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<h7>Rationale and Aim</h7> | <h7>Rationale and Aim</h7> | ||
− | < | + | <p> |
+ | After the initial design of the Sensynova platform, it was important to determine, in silico, if | ||
+ | multicellular biosensor systems constructed according to our paradigm would be able to produce | ||
+ | responses to target molecules which were comparable to traditional whole cell sensors. Therefore, a | ||
+ | 3D, spatially explicit, stochastic model was constructed, in which each cell was modelled as a | ||
+ | separate agent containing kinetic equations specific to the biosensor components present in that cell | ||
+ | type. To enable the application of experimentally derived rate constants, an IPTG sensor was | ||
+ | designed according to our platform and modelled. This design was later used as our proof-of- | ||
+ | concept <i>in vitro</i> system.<br /> | ||
+ | <br /> | ||
+ | Additionally, in traditionally engineered biosensor systems, biosensor components are often present | ||
+ | in equal amounts, mostly one detection device to one processing device to one reporter device. | ||
+ | However, other than ease of production, there is no evidence that a component ratio of 1:1:1 is | ||
+ | optimum for all systems. An unexpected side effect of splitting biosensor components into different | ||
+ | cells was the production of a new design space in which biosensor behaviour could be altered by | ||
+ | varying the ratios of cell types, and therefore biosensor components, in a multicellular system. We | ||
+ | wanted to harness this new method of fine-tuning biosensor circuits through the in silico exploration | ||
+ | of cell type ratios and subsequent in vitro confirmation of optimum component ratios. | ||
+ | </p> | ||
Revision as of 11:29, 27 October 2017
Our Models |
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Multicellular Modelling: SimbioticsDiagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption.
After the initial design of the Sensynova platform, it was important to determine, in silico, if
multicellular biosensor systems constructed according to our paradigm would be able to produce
responses to target molecules which were comparable to traditional whole cell sensors. Therefore, a
3D, spatially explicit, stochastic model was constructed, in which each cell was modelled as a
separate agent containing kinetic equations specific to the biosensor components present in that cell
type. To enable the application of experimentally derived rate constants, an IPTG sensor was
designed according to our platform and modelled. This design was later used as our proof-of-
concept in vitro system. |
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Cell Free Protein Synthesis Systems Optimisation: Design of Experiments (JMP)Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption.
Previous research has shown that the concentration of some components of the supplement solution are crucial for efficient protein synthesis, and that for each batch of extract produced the optimal concentration may need to be found (Yang, et al., 2012). Studies which have explored this have only focused on, at most, a few components at a time (Garamella, et al., 2016; Kelwick, et al., 2016), which means that important interactions between the components may have been missed. In this study, a multifactorial approach will be taken to investigate the effect that all supplements have on the protein synthesis activity of CFPS systems simultaneously.
Traditionally, biologists tend to use One Factor At a Time (OFAT) approaches to determine
the effect and importance of factors on a system, which can sometimes be a poor method.
By only determining the effect that a single factor has on a system at a time, important
interactions can be missed. For example, removing only factor A may have no effect, and
removing only factor B may also have no effect, but removing both may cause an adverse
effect. Therefore, it is important to take a multifactorial approach when investigating the
importance of conditions or components of a system, or when trying to optimise a system.
An issue with this approach is that a large number of experiments may be required to fully
investigate all factors. By using statistical methods, a Design of Experiments (DoE) can be
determined which has the minimum number of experiments required to explore questions
such as the importance of factors in a system. This approach also allows for robustness
testing or determining batch-batch variation (Anderson & Whitcomb, 2010). As discussed
here [Link to Cell Free section of wiki], CFPS systems can be plagued with issues rising from
variation, so this approach offers a method to investigate the causes. It could also be used to
determine less important components of the supplement solution premix which is added to
CFPS systems, and hence a minimal supplement premix could be determined. Previous research has shown that the concentration of certain salts in the CFPS supplement premix are crucial for maximal protein synthesis activity [REF]. A Design of Experiments approach was used to determine which of the four salts (magnesium glutamate, potassium glutamate, sodium oxalate, and ammonium acetate) are the most important using the JMP software. A classical screening design was created with all four salts as continuous factors and CFPS activity as the response to be maximised. A concentration of ‘0’ was used as the lower limit for each factor, and the concentration used normally in CFPS supplement premixes was used as the upper limit (Figure 1). The screening design generated is shown in table 1. CFPS reactions were performed using supplement solution premixes with salt concentrations as determined by the main effects screening design. Reactions were incubated with 1.7 μg plasmid DNA encoding sfGFP (superfolder Green Fluorescent Protein) at 37 o C for 13 hours. CFPS activity was calculated as fluorescence intensity at 13 hours minus fluorescence intensity at 15 mins. This data was then used to generate a bar chart of Contrast values and a Half-Norma Plot (Figure 2 and 3) to determine which factors were having the most effect on CFPS activity. It should be noted that predictions for non-primary factors (i.e. interactions) may be inaccurate as they were forced-orthogonal. Considering the primary factors, magnesium glutamate was found to be the salt supplement with the largest contrast value, followed by potassium glutamate. This suggests that these two salt supplements were the most important. Sodium oxalate had a lower contrast value than either of the two glutamate salts, and was considered to have moderate importance in terms of CFPS activity. Ammonium acetate had an extremely low contrast value, suggesting that it may be unimportant for enhancing CFPS activity. The DoE software, JMP, was used to create a surface response design (SRD) for the three salts which were found by the screening design to have the most effect on CFPS activity (magnesium glutamate, potassium glutamate, and sodium oxalate). Ammonium acetate was kept at the default concentration and was not varied. Four SRDs were created using JMP; Central Composite Design-Uniform Precision design (CCD-UP), Box-Behnken (BB), Central Composite Design-Orthogonal (CCD-O), and Central Composite Design (CCD). The design diagnostics feature was used to compare the designs (Figure 4). Specifically, the colour map on correlations, power analysis for each factor and interaction, D, G, and A efficiencies, average variance of prediction, and number of reactions were compared to determine which design would be used. The colour map on correlations shows how correlated two terms are (red is highly correlated, blue is highly un-correlated). The more correlated two terms are, the more difficult it is to determine which is responsible for the effect on the response (Anderson & Whitcomb, 2010). As would be expected, in each design, terms are highly correlated with themselves (observed as a diagonal red line). Other terms are generally very lowly correlated with different terms. For the CCD-UP, BB, and CCD, the terms at the bottom right of the map have correlations above 0. For CCD-UP and BB, these correlations are still very low, but for CCD they are at about 0.5. Power analysis shows the likelihood of detecting an active effect for terms in the design (Anderson & Whitcomb, 2010). The CCD-O had a higher Power for all terms, with CCD-UP having the next highest. BB and CCD had lower Power for all terms, but some terms were higher in the BB design than the CC design, and some higher in CC design than the BB design.
D, G, and A efficiencies are a measure of each design to be D, G, and A optimised. A design
is D optimal if confidence regions for the vector of regression coefficients are minimized, G
optimal if maximum prediction variance over the design region is minimized, and A optimal if
the sum of the regression coefficient variance is minimized (Anderson & Whitcomb, 2010).
The CCD-UP design has the highest D efficiency and the BB design has the lowest. The
CCD design has the highest G efficiency and the BB design has the lowest. The CCD-UP
design has the highest A efficiency and the BB design has the lowest.
Results for the remaining reactions were used to build a model in JMP to predict an optimal
composition for the three salts. The model predicted that at high amounts, magnesium
glutamate and sodium oxalate were having an inhibitory effect, and potassium glutamate
was having an enhancing effect on CFPS activity (Figure 3.3.3a). It is well known that
magnesium ions are crucial for protein synthesis, for example in the functioning of
ribosomes, however at high amounts magnesium can become inhibiting to protein synthesis
by stalling translation at the translocation step (Li, et al., 2014). Therefore, it is not
unexpected that magnesium glutamate causes a decrease in protein synthesis activity at
certain concentrations. |