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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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Bioinformatics on networks

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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