BIOINFORMATICS ON NETWORKS

Nick Sahinidis

University of Illinois atUrbana-Champaign

Chemical and Biomolecular Engineering

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)

• Introduction to:– Metabolic networks

– Flux balance analysis

– S-systems theory

– Gene additions and deletions

– Pathway reconstruction from data

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

• 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

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

• 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

A B C E

D

v1

v7

v6

v4v3

v5

v2

Network Boundary

v3: B Dv2: B C

v4: D B

v1: A B

v5: C Dv6: C Ev7: 2D E

Internal Fluxes

b4: Eb3: D

b1: Ab2: C

Exchange Fluxes

b1

b2

b4

b3

Exchange fluxes may be positive (system output) orNegative (input to metabolic network)

FBA EQUATIONS

A B C E

D

v1

v7

v6

v4v3

v5

v2

Network Boundary

b1

b2

b4

b3Sign restrictions

0 v1,…,v7

b1 0 - b2 + - b3 +

b4 0

Steady state mass balances

A: - v1 - b1 = 0B: v1 + v4 – v2 – v3 = 0C: v2 - v5 - v6 - b2 = 0D: v3 + v5 - v4 - 2v7 - b3 = 0E: v6 + v7 - b4 = 0

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

• 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

• 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