (2007),Mol. Syst. Biol.

Saccharomyces cerevisiae 1175 708 Frster J. et al. (2003),Genome Res.

Bacillus subtilis 1020 844 Oh YK. et al. (2007), J.Biol. Chem.

Lactobacillus plantarum 643 721 Teusink B. et al., (2006),J. Bio. Chem.

Human 3673 1865 Duarte NC. et al., (2007),PNAS

>30


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Data visualization via Gene-Protein-Reactionrelations (formalized knowledge)


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From network to model

The Modeling Ideal - A complete kinetic description

Flux*Rxn1= f(pH, temp, concentration,regulators,)

Can model fluxes and concentrations overtime

Drawbacks Lots of parameters

Measured in vitro(valid in vivo?)

Can be complex, nasty equations

Nearly impossible to get all parameters at genome-scale

*measure of turnover rate of substrates through a reaction (mmol.h-1.gDW-1)


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Theory vs. Genome-scale modeling

Theory

Complete knowledge

Solution is a single point

Genome-scale

Incomplete knowledge

Solution is a space

Flux A

Flux C

Flux B

Flux A

Flux C

Flux B

AB

C

For genome-scale networks there is no detailed kinetic

description -> too many reactions involved!


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Genome-scale modeling

How to model genome-scale networks?

We need: A metabolic reaction network

Exchange reactions: link between environment andreaction network (systems boundary)

Constraints that limit network function:

Mass balancing (conservation) of metabolites inthe systems

Exchange fluxes with environment

Goal:prediction of growth and reaction fluxes


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From network to constraint-based model

A system represents a set of components together with the relations connecting

them to form a whole unity

Defining a system divides reality into the system itself and its environment

Mass balancing


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Constraint-based modeling - Data structure

Stoichiometric matrix S (Mass balancing):

1: metabolite produced in reaction

-1: metabolite consumed by reaction

0: metabolite not involved in reaction


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Principles of Constraint-Based Analysis

Steady-state assumption: for each metabolite in network, write a

balance equation

XiV1 V2

V3

Flux balance on component Xi:

V1 = V2 + V3 V1 - V2 - V3 = 0

Matrix notation: S.v = 0 S = Stoichiometric matrix (m x n)v = Metabolic reaction fluxes (n)

Result is a system of m equations (number of metabolites) and n

unknowns (reaction fluxes)

Normally, n>m so the system is underdetermined

No unique solution!


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Impose constraints

Flux

C

Flux B

Flux

C

Flux B

BoundedSolution space

UnboundedSolution space

Constraints:(i) Stoichiometry (mass

conservation)(ii) Exchange fluxes (capacity)(iii)

AB

C

Exchange reactions allow nutrients to be taken up from the environmentwith a certain maximum flux


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Interpretation of solution space

Solution space, Convex cone, Flux cone

One allowable functional state (flux

distribution) of network given

constraints

AB

C

AB

C


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Flux balance analysis (FBA)

A

B

C Constraints set bounds on solution space,

but where in this space does the real

solution lie?

FBA: optimize for that flux distribution thatmaximizes an objective function (e.g. biomass

flux) subject to S.v=0 and jvjj

Thus, it is assumed that organisms are evolved

for maximal growth -> efficiency!


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Prediction of microbial evolution by fluxbalance analysis (in E. coli)


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Flux coupling / correlations

Genome-scale analysis to determine whether two fluxes (v1 and

v2) are:

Fully coupled: a non-zero flux of v1 implies a non-zero fixed flux for

v2 (and vice versa)

Directionally coupled: a non-zero flux v1 implies a non-zero flux forv2, but not necessarily the reverse

Uncoupled: a non-zero flux v1 does not imply a non-zero flux for v2

(and vice versa)


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Flux coupling / correlations

A and B: directionally

B and C: fullyC and D: uncoupled

Flux coupling: maximize and minimize the flux through one

reaction and constrain the other by a finite value (e.g. 1)


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Measured Vs. In silicoflux correlations

Emmerling M. et al. J Bacteriol. 2002Segre D. et al.PNAS, 2002

In silicoand measured flux correlations are in agreement

Notebaart RA. et al. (2008), PLoS Comput Biol


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Flux coupling for data analysis

Does flux coupling relate to transcriptional co-regulation ofgenes?


Intra-operonic

Inter-operonic


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Does flux coupling relate to transcriptional co-regulation ofgenes?


TF similarity = number of shared TFs / total involved TFs


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Flux coupled genes in the E. coli metabolism are more likely lost or

gained together over evolution

Coupling type Event #Events OR*(95% c.i.)

Fully coupled Transfer 5964.6 (24.2

168.8)

Fully coupled Loss 1,624 50.0 (41.859.6)

Directionally

coupled Transfer 78

60.3 (24.3

147.2)

Directionally

coupledLoss 2,833 9.6 (8.311.1)

*odd ratio (OR): how much more likely is an event X relative to event Y

Pal C. and Papp B. et al. (2005), Nature Genetics


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Summary / conclusions

Systems biology: studying living cells/tissues/etc byexploring their components and their interactions

Even without detailed knowledge of kinetics, genome-scalemodeling is still possible

Genome-scale modeling has shown to be relevant in studyingevolution and to interpret ~omics data

Major challenge is to integrate knowledge of kinetics andgenome-scale networks

Comparative Genome Analysis - day 9Richard.ppt

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