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 – – –

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 ABCE 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 ABCE D v1v1 v7v7 v6v6 v4v4 v3v3 v5v5 v2v2 Network Boundary v 3 : B D v 2 : B C v 4 : D B v 1 : A B v 5 : C D v 6 : C E v 7 : 2D E Internal Fluxes b 4 : E b 3 : D b 1 : A b 2 : C Exchange Fluxes b1b1 b2b2 b4b4 b3b3 Exchange fluxes may be positive (system output) or Negative (input to metabolic network)

FBA EQUATIONS ABCE D v1v1 v7v7 v6v6 v4v4 v3v3 v5v5 v2v2 Network Boundary b1b1 b2b2 b4b4 b3b3 Sign restrictions 0  v 1,…,v 7 b 1  0 -   b 2  +  -   b 3  +  b 4  0 Steady state mass balances A: - v 1 - b 1 = 0 B: v 1 + v 4 – v 2 – v 3 = 0 C: v 2 - v 5 - v 6 - b 2 = 0 D: v 3 + v 5 - v 4 - 2v 7 - b 3 = 0 E: v 6 + v 7 - b 4 = 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 – E. Voit, Computational Analysis of Biochemical Systems, Cambridge University Press, N. Friedman, Inferring cellular networks using probabilistic graphical models, Science, 303, , Metabolic Systems Engineering course: –