bioinformatics on networks nick sahinidis university of illinois at urbana-champaign chemical and...
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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