Systems biology Reconstruction and modeling large biological networks

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Systems biology / Reconstruction and modeling
large biological networks
Richard Notebaart
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Seminar

• 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
• fashionable catchword?
• a real new (philosophical) concept?
• new discipline in biology?
• just biology?
• ...

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Systems concept

• 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
Ecosystem
Multicellular organisms
Organs
Tissues
Cells
Pathways
Proteins/genes
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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
• THUS
• You cannot understand a system via pure
  reductionism (studying the components in
  isolation)

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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 to large systems
- Integrative bioinformatics - (Network)
modeling
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Reconstruction of networks from omics 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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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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Reconstructed genome-scale networks
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Data visualization via Gene-Protein-Reaction
relations (formalized knowledge)
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From network to model
The Modeling Ideal - A complete kinetic
description

• FluxRxn1 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
For genome-scale networks there is no detailed
kinetic description -gt 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

• 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
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

• 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

Flux balance on component Xi
V2
V1
Xi
V1 V2 V3 ? V1 - V2 - V3 0
V3

• Normally, ngtm so the system is underdetermined
• No unique solution!

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What is underdetermined?

• Determined System (2 equations, 2 unknowns)
• XY2
• 2X-Y1
• Solution X1, Y1
• Underdetermined System (1 equation, 2 unknowns)
  
  XY2
• Infinite Solutions!
• In metabolism ? more fluxes (unknowns) than
  metabolites (equations)

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Impose constraints
B
A
C
Exchange reactions allow nutrients to be taken up
from environment with a certain maximum flux,
e.g. -2vexchange0
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Interpretation of the convex cone
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)
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.v0 and ajvjßj Thus, it
is assumed that organisms are evolved for maximal
growth -gt efficiency!
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Prediction of microbial evolution by flux balance
analysis (in E. coli)
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Prediction of growth fails with flux balance
analysis (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 -gt thus L. plantarum is
not efficient!
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Some other constraint-based methods
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
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
Designed for matlab and freely available!
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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 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
A and B directionally B and C fully C and D
uncoupled
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Measured Vs. In silico flux correlations
Emmerling M. et al. J Bacteriol. 2002 Segre D.
et al. PNAS, 2002
(p lt 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

• 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
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

• 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 deletions may depend on the
  environment (plasticity)
• The presence of mutational 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

• 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 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
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Effect of environment and alternative pathways
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
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

• 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

• 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.