Systems biology / Reconstruction and modeling large biological networks

1. Systems biology / Reconstruction and modeling large biological networks Richard Notebaart

2. Seminar • What is systems biology? • How to reconstruct large biological networks/systems • Methods to analyze large biological networks/systems • Applying systems biology approaches to answer biological questions

3. What is systems biology: • fashionable catchword? • a real new (philosophical) concept? • new discipline in biology? • just biology? • ...

4. Systems concept • A system represents a set of components together with the relations connecting them to form a unity • Defining a system divides reality into the system itself and its environment • The number of interconnections within a system is larger than the number of connections with the environment • Systems can include other systems as part of their construction • concept of modularity! • allows complex systems to be put together from known simple ones (system of systems) • concept of modularity!

5. Systems levels Ecosystem Multicellular organisms Organs Tissues Cells Pathways Proteins/genes

6. Systems theory • The behavior of a system depends on: • (Properties of the) components of the system • The interactions between the components • THUS: • You cannot understand a system via pure reductionism (studying the components in isolation)

7. Systems biology • New? NO and YES • Systems theory and theoretical biology are old • Experimental and computational possibilities are new

8. (publications of von Bartalanffy, 1933-1970)

9. Omics-revolution shifts paradigm to large systems - Integrative bioinformatics - (Network) modeling

10. Reconstruction of networks from ~omics for systems analysis • Gene expression networks: based on micro-array data and clustering of genes with similar expression values over different conditions (i.e. correlations). • Protein-protein interaction networks: based on yeast-two-hybrid approaches. • Metabolic networks: network of interacting metabolites through biochemical reactions.

11. genome transcriptome proteome metabolome How to reconstruct metabolic networks? • Genome annotation allows for reconstruction: • If an annotated gene codes for an enzyme it can (in most cases) be associated to a reaction Genome-scale network

12. Reconstructed genome-scale networks

13. Data visualization via Gene-Protein-Reaction relations (formalized knowledge)

14. From network to model The Modeling Ideal - A complete kinetic description • Flux*Rxn1 = f(pH, temp, concentration, regulators,…) • Can model fluxes and concentrations over time • Drawbacks • Lots of parameters • Measured in vitro (valid in vivo?) • Can be complex, ‘nasty’ equations • Nearly impossible to get all parameters at genome-scale • *measure of turnover rate of substrates through a reaction (mmol.h-1.gDW-1)

15. Theory vs. Genome-scale modeling For genome-scale networks there is no detailed kinetic description -> too many reactions involved! B A C • Theory • Complete knowledge • Solution is a single point • Genome-scale • Incomplete knowledge • Solution is a space Flux B Flux B Flux A Flux A Flux C Flux C

16. Genome-scale modeling • How to model genome-scale networks? • We need: • A metabolic reaction network • Exchange reactions: link between environment and reaction network (systems boundary) • Constraints that limit network function: • Mass balancing (conservation) of metabolites in the systems • Exchange fluxes with environment • …… • Goal: prediction of growth and reaction fluxes

17. From network to constraint-based model Mass balancing • A system represents a set of components together with the relations connecting them to form a whole unity • Defining a system divides reality into the system itself and its environment

18. Constraint-based modeling - Data structure • Stoichiometric matrix S (Mass balancing): 1: metabolite produced in reaction -1: metabolite consumed by reaction 0: metabolite not involved in reaction

19. Result is a system of m equations (number of metabolites) and n unknowns (fluxes) S = Stoichiometric matrix (m x n) v = Metabolic reaction fluxes (n) Matrix notation: S.v = 0 Principles of Constraint-Based Analysis • Steady-state assumption: for each metabolite in network, write a balance equation Flux balance on component Xi: V2 V1 Xi V1 = V2 + V3  V1 - V2 - V3 = 0 V3 Normally, n>m so the system is underdetermined • No unique solution!

20. What is underdetermined? • Determined System (2 equations, 2 unknowns): • X+Y=2 • 2X-Y=1 • Solution X=1, Y=1 • Underdetermined System (1 equation, 2 unknowns) X+Y=2 • Infinite Solutions! • In metabolism  more fluxes (unknowns) than metabolites (equations)

21. Constraints Constraints (i) Stoichiometry(mass conservation) (ii) Exchange fluxes (capacity) (iii) … Impose constraints B A C Exchange reactions allow nutrients to be taken up from environment with a certain maximum flux, e.g. -2≤vexchange≤0

22. Interpretation of the convex cone B A C Convex cone, Flux cone, Solution space C One allowable functional state (flux distribution) of network given constraints B A

23. Flux balance analysis (FBA) C Constraints set bounds on solution space, but where in this space does the “real” solution lie? B A FBA: optimize for that flux distribution that maximizes an objective function (e.g. biomass flux) – subject to S.v=0 and αj≤vj≤βj Thus, it is assumed that organisms are evolved for maximal growth -> efficiency!

24. Prediction of microbial evolution by flux balance analysis (in E. coli)

25. Prediction of growth fails with flux balance analysis (in L. plantarum) Teusink B. et al., 2006, J. Bio. Chem. glucose pyruvate 2 ATP/Glc 2.5 ATP/Glc lactate acetate + formate + ethanol FBA predicts mixed acid fermentation with 40% too high biomass formation -> thus L. plantarum is not efficient!

26. Some other constraint-based methods Robustness analysis: examining the effect of changing the flux through a reaction on the objective function (i.e. growth)

27. Some other constraint-based methods Flux variability analysis: compute minimum and maximum flux values through each reaction without changing the optimal solution (i.e. maximum growth / phenotype) FBA is performed to determine the optimal solution and is used as constraint. Example of application: if one wants to change the optimal solution it is relevant to know which reactions have wide and narrow flux ranges

28. Available software – COBRA toolbox Designed for matlab and freely available!

29. Flux coupling / correlations • Genome-scale analysis to determine whether two fluxes (v1 and v2) are: • Fully coupled: a non-zero flux of v1 implies a non-zero fixed flux for v2 (and vice versa) • Directionally coupled: a non-zero flux v1 implies a non-zero flux for v2, but not necessarily the reverse • Uncoupled: a non-zero flux v1 does not imply a non-zero flux for v2 (and vice versa)

30. Flux coupling / correlations A and B: directionally B and C: fully C and D: uncoupled

31. Measured Vs. In silico flux correlations Emmerling M. et al. J Bacteriol. 2002 Segre D. et al. PNAS, 2002 (p < 10-14) In silico and measured flux correlations are in agreement Notebaart RA. et al. (2007), PLoS Comput Biol (in press)

32. Flux coupling for data analysis • Does flux coupling relate to transcriptional co-regulation of genes? Notebaart RA. et al. (2007), PLoS Comput Biol (in press)

33. Flux coupling for data analysis Pal C. et al. (2005), Nature Genetics Flux coupled genes in the E. coli metabolism are more likely lost or gained together over evolution *odd ratio (OR): how much more likely is an event X relative to event Y

34. Gene dispensability in metabolism of yeast • Studies have shown that many metabolic genes are dispensable (80% of yeast genes appear not to be essential for growth) • Main question: why are most genes dispensable? • ‘Forces’ that explain dispensability: • The impact of gene deletionsmay depend on the environment (plasticity) • The presence ofmutational robustness (compensatory mechanisms)  alternative pathways • Or both… • Objective: explore the interaction between the two forces. Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA

35. Gene dispensability in metabolism • A ’model’ of mutational robustness and environment: • Simulate metabolism in different environments and • identify genes in alternative pathways by synthetic lethality Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA

36. Gene dispensability – single gene deletion Gene is essential when a deletion is lethal (i.e. no growth): Delete the gene and apply FBA  optimization equals zero  gene is essential! Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA

37. Effect of environment and alternative pathways BUT, single gene deletion does not supply direct information on alternative pathways and its role in gene dispensability  Method: Identify synthetic lethality between gene A and B: i) Delete only gene A and apply FBA  optimization unequal to zero  gene is not essential ii) Delete only gene B and apply FBA  optimization unequal to zero  gene is not essential iii) Delete both gene A and B and apply FBA  optimization equals zero  either A or B must be present  thus alternative pathway which explains gene dispensability! Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA

38. Effect of environment and alternative pathways Alternative paths in all environments: 14.3% Alternative paths (SL) in 1 or 2 environments: 50% 50% of genes in alternative pathways provide mutational robustness in only 1 or 2 environments  thus the environment plays an important role in gene dispensability! Harrison R and Papp B. et al. (2007), Proc Natl Acad Sci USA

39. Summary / conclusions • Systems biology: studying living cells/tissues/etc by exploring their components and their interactions • Even without detailed knowledge of kinetics, genome-scale modeling is still possible • Genome-scale modeling has shown to be relevant in studying evolution and to interpret ~omics data • Major challenge is to integrate knowledge of kinetics and genome-scale networks

40. Assignment • Read the following article: Pal C., Papp B., Lercher MJ., Csermely P., Oliver SG. and Hurst LD. (2006), Chance and necessity in the evolution of minimal metabolic networks, Nature • Write a report of 2 / 3 pages and include/consider at least the following points: • What is the main hypothesis and scientific question? • What do you think about the hypothesis? Will it have important implications? • Do the authors ask other scientific (sub)questions (related to the main question) and if so, what are they and was it necessary to address them? • What methods have been used and explain them (in your own words!). • What are the major findings/results? • Summarize the conclusions and describe if you agree with it based on the described results.