Transcript ppt - University of Illinois Urbana

BIOINFORMATICS
ON NETWORKS
Nick Sahinidis
University of Illinois at
Urbana-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
Internal Fluxes
b2
A
v1
B
v2
b1
v3
v6
C
v4
E
b4
v5
v7
v1 : A
v2 : B
v3 : B
v4 : D
v5 : C
v6 : C
v7: 2D
B
C
D
B
D
E
E
D
Exchange Fluxes
Network Boundary
b3
b1:
b2:
b3:
b4:
A
C
D
E
Exchange fluxes may be positive (system output) or
Negative (input to metabolic network)
FBA EQUATIONS
Steady state mass balances
b2
A
v1
B
v2
b1
v3
v6
C
v4
E
b4
v5
v7
D
A:
B:
C:
D:
E:
- v1 - b1 = 0
v1 + v4 – v2 – v3 = 0
v2 - v5 - v6 - b2 = 0
v3 + v5 - v4 - 2v7 - b3 = 0
v6 + v7 - b4 = 0
Network Boundary
b3
Sign restrictions
0  v1,…,v7
b1  0
-  b2  +
-  b3  +
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