Presented at Babraham Institute November 2014.

1. RobOKoD: microbial strain design for
(over)production of target
compounds.
Natalie J Stanford, Pierre Millard, Neil Swainston.

2. RobOKoD = Robust, Optimisation, Knockout, and
Dampening.

3. Gene addition
KO/Over-expression?
Conditions?
Current methods for microbial strain design are
limited.

4. …this makes engineering strains difficult, time
consuming, and expensive.

5. Computational approaches allow us to explore different
strategies quickly, leading to more effective cell design.
Condition
selection.
Gene
addition
KO selection/Over-
expression
Iterative
selection

6. Condition
selection.
Gene
addition
KO selection/Over-
expression
Iterative
selection
First we need to modify the reconstructed cell so
that, in theory, it can produce the target.

7. First we need to modify the reconstructed cell so
that, in theory, it can produce the target.
Condition
selection.
Gene
addition
KO selection/Over-
expression
Iterative
selection
Gene addition/
KO selection.
Gene addition.
Input
Growt
h
Target
Checked using:
• Flux Balance
Analysis

8. How is Flux Balance Analysis (FBA) used to verify that
the target can be produced?

9. Flux Balance Analysis allows us to compute feasible
cellular flux distributions.
Growth
Defined nutrient
input
Target
10

10. Growth
Defined nutrient
input
Target
10 Objective =
Max Growth
Flux Balance Analysis allows us to compute feasible
cellular flux distributions.

11. Growth
Defined nutrient
input
Target
10 Objective =
Max Growth
10
10
10
10
Flux Balance Analysis allows us to compute feasible
cellular flux distributions.

12. Growth
Defined nutrient
input
Target
10 Objective =
Max Growth
10
10
10
10
Flux Balance Analysis allows us to compute feasible
cellular flux distributions.

13. Growth
Defined nutrient
input
Target
10 Objective =
Max target
We can verify whether the target can be produced
from gene additions using FBA.

14. Growth
Defined nutrient
input
Target
10 Objective =
Max target
10
10
We can verify whether the target can be produced
from gene additions using FBA.

15. We can also verify whether it is possible for the cells
to remain viable whilst producing target.
Growth
Defined nutrient
input
Target
10 Objective =
Max Both

16. Growth
Defined nutrient
input
Target
10 Objective =
Max Both
10
5
5
5
5
We can also verify whether it is possible for the cells
to remain viable whilst producing target.

17. Condition
selection.
Gene addition/
KO selection.
Over-
expression
Iterative
selection
Over-
expression
RobOKoD (Robust
Overexpression, Knockout and
Dampening
Condition
selection.
Condition selection.
• Could be
identified
using
phenotypic
phase
plane
analysis.
Gene addition/
KO selection.
Gene addition/
KO selection.Checked using:
• FBA
Gene addition.
Gene
addition
Gene addition/
KO selection.
Gene addition.
Input
Growt
h
butanol
Checked using:
• Flux Balance
Analysis
• Looked at Flux variability
profiles to see which
reactions were important.
• Identified competing
reactions.

18. RobOKoD uses two principles in its strain design.
1. To improve target production network changes should
reduce carbon loss to peripheral pathway,
2. Flux Variability of each reaction will differ depending on
whether the reaction is important for growth, generating
the desired product, both, or neither.

19. We use Flux Variability Profiling (FVAp), which
relies on Flux Variability Analysis (FVA).

20. As we saw in the earlier example, growth can use
two different pathways.
Growth
Defined nutrient
input
target
10 Objective =
Max Growth

21. Growth
Defined nutrient
input
target
10 Objective =
Max Growth
Max flux
Min flux.
Flux variability analysis shows us the minimum and
maximum flux each reaction can carry, providing the right
combination of other reactions are in place.

22. Growth
Defined nutrient
input
target
10/10 Objective =
Max Growth
10/0
10/0
10/0
10/10
10/0
10/0
10/0
Max flux
Min flux.
Flux variability analysis shows us the minimum and
maximum flux each reaction can carry, providing the right
combination of other reactions are in place.

23. We can use this to identify reactions that are important for
generating the target, and those that compete.
Growth
Defined nutrient
input
target
10
Max flux
Min flux.
Objective:
Max target subject to
4 units of growth
4

24. Growth
Defined nutrient
input
target
10/10
Max flux
Min flux.
Objective:
Max target subject to
4 units of growth
4/4
10/6
4/0
4/0
4/0
4/0
4/0
6/6
We can use this to identify reactions that are important for
generating the target, and those that compete.

25. Growth
Defined nutrient
input
target
10/10
Max flux
Min flux.
Objective:
Max target subject to
4 units of growth
4/4
10/6
4/0
4/0
4/0
4/0
4/0
6/6
We can use this to identify reactions that are important for
generating the target, and those that compete.

26. Growth
Defined nutrient
input
target
10/10
Max flux
Min flux.
Objective:
Max target subject to
4 units of growth
4/4
10/6
4/0
4/0
4/0
4/0
4/0
6/6
We could use this to identify reactions that were important
for generating butanol, and those that competed.

27. Building the FVAp Part 1:
Optimising target, subject to fixed amounts of
growth.

28. Growth
Defined
nutrient
input
target
Max flux
Min flux.
Objective: Max
target subject to
2.5 units of fixed
growth.
2.5
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10

29. Growth
Defined
nutrient
input
target
Max flux
Min flux.
2.5
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
2.5/0
10
2.5/0
10/7.5
7.5
2.5/0
2.5/0
2.5/0
10
10
Objective: Max
target subject to
2.5 units of fixed
growth.

30. Growth
Defined
nutrient
input
target
Max flux
Min flux.
510
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux10
10
Objective: Max
target subject to 5
units of fixed
growth.

31. Growth
Defined
nutrient
input
target
Max flux
Min flux.
5
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
5/0
10
5/0
10/5
5
5/0
5/0
5/0
10
10
Objective: Max
target subject to 5
units of fixed
growth.

32. Growth
Defined
nutrient
input
target
Max flux
Min flux.
10
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10
Objective: Max
target subject to 10
units of fixed
growth.

33. Growth
Defined
nutrient
input
target
Max flux
Min flux.
10
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10/0
10
10/0
10/0
0/0
10/0
10/0
10/0
10
10
Objective: Max
target subject to 10
units of fixed
growth.

34. Building the FVAp Part 2:
Optimising growth, subject to fixed amounts of
target.

35. Growth
Defined
nutrient
input
target
Max flux
Min flux.
2.5
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10
Objective: Max
growth subject to
2.5 units of fixed
target.

36. Growth
Defined
nutrient
input
target
Max flux
Min flux.
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
7.5/0
10
7.5/0
10/2.5
7.5/0
7.5/0
10
10
2.5
7.5
7.5/0
Objective: Max
growth subject to
2.5 units of fixed
target.

37. Growth
Defined
nutrient
input
target
Max flux
Min flux.
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10
7.5
Objective: Max
growth subject to
7.5 units of fixed
target.

38. Growth
Defined
nutrient
input
target
Max flux
Min flux.
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10
7.5
Objective: Max
growth subject to
7.5 units of fixed
target.
2.5/0
2.5/0
2.5/0
2.5
10/7.5
2.5/0
2.5/0

39. Growth
Defined
nutrient
input
target
Max flux
Min flux.
100% Target 100% Growth
Flux
100% Target 100% Growth
Flux
10
10
10
10
Objective: Max
growth subject to
10 units of fixed
target.
0
0
0
0
10/10
0
2.5/0

40. In more complicated networks there is more nuance
in the reaction profiles.

41. Knockouts profiles show an increase in flux as we
move towards maximal growth.
Opt Target
Fixed growth.
Opt growth
Fixed target.

42. Strong overexpressions show a decrease in flux as we
move towards optimal growth.
Opt Target
Fixed growth.
Opt growth
Fixed target.

43. Weak overexpressions flux advantages for the target.
Opt Target
Fixed growth.
Opt growth
Fixed target.

44. Dampenings show flux disadvantages for the target.
Opt Target
Fixed growth.
Opt growth
Fixed target.

45. Testing RobOKoD.

46. A successful strain of butanol producing E.coli was
designed in 2011.

47. Reverse beta
Oxidation cycle
Anoxic conditions.
We predicted 4 knockouts, and anoxic conditions
were required to generate butanol.

48. The strain we predicted using these techniques showed
modified functions that were similar to the laboratory
engineered strain.

49. And the production of butanol was closer to that in
the experimental strain.