Systems biology reconstruction and modeling large biological networks
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Systems biology / Reconstruction and modeling large biological networks. Richard Notebaart. Seminar. What is systems biology? How to reconstruct large biological networks/systems Methods to analyze large biological networks/systems

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Systems biology reconstruction and modeling large biological networks l.jpg
Systems biology / Reconstruction and modeling large biological networks

Richard Notebaart


Seminar l.jpg
Seminar biological networks

  • What is systems biology?

  • How to reconstruct large biological networks/systems

  • Methods to analyze large biological networks/systems

  • Applying systems biology approaches to answer biological questions


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  • What is systems biology: biological networks

    • fashionable catchword?

    • a real new (philosophical) concept?

    • new discipline in biology?

    • just biology?

    • ...


Systems concept l.jpg
Systems concept biological networks

  • 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

  • The number of interconnections within a system is larger than the number of connections with the environment

  • Systems can include other systems as part of their construction

    • concept of modularity!

      • allows complex systems to be put together from known simple ones (system of systems)

      • concept of modularity!


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Systems levels biological networks

Ecosystem

Multicellular organisms

Organs

Tissues

Cells

Pathways

Proteins/genes


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Systems theory biological networks

  • The behavior of a system depends on:

    • (Properties of the) components of the system

    • The interactions between the components

    • THUS:

    • You cannot understand a system via pure reductionism (studying the components in isolation)


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Systems biology biological networks

  • New? NO and YES

    • Systems theory and theoretical biology are old

    • Experimental and computational possibilities are new



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Omics-revolution shifts paradigm to large systems biological networks

- Integrative bioinformatics - (Network) modeling


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Reconstruction of networks from ~ biological networksomics for systems analysis

  • Gene expression networks: based on micro-array data and clustering of genes with similar expression values over different conditions (i.e. correlations).

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

  • Metabolic networks: network of interacting metabolites through biochemical reactions.


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genome biological networks

transcriptome

proteome

metabolome

How to reconstruct metabolic networks?

  • Genome annotation allows for reconstruction:

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

Genome-scale network




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From network to model (formalized knowledge)

The Modeling Ideal - A complete kinetic description

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

  • Can model fluxes and concentrations over time

  • 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 (formalized knowledge)

For genome-scale networks there is no detailed kinetic description -> too many reactions involved!

B

A

C

  • Theory

    • Complete knowledge

    • Solution is a single point

  • Genome-scale

    • Incomplete knowledge

    • Solution is a space

Flux B

Flux B

Flux A

Flux A

Flux C

Flux C


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Genome-scale modeling (formalized knowledge)

  • How to model genome-scale networks?

    • We need:

      • A metabolic reaction network

      • Exchange reactions: link between environment and reaction network (systems boundary)

      • Constraints that limit network function:

        • Mass balancing (conservation) of metabolites in the systems

        • Exchange fluxes with environment

        • ……

    • Goal: prediction of growth and reaction fluxes


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From network to constraint-based model (formalized knowledge)

Mass balancing

  • 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


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Constraint-based modeling - Data structure (formalized knowledge)

  • 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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  • R (formalized knowledge)esult is a system of m equations (number of metabolites) and n unknowns (fluxes)

S = Stoichiometric matrix (m x n)

v = Metabolic reaction fluxes (n)

Matrix notation: S.v = 0

Principles of Constraint-Based Analysis

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

Flux balance on component Xi:

V2

V1

Xi

V1 = V2 + V3  V1 - V2 - V3 = 0

V3

Normally, n>m so the system is underdetermined

  • No unique solution!


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What is underdetermined? (formalized knowledge)

  • Determined System (2 equations, 2 unknowns):

    • X+Y=2

    • 2X-Y=1

    • Solution X=1, Y=1

  • Underdetermined System (1 equation, 2 unknowns) X+Y=2

    • Infinite Solutions!

  • In metabolism  more fluxes (unknowns) than metabolites (equations)


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Constraints (formalized knowledge)

Constraints

(i)

Stoichiometry(mass conservation)

(ii) Exchange fluxes (capacity)

(iii) …

Impose constraints

B

A

C

Exchange reactions allow nutrients to be taken up from environment with a certain maximum flux, e.g. -2≤vexchange≤0


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Interpretation of the convex cone (formalized knowledge)

B

A

C

Convex cone, Flux cone, Solution space

C

One allowable functional state (flux distribution) of network given constraints

B

A


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Flux balance analysis (FBA) (formalized knowledge)

C

Constraints set bounds on solution space, but where in this space does the “real” solution lie?

B

A

FBA: optimize for that flux distribution that maximizes an objective function (e.g. biomass flux) – subject to S.v=0 and αj≤vj≤βj

Thus, it is assumed that organisms are evolved for maximal growth -> efficiency!



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Prediction of growth fails with flux balance analysis (in (in L. plantarum)

Teusink B. et al., 2006, J. Bio. Chem.

glucose

pyruvate

2 ATP/Glc

2.5 ATP/Glc

lactate

acetate + formate + ethanol

FBA predicts mixed acid fermentation with 40% too high biomass formation -> thus L. plantarum is not efficient!


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Some other constraint-based methods (in

Robustness analysis: examining the effect of changing the flux through a reaction on the objective function (i.e. growth)


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Some other constraint-based methods (in

Flux variability analysis: compute minimum and maximum flux values through each reaction without changing the optimal solution (i.e. maximum growth / phenotype)

FBA is performed to determine the optimal solution and is used as constraint.

Example of application: if one wants to change the optimal solution it is relevant to know which reactions have wide and narrow flux ranges


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Available software – COBRA toolbox (in

Designed for matlab and freely available!


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Flux coupling / correlations (in

  • 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 for v2, 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 (in

A and B: directionally

B and C: fully

C and D: uncoupled


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Measured Vs. (in In silico flux correlations

Emmerling M. et al. J Bacteriol. 2002

Segre D. et al. PNAS, 2002

(p < 10-14)

In silico and measured flux correlations are in agreement

Notebaart RA. et al. (2007), PLoS Comput Biol (in press)


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Flux coupling for data analysis (in

  • Does flux coupling relate to transcriptional co-regulation of genes?

Notebaart RA. et al. (2007), PLoS Comput Biol (in press)


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Flux coupling for data analysis (in

Pal C. et al. (2005), Nature Genetics

Flux coupled genes in the E. coli metabolism are more likely lost or gained together over evolution

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


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Gene dispensability in metabolism of yeast (in

  • Studies have shown that many metabolic genes are dispensable (80% of yeast genes appear not to be essential for growth)

    • Main question: why are most genes dispensable?

    • ‘Forces’ that explain dispensability:

      • The impact of gene deletionsmay depend on the environment (plasticity)

      • The presence ofmutational robustness (compensatory mechanisms)  alternative pathways

      • Or both…

    • Objective: explore the interaction between the two forces.

Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA


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Gene dispensability in metabolism (in

  • A ’model’ of mutational robustness and environment:

    • Simulate metabolism in different environments and

    • identify genes in alternative pathways by synthetic lethality

Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA


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Gene dispensability – (in single gene deletion

Gene is essential when a deletion is lethal (i.e. no growth):

Delete the gene and apply FBA  optimization equals zero  gene is essential!

Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA


Effect of environment and alternative pathways l.jpg
Effect of environment and alternative pathways (in

BUT, single gene deletion does not supply direct information on alternative pathways and its role in gene dispensability 

Method: Identify synthetic lethality between gene A and B:

i) Delete only gene A and apply FBA  optimization unequal to zero  gene is not essential

ii) Delete only gene B and apply FBA  optimization unequal to zero  gene is not essential

iii) Delete both gene A and B and apply FBA  optimization equals zero  either A or B must be present  thus alternative pathway which explains gene dispensability!

Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA


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Effect of environment and alternative pathways (in

Alternative paths in all environments: 14.3%

Alternative paths (SL) in 1 or 2 environments: 50%

50% of genes in alternative pathways provide mutational robustness in only 1 or 2 environments  thus the environment plays an important role in gene dispensability!

Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA


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Summary / conclusions (in

  • Systems biology: studying living cells/tissues/etc by exploring their components and their interactions

  • Even without detailed knowledge of kinetics, genome-scale modeling is still possible

  • Genome-scale modeling has shown to be relevant in studying evolution and to interpret ~omics data

  • Major challenge is to integrate knowledge of kinetics and genome-scale networks


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Assignment (in

  • Read the following article: Pal C., Papp B., Lercher MJ., Csermely P., Oliver SG. and Hurst LD. (2006), Chance and necessity in the evolution of minimal metabolic networks, Nature

  • Write a report of 2 / 3 pages and include/consider at least the following points:

    • What is the main hypothesis and scientific question?

    • What do you think about the hypothesis? Will it have important implications?

    • Do the authors ask other scientific (sub)questions (related to the main question) and if so, what are they and was it necessary to address them?

    • What methods have been used and explain them (in your own words!).

    • What are the major findings/results?

    • Summarize the conclusions and describe if you agree with it based on the described results.


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