1. BT8118 – Adv. Topics in Systems Biology
Prof. Eivind Almaas
Dept. of Biotechnology, NTNU

2. Course overview 3 double-day meetings: 23&24/9, 14&15/10, 28&29/10
Grade: based on project presentation (50%) and oral examination (50%)
Final exam: 18/11
15-20 min project presentations in plenum
Individual projects handed out 14/10: A metabolic reconstruction

3. Intended Learning Outcomes
At the completion of this course, the student should be able to:
Explain the principles and central methods of linear programming, as well as formulate and solve linear programming problems using pen and paper or MatLab
Discuss the steps involved in metabolic network reconstruction, and describe how to evaluate a model’s fidelity
Explain the principles of constraint-based modeling, and discuss its strengths and weaknesses, as well as using computer analysis to determine a model’s phenotypic behavior
Use FBA, MoMA, and dynamic FBA to analyze a metabolic model and further modify it to achieve a predetermined objective
To achieve the Intended Learning Outcomes, class sessions will be based on active discussions & participation, not solely on lectures.

4. Introduction

5. Cellular networks: GENOME protein-gene interactions PROTEOME
protein-protein interactions
METABOLISM
Bio-chemical reactions
Citrate Cycle

6. Bacterial cells are complex dynamical systems
They interact nonlinearly with their environments through e.g.
- movement (chemotaxis)
- quorum sensing
- hysteretic response
Display functional stability — they generally behave predictably e.g.
- cell cycle progression
- measured growth
- substrate uptake and excretion rates

7. Metabolic chart from Roche Applied Science
Point out TCA cycle (tricarboxylic acid cycle)
…not only networks in biology…

8. Metabolism: Flux Balance Analysis (FBA)
FBA input:
List of metabolic reactions
Reaction stoichiometry
Impose mass balance
Impose steady state
Optimize goal function
FBA ignores:
Fluctuations and transients
Enzyme efficiencies
Metabolite concentrations / toxicity
Regulatory effects
Cellular localization …
Standard form linear program formulation:

9. FBA idea
Most used (and only currently realistic) method for modeling genome-scale metabolism
Based on:
List of all possible reactions
Mass conservation
Steady-state
Optimization of a cellular objective

10. Example: Cartoon metabolism
Optimal growth curve 2 3
Optimization of objective function 1 2 3
optimal growth line
Edwards et al, Biotechn. Bioeng. 77, 27 (2002)

11. Experiment: E. coli growth on glycerol
Study adaptive growth of E. coli on glycerol:
60-day experiment
Three independent populations:
E1 & T=30C; T=37C
Initially sub-optimal performance
glycerol
R.U. Ibarra, J.S. Edwards & B.O. Palsson, Nature 420, 186 (2002)

12. FBA calculations Metabolic flux map Metabolic flux statistics
Some possibilities:
Predict single- and double-knockout mutants, test experimentally
Identify possible minimal genomes
Identify high-flux reaction sets
Identify reasons for enzyme dispensability and gene-dosage
Mechanisms for bacterial adaptation to growth environment, test experimentally

13. How do we get “there”? Need to understand WHAT a metabolic network is
Have a good grasp of what goes into MAKING a metabolic reconstruction
Understand the modeling PRINCIPLES and METHODS to know their strengths and pitfalls
Know state-of-the-art COMPUTATIONAL tools, so that you do not have to re-invent the wheel!!

14. Metabolic network representations
E. Almaas, J. Exp. Biol. 210, 1548 (2007)

15. Effect of network representation
E. Almaas, J. Exp. Biol. 210, 1548 (2007)

16. Effect of network representation
E. Almaas, J. Exp. Biol. 210, 1548 (2007)

17. Metabolic networks scale-free in all domains of life
Large-scale Metabolic Network Structure
Nodes: chemicals (substrates)
Links: chem. reaction
Archaea
Bacteria
Eukaryotes
Metabolic networks scale-free in all domains of life
H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature 407, 651 (2000).

18. How do we get “there”? Need to understand WHAT a metabolic network is
Have a good grasp of what goes into MAKING a metabolic reconstruction
Understand the modeling PRINCIPLES and METHODS to know their strengths and pitfalls
Know state-of-the-art COMPUTATIONAL tools, so that you do not have to re-invent the wheel!!

19. What is a metabolic reconstruction?
2. What steps should go into a metabolic reconstruction process?

22. Thiele & Palsson, Nature Protcols, 5:95 (2010)

23. Refinement of reconstruction

24. Gene-protein relations (GPR)

25. Biomass function

26. Cellular (bacterial) composition
Growth-associated maintenance

27. Confidence score to assess quality of model reconstruction pieces

28. Thiele & Palsson, Nature Protcols, 5:95 (2010)

29. Mathematical representations of metabolic networks

30. Stoichiometric Matrix: “Container” for reaction information
What is missing?

31. Metabolite vs. Reaction centric view

32. Why / how is the S-matrix relevant
Why / how is the S-matrix relevant?  Represent metabolism as dynamical system in steady state

33. Linear Programming

35. Simple example

36. Basic Feasible solutions

37. Solution space Synonymous: Null space or (Right) null space
Rows of A correspond to constraints (metabolite mass conservation) on the variables (fluxes)
Columns of A correspond to reactions

38. Solution space Synonymous: Null space or (Right) null space What is A?
Null space of A?

39. In and out of Null space Stoichiometric matrix: S = [-1 -1 2]
- x - y + 2 z = 0
Normal vector to plane:
[-1, -1, 2] /√6
Possible null space (2-d) basis vectors:
a = [1,1,1] /√3
b = [-1,1,0] /√2
Corresponding projection matrix:
√(2/5), -√(3/5)
√(2/5), √(3/5)
1 , 0 b a

40. Fundamental Theorem of Linear Optimization

41. Simplex algorithm

42. Simplex example