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
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
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
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.