1. Transcriptional Regulation in Constraints-based metabolic Models of E. coli Published by Markus Covert and Bernhard Palsson, 2002

2. Outline Background – Metabolic Network Modeling – What is FBA model? – Application and Challenge of FBA – How to integrate FBA with regulatory constrains Method Results

3. Metabolic network modeling(MNM)

4. Terminology I Flux: the rate of flow of metabolites along a metabolic pathway. Stoichiometric matrix

5. Terminology II Mass balance Constrains – Invariant constrains Physico-chemical – Variant constrains Environmental constraints Regulatory constrains

6. FBA model

7. Summary of FBA model

8. Resource and tools for FBA

9. Application of FBA model

10. Challenges of FBA Reconstruction problem – Rely fundamentally on the availability of genome sequences and annotations Incomplete annotation – Some path and enzyme may be missing Select objective function

11. rFBA Add regulatory constrains to FBA – Boolean formalism ： AND, OR, NOT – ON and OFF If OFF, flux 0 If ON, the flux is calculated by FBA model Trans= IF (G) AND NOT (B) Rxn= IF (A) AND (E)

12. Metabolic and Regulatory Network Reconstruction The metabolic network was reconstructed by identifying a set of biochemical reactions in the central E. coli metabolism, taken from the annotated genome sequence as well as from biochemical and physiological literature. The regulatory network was derived from the literature data and represented as a set of regulatory rules following established procedures. These rules were based on external conditions and/or internal conditions of the system. Regulatory constraints were described using a Boolean formalism in which gene products are either available (ON) or unavailable (OFF) to the cell.

13. Method Regulatory network: 149 genes  16 regulatory proteins and 73 enzymes  113 reactions

14. Transcriptional Regulation and the Calculation of Steady-state Metabolic Flux Distributions FBA was used to determine an optimal metabolic flux distribution for the given conditions For the purposes of these simulations, capacity constraints included maximum uptake rates of oxygen as well as substrates such as glucose, acetate, and lactose, as determined from growth experiments found in the literature. The production of growth precursors in certain ratios was used here as an approximation. LINDO was used to calculate the optimal flux distributions.

15. Method

16. Changing Environments and Time-dependent Cell Behavior The time constants that describe metabolic transients are fast (on the order of milliseconds to tens of seconds) as compared with the time constants associated with transcriptional regulation (generally on the order of a few minutes or slower) or cell growth (on the order of hours to days). Therefore, dynamic simulations may be performed by considering the behavior inside the cell to be in a quasi-steady state during short time intervals relative to the environment.

17. Time-dependent Cell behavior Beginning at T 0, all the condition as initial Generate regulatory rules based on current environmental and internal conditions Determined which genes are up-regulated Set reactions related to up-regulated genes as unconstrained, otherwise set to 0 Run FBA to calculate the flux distribution Terminated in 3sec, calculate environmental and internal conditions Time Delay rFBA

18. Combined regulatory/metabolic network for central metabolism in E. coli

19. Mutant Study

20. rFBAFBA Correct prediction 106/11697/116

21. Dynamic Growth Simulation Case 1: Aerobic growth of E. coli on acetate with glucose reutilization » When glucose is deleted from the environment, the acetate is then reutilized as a substrate Case 2: Anaerobic Growth on glucose Case 3:Aerobic growth on glucose and lactose

22. Dynamic Growth Simulation Case 1: Aerobic growth of E. coli on acetate with minimal glucose » When glucose is deleted from the environment, the acetate is then reutilized as a substrate Case 2: Anaerobic Growth on glucose Case 3:Aerobic growth on glucose and lactose

23. Aerobic growth on acetate with glucose reutilization

25. Dynamic Growth Simulation Case 1: Aerobic growth of E. coli on acetate with glucose reutilization » When glucose is deleted from the environment, the acetate is then reutilized as a substrate Case 2: Anaerobic Growth on glucose Case 3:Aerobic growth on glucose and lactose

26. Anaerobic Growth on glucose

28. Dynamic Growth Simulation Case 1: Aerobic growth of E. coli on acetate with glucose reutilization » When glucose is deleted from the environment, the acetate is then reutilized as a substrate Case 2: Anaerobic Growth on glucose Case 3:Aerobic growth on glucose and lactose

29. Aerobic growth on glucose and lactose

31. Reference Transcriptional Regulation in Constraints-based Metabolic Models of Escherichia coli, Markus Covert and Bernhard Palsson, doi: 10.1074/jbc.M201691200 Markus W Covert, Christophe H. Schilling and Bernhard PalssonRegulation of Gene Expression in Flux Balance Models of Metabolism, J Theor Biol. 2001 Nov 7;213(1):73-88. Flux balance analysis of biological systems: applications and challenges, karthik Raman and Nagasuma Chandra, Brief Bioinform (2009) 10 (4): 435-449. doi: 10.1093/bib/bbp011 Genome-scale metabolic networks, Marco Terzer Nathaniel D. Maynard Markus W. Covert and J org Stelling, DOI: 10.1002/wsbm.037 Cellular Metabolic Network Modeling, Eivind Almaas, NetSci Conference 2007 http://en.wikipedia.org/wiki/Main_Page