innsbruck 05 mar_2014 copy
TRANSCRIPT
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Improving strain design for biotechnology with constraint-based modelling.
POSTER SESSION:PS3-36
Natalie J. Stanford.
Pierre Millard.
Neil Swainston.
What does modelling offer us over what we already have?
Gene addition
KO/Over-expression?
Conditions?
Current methods for selection are limited.
The current methods are just not suitable.
…this makes engineering strains difficult, time consuming, and expensive.
Conditionselection.
Gene addition
KO selection/Over-expression
Iterativeselection
Computational approaches allow us to explore different strategies quickly, leading to more effective cell design.
A successful strain of butanol producing E.coli was designed in 2011.
First we needed to modify the reconstructed cell so that, in theory, it could produce butanol.
Conditionselection.
Gene addition
KO selection/Over-expression
Iterativeselection
First we needed to modify the reconstructed cell so that, in theory, it could produce butanol.
Conditionselection.
Gene addition
KO selection/Over-expression
Iterativeselection
Gene addition/KO selection.
Gene addition.
Input
Growth
butanol
Checked using:• Flux Balance
Analysis
How did we use Flux Balance Analysis (FBA) to verify that we could make butanol?
Flux Balance Analysis allowed us to compute feasible cellular flux distributions.
GrowthDefined nutrient
input
butanol
10
GrowthDefined nutrient
input
butanol
10 Objective =Max Growth
Flux Balance Analysis allowed us to compute feasible cellular flux distributions.
GrowthDefined nutrient
input
butanol
10 Objective =Max Growth
10
10
10
10
Flux Balance Analysis allowed us to compute feasible cellular flux distributions.
GrowthDefined nutrient
input
butanol
10 Objective =Max Growth
10
10
10
10
Flux Balance Analysis allowed us to compute feasible cellular flux distributions.
GrowthDefined nutrient
input
butanol
10 Objective =Max butanol
We could verify whether butanol could be produced from our gene additions using FBA.
GrowthDefined nutrient
input
butanol
10 Objective =Max butanol
10
10
We could verify whether butanol could be produced from our gene additions using FBA.
We could also verify whether it was possible for the cells to remain viable whilst producing butanol.
GrowthDefined nutrient
input
butanol
10 Objective =Max Both
GrowthDefined nutrient
input
butanol
10 Objective =Max Both
10
5
5
5
5
We could also verify whether it was possible for the cells to remain viable whilst producing butanol.
Conditionselection.
Gene addition/KO selection.
Over-expression
Iterativeselection
Over-expression
KO/Overexpression selection
• Looked at Flux variability profiles to see which reactions were important.
• Identified competing reactions.
Gene addition/KO selection.Checked using:
• FBA
Gene addition.
Gene addition
Gene addition/KO selection.
Gene addition.
Input
Growth
butanol
Checked using:• Flux Balance
Analysis
How did we use Flux Variability Analysis (FVA) to identify important modifications within the
cell?
As we saw in the earlier example, growth could use two different pathways.
GrowthDefined nutrient
input
butanol
10 Objective =Max Growth
GrowthDefined nutrient
input
butanol
10 Objective =Max Growth
Max fluxMin flux.
Flux variability analysis showed us the minimum and maximum flux each reaction could carry, providing the right combination of other
reactions were in place.
GrowthDefined nutrient
input
butanol
10/10 Objective =Max Growth
10/0
10/0
10/0
10/10
10/0
10/0
10/0
Max fluxMin flux.
Flux variability analysis showed us the minimum and maximum flux each reaction could carry, providing the right combination of other
reactions were in place.
We could use this to identify reactions that were important for generating butanol, and those that competed.
GrowthDefined nutrient
input
butanol
10
Max fluxMin flux.
Objective:Max butanol subject to 4 units of growth
4
GrowthDefined nutrient
input
butanol
10/10
Max fluxMin flux.
Objective:Max butanol 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.
GrowthDefined nutrient
input
butanol
10/10
Max fluxMin flux.
Objective:Max butanol 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.
GrowthDefined nutrient
input
butanol
10/10
Max fluxMin flux.
Objective:Max butanol 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.
Conditionselection.
Gene addition/KO selection.
Over-expression
Iterativeselection
Over-expression
KO/Overexpression selection
Conditionselection.
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
Growth
butanol
Checked using:• Flux Balance
Analysis
• Looked at Flux variability profiles to see which reactions were important.
• Identified competing reactions.
Did we manage to predict a computational strain that was similar to the experimental strain?
Reverse betaOxidation cycle
Anoxic conditions.
We predicted 4 knockouts, and anoxic conditions were required to generate butanol.
The strain we predicted using these techniques showed modified functions that were similar to the laboratory engineered strain.
