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University of Illinois at Urbana-Champaign. BIOINFORMATICS ON NETWORKS. Nick Sahinidis. Chemical and Biomolecular Engineering. MOTIVATION. Genomics and proteomics help us understand the structure, properties, and function of single genes and proteins

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University of Illinois atUrbana-Champaign

BIOINFORMATICS

ON NETWORKS

Nick Sahinidis

Chemical and Biomolecular Engineering

motivation
MOTIVATION
  • Genomics and proteomics help us understand the structure, properties, and function of single genes and proteins
  • Genes and proteins function in complex networks
  • Bioinformatics on biochemical networks aims to understand and rationally manipulate networks of genes and proteins
  • These networks are very complex
    • http://www.expasy.org/cgi-bin/show_thumbnails.pl
    • http://www.expasy.org/cgi-bin/show_thumbnails.pl?2
    • http://www.genome.ad.jp/kegg/pathway.html
learning objectives two lectures
LEARNING OBJECTIVES (two lectures)
  • Introduction to:
    • Metabolic networks
    • Flux balance analysis
    • S-systems theory
    • Gene additions and deletions
    • Pathway reconstruction from data
metabolic networks
METABOLIC NETWORKS
  • Definitions
    • Metabolic network: a system of interacting proteins and small molecules converting raw materials to energy and other useful substances in a living organism
    • Metabolites: materials consumed or produced in a metabolic network
    • Enzymes: proteins that catalyze reactions
    • The sets of metabolites and enzymes of a network are not necessarily disjoint
  • Key observation
    • A large proportion of the chemical processes that underlie life are shared across a very wide range of organisms
graphical representation
GRAPHICAL REPRESENTATION
  • Nodes represent metabolites and enzymes
  • Arcs correspond to reactions and modulation
  • Dotted or colored lines often reserved to denote modulation
  • A negative sign associated with an arc is used to denote inhibition
metabolic network example
METABOLIC NETWORK EXAMPLE

A

B

C

E

D

  • Five metabolites (A, B, C, D, E)
  • Six reactions (one reversible and five irreversible)
  • Network interacts with environment through:
    • Consumption of A
    • Secretion of E
    • Consumption or secretion of C and D
flux balance analysis
FLUX BALANCE ANALYSIS
  • Pseudo steady-state hypothesis: metabolic dynamics are much faster compared to those of the environment
  • Model network through steady-state mass balances for metabolites
  • For each metabolite, its rate of consumption must equal its rate of production
fba example

Internal Fluxes

v1: A B

v2: B C

b2

v3: B D

v4: D B

v2

v1

v6

v5: C D

b1

b4

v4

v5

v6: C E

v3

v7

v7: 2D E

Exchange Fluxes

Network Boundary

b1: A

b3

b2: C

b3: D

b4: E

FBA EXAMPLE

A

B

C

E

D

Exchange fluxes may be positive (system output) or

Negative (input to metabolic network)

fba equations

b2

v2

v1

v6

b1

b4

v4

v5

v3

Steady state mass balances

v7

A: - v1 - b1 = 0

B: v1 + v4 – v2 – v3 = 0

Network Boundary

b3

C: v2 - v5 - v6 - b2 = 0

D: v3 + v5 - v4 - 2v7 - b3 = 0

E: v6 + v7 - b4 = 0

FBA EQUATIONS

A

B

C

E

D

Sign restrictions

0  v1,…,v7

b1  0

-  b2  +

-  b3  +

b4  0

modeling with fba
MODELING WITH FBA
  • Problem #1: Interpret metabolic network behavior
    • Hypothesis: Network is an optimizer
    • Likely objectives:
      • Maximize growth
      • Minimize energy consumption
    • Leads to a linear program
  • Problem #2: Manipulate a metabolic network to produce certain desired products through
    • Control of external fluxes
    • Structural manipulations in the network
gene additions and deletions
GENE ADDITIONS AND DELETIONS
  • Two-level problem
    • Upper level: maximize a bioengineering objective through gene knockouts
    • Lower level: cell is still an optimizer that seeks to optimize its own objective through adjusting internal fluxes
  • Use binary variable for each gene to decide whether to knock it out or not (or whether to over-express)
  • Inner linear program can be converted to a set of linear equalities and inequalities via duality theory giving rise to a mixed-integer linear program for the overall problem
references and further reading
REFERENCES AND FURTHER READING
  • B. Palsson, 2000 Hougen Lectures
    • http://gcrg.ucsd.edu/presentations/hougen/hougen.htm
  • E. Voit, Computational Analysis of Biochemical Systems, Cambridge University Press, 2000.
  • N. Friedman, Inferring cellular networks using probabilistic graphical models, Science, 303, 799-805, 2004.
  • Metabolic Systems Engineering course:
    • http://archimedes.scs.uiuc.edu/courses/meteng.html
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