Name: Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow (Video) | JoVE Journal
Uploaded: 2025-10-17T12:50:00.000+00:00
Duration: 8 min 58 s
Description: Discover automated biosensor optimization using design-of-experiments and genetic library screening. This protocol identifies robust candidates with enhanced sensitivity for biotechnological applications.

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GAG and PLR were supported by a BBSRC DTP grant (BB/M011208/1). MC was supported by a BBSRC Responsive mode grant (BB/P01738X/1). We would also like to thank The Henry Royce Institute for Advanced Materials (funded through EPSRC grants nos. EP/R00661X/1, EP/S019367/1, EP/P025021/1 and EP/P025498/1) for access to their facilities&#160;and the MBEC UKRI Engineering Biology Mission award (BB/Y00812X/1) for technical support and training.

The authors declare no conflicts of interest.

Libraries of genetic elements encompassing broad genetic variability are crucial for the success of a DoE-based methodology. As demonstrated in Figure 2A, the number of theoretical variants increases with the number of positions to be targeted for mutagenesis as well as the degree of randomization, with this ever-increasing library size creating significant screening bottlenecks. Reducing the number of targeted positions or the degree of randomization can reduce the number of variants that need to be screened and is an attractive approach if a more targeted approach to tuning is required or if significant a priori knowledge of the system exists as a guide. In the case of systems like biosensors, however, which feature many overlapping features or may not have well-characterized genetic elements, it is difficult to circumvent the library size requirement. The usage of automated liquid handlers can trivialize the labor of picking colonies and culturing variants, specifically when increased data point collection is required for the plotting of more advanced functions like dose response curves vs two data point comparators, such as ON/OFF. Whilst it is difficult to specifically quantify time saved from including automated workflows, the major benefit of its implementation is in the time that can be devoted to other parallel experiments38.
Despite this, in many cases where library sizes exceed 104, even liquid handler-assisted methodologies become impractical39. In such cases, a preliminary screening of variants using flow cytometers is a highly attractive approach, with a much larger sorting capacity of up to 107 cells per h40. Using fluorescence-activated cell sorting (FACS) with two rounds of positive selection and at least one round of negative selection has been routinely employed in the triaging of the top-performing variants of biosensors before more extensive characterization16,26,42. The development and execution of such protocols utilizing FACS have been reviewed in detail elsewhere31. Initial triaging screens are possible using plate based liquid handler assays, as outlined in the above protocol; by comparing ON/OFF data using a single effector concentration as shown in Figure 3D, only variant biosensors with an appreciable gain of function (3.6-fold as provided in the methods) can be selected for further detailed characterization, streamlining library development and experimental run time. Both FACS and liquid handler platforms, however, come with significant capital investment for the lab and often require a level of technical expertise to operate and maintain. Agar plate-based screens provide a lower technical and financial barrier to entry, and have been used in combination with gfp or blue-white colony screening to select for RBS library variants as well as enzyme mutants based on the intensity of fluorescence43,44. These too, however, are limited in the number of variants that can be effectively screened, but also require a significant difference in fluorescence to be detectable44. As a compromise between fully combinatorial simultaneous mutation of multiple elements at once, a &#34;divide and conquer&#34; methodology instead aims to split large variant libraries into more manageable screening blocks41. Whilst a modularized approach may be pragmatic, it does not effectively explore the combinatorial design space, as the performance of biological components is known to be highly context dependent, especially in bacteria, where transcriptional and translational coupling is prevalent. This has the potential to result in design choices that target the local maxima instead of the global optima.
Transport and recognition of specific inducers represent another important feature of biosensor development that deserves mention, despite not being the main focus of the outlined protocol. A lack of an appropriate aTF for the target molecule represents a significant challenge for researchers. Rational selection of an existing aTF that binds a structural analog of the target effector can provide an ideal template on which to apply codon randomization in order to tune specificity of the aTF to the desired effector17. Alignment of the target sequence to solved structures or Alphafold predictions can enable identification of effector binding sites with suspected amino acid residues subjected to semi-rational randomization and ranking, adaptable to the protocol17. Similarly, selection and modulation of transporters responsible for the import/export of effectors can significantly alter biosensor dose-response characteristics. Transporter gene expression was found to be a significant player in biosensor response, with a diversified library of Ptac promoters and RBS controlling the expression of a MucK transporter used to stabilize its expression over multiple rounds of FACS, enhancing the robustness of biosensor response17. Similarly, screening of different transporter proteins themselves can modulate the specificity of biosensors, with screening of a putative set of PcaK transporters using a PCA-responsive biosensor leading to the identification of two transporters uniquely able to uptake 3,5-hydroxylated substrates, expanding the set of compounds detectable by that system24.
Automated platforms can become even more powerful tools for characterization and exploration when coupling DoE principles with deep learning models46,47. After first exploring the design space of a biosensor with a high-throughput platform, machine learning algorithms have been leveraged to predict the function of uncharacterized sequences with good accuracy 47. The methods outlined in this protocol could easily be applied in the testing of new language learning models for promoter design, akin to models that have been developed based on cross-RBS sequence prediction48. Furthermore, integration of such models can take advantage of sequence-function data contained within the non-functional promoter designs and provide critical insight into the broader functionality of the biosensor as a whole. Namely, this would have the potential to address a crucial limitation of the DoE workflow, that false positives, e.g., a non-functional promoter, do not necessarily equate to a measured output of zero, with phenomena such as spurious transcription or introducing an element of noise into DoE data, which is difficult to identify and control for. Crucially, in order to encompass the total experimental design space, any generated libraries must exhibit a broad range of activities, as an insufficient activity range will result in skewed design outputs and create unreliability in any statistical models generated from the dataset30. If low library variability becomes an issue, exploring other sequence elements or increasing the degree of randomization can be implemented, and the variation between the libraries can be compared until a suitable variant pool is attained.
A crucial aspect of the DoE process involves the correct estimation and selection of primary and secondary effects obtained from the DSD that will then be incorporated into statistical analysis. DoE, being a modelling-based approach, is highly prone to overfitting and bias, which can quickly complicate the optimization process through guiding iterative engineering efforts into suboptimal sections of the design space49. As such, it is critical to ensure that any designs are robustly insulated from such effects, both at the stage of conception and the analysis of data. At the initial screening design phase, centered runs where all factors are set to the geometric mean (i.e., 0,0,0,0) can help to reduce model bias by better accounting for non-linear interactions, whilst not adding significant experimental burden (1-3 additional runs)49. Additionally, including randomized designs can help to account for extraneous variables that are not included in the experimental design but could still influence the response variables that are being measured. In a biological context, randomization addresses issues like spatiotemporal effects, such as plate position, or batch-to-batch variation, preventing such effects from significantly affecting data interpretation. Attention to such details at this early stage can improve the robustness of models and lead to more reliable conclusions. After performing the DSD-suggested experiments, statistical analysis of the data is required to elucidate those factors that had a significant impact on output parameters. Half-normal plots offer an intuitive visual representation of effect magnitudes, with effects with no significance typically falling along a straight line, whereas effects with a significant impact will deviate from this line, enabling simple selection of the most important factors in biosensor optimization. With this in mind, a conservative approach to effect selection should be taken, as with all modelling, to reduce the risk of model over-fitting.
The ability to rapidly design and optimize biosensors and other genetic circuits will greatly accelerate the pace of research in the field of biotechnology, such as in strain and enzyme development, but also in real-time diagnostics. The emergence of DoE-based screening methodologies in this field is particularly promising, facilitating the efficient use of time and resources whilst exploring the maximum possible design space, and has already been used to great effect in the optimization of genetic circuits for metabolic pathways and for biosensors31,33,34,51. DoE is particularly well suited to multifactorial optimization problems in which many first, second or even third order interactions are at work which would be otherwise difficult to interrogate with the typical one factor at a time experimental design approach. Further, efforts to engineer one aspect of a biosensor often inadvertently result in the sacrifice of another parameter, such as improving sensitivity at the expense of dynamic range51. Through the ability of DoE to map such hidden interactions as well as create models that predict biosensor behavior, the build test learn cycle is greatly accelerated.

The ability to regulate the expression of genes is an essential function in all forms of life, facilitating dynamic, real-time responses to both internal and external influences, ranging from chemical effectors to biophysical stimuli1. The myriad number of transcriptional regulatory systems found across nature has enormous potential in the augmentation and acceleration of metabolic engineering and biotechnological research enabled by synthetic biology. In prokaryotes, much of the regulation that occurs in the cell is controlled through one-component regulatory mechanisms driven by the interactions of allosteric transcription factors (aTFs) with a number of small-molecule effectors2. Typically consisting of an effector binding domain (EBD) and DNA binding domain (DBD), aTFs act as molecular switches upon binding to specific small molecule effectors, modulating the affinity of their DBD for specific DNA operator sequences located in the promoter regions of the genome, resulting in activation or repression of transcription3. This deceptively simple mechanism has given rise to a number of diverse regulations strategies, ranging from repression/de-repression systems to activators able to detect and respond to a staggering number of small-molecule effectors or environmental stimuli4. Consequently, synthetic biology has leveraged this diversity in order to construct genetically encoded biosensors capable of coupling a plethora of input signals into bespoke outputs, i.e., fluorescent reporter protein production and regulation of synthetic pathways. When applied in biotechnological workflows, such systems enable the real-time monitoring of intracellular concentrations of metabolites, dynamic regulation of pathways, and facile readouts aiding in the development of more efficient pathways, strains, and bioprocesses5,6,7,8.
Fundamentally, biosensors are characterized according to a number of parameters, including specificity, operational range, dynamic range, sensitivity, and slope, with the dose response curve of a biosensor describing these parameters through the output signal as a function of ligand concentration (Figure 1)9,10,11,12. The Hill equation can be used to fit the performance characteristics of a biosensor, providing semi-empirical evidence for each of the parameters described above, thus providing a means of characterizing the biosensor system whilst also enabling assessment of efforts required to tune the system toward application-driven outcomes. Specificity can be defined as the relative differences in signal output induced by one effector compared to an array of other potential effector molecules and is typically modulated at the EBD level of the aTF. Operational range is defined as the range of ligand concentrations that the biosensor is able to sense, dictating the range of concentrations to which the biosensor will be able to respond. Dynamic range, by contrast, describes the ratio of the highest measurable activation (ON) state to the inactivated (OFF) state and is an important parameter for ensuring the biosensor reliably reports effector concentrations above background auto-fluorescence10. Sensitivity for the effector molecule is described as the concentration required to elicit a certain output signal, measured by the half maximal concentration (EC50) of the effector13. Finally, the slope of the curve is denoted by the cooperativity (nH) of the aTF for its operator site at the promoter and results in a more digital or analog response output profile. Cooperativity arises from protein-protein interactions between ligand-bound aTFs forming multimeric complexes that strengthen affinity for the operator site, resulting in sharp increases in responsiveness of the circuit with saturating ligand concentrations and a more digital response profile14.
The dose response characteristics of a biosensor can significantly affect the suitability of its applications, and are often a &#39;make or break&#39; point for any conceptual application. Biosensor performance can vary enormously from system to system, with the operational range typically varying from 0.1nM-10mM13, whereas dynamic ranges can be from 1.4-2000 fold15. Furthermore, whilst the number of effector compounds reported for aTFs is broad (metabolic products, amino acids, metals, antibiotics, quorum sensing, etc.)11, it is not all encompassing, presenting challenges to researchers when a suitable biosensor for their effector does not already exist in the literature. Synthetic biology enabled engineering of biosensors that address such challenges, permitting the tuning of effector scope and dose-response characteristics to more closely align with the research outcomes of the application, and has been used to address both aforementioned problems, respectively16,17. Two key areas for engineering are targetable via synthetic biology, the promoter region of the biosensor and the aTF itself, mediating their effects at the transcriptional and protein level. Approaches that focus on the genetic elements of the biosensor in order to modulate performance at the promoter level, include engineering of the RBS, operator sites, and -35, -10 (hexboxes) in order to tune sensitivity, operational and dynamic ranges, and are the focus of the methodology outlined in this manuscript (Figure 1). Alternatively, modifying the selectivity and sensitivity of the biosensor through analysis of its effector binding domain and mutation of residues involved in effector coordination (using sequence homology and/or structural biology) can modulate its response to a cognate effector18,19,20 or change it entirely toward a non-cognate effector of interest16,17,21. Such tuning efforts can then direct biosensors towards specific applications, with these commonly being metabolic controllers (feedback loops, control circuits), primary screening tools (Enzyme or strain discovery), secondary screening tools (enzyme variant screening, metabolic engineering), and transporter bioprospecting22,23,24. One such example involves the use of a muconic acid-responsive aTF, CatM, combined with an engineered promoter sequence to improve dynamic and operational range. This system was integrated with fluorescence-activated cell sorting to isolate the most effective muconic acid production strains based on GFP fluorescence following adaptive laboratory evolution25.
Whilst the success of the engineering strategies outlined above is self-evident, the interdependencies of the levers available to a synthetic biologist to tune biosensors can complicate the design process and prolong the period spent in biosensor development, which may dissuade some researchers from implementing biosensors into their own workflows. As illustrated in Figure 1, attempts to tune a biosensor towards a particular outcome through modifying the hexboxes may inadvertently attenuate other parameters, this interdependency being a recognized challenge in biosensor engineering12. Common approaches of biosensor tuning consist of rational design and directed evolution engineering, with the former relying on a priori understanding of the structural and mechanistic features of the system to focus experimentation on components with a high likelihood of success; whereas the latter relies upon randomized mutagenesis and natural evolution coupled to a high throughput screen to select for variants exhibiting optimized characteristics26,27,28,29,30. Whilst effective, both techniques also suffer from some drawbacks: targeted engineering, for example, through its focus on specific structural or functional elements, is prone to limited exploration of the full experimental space and may ignore allosteric or secondary effects30. Untargeted library generation and screening, whilst ideal for optimizing designs in an unguided manner, requiring little in the way of upfront design work (i.e., library design and generation), do require a bias toward useful mutation. Without such a bias, it is expected that a much larger percentage of mutations may be deleterious, requiring more screening31. As such, holistic approaches that consider not only the primary effect of constituent parts of the biosensor (aTF, RBS, operator sites and hexboxes) but also how these components interact with one another to influence biosensor parameters are highly attractive.
Structured multivariate experimentation and statistical modelling methodologies have been widely adopted in process engineering workflows in order to interrogate multidimensional experimental space using the minimum possible number of experiments. This approach, which combines experimentation and modelling, termed design of experiments (DoE), crucially allows researchers to optimize complex multivariable processes toward defined outcomes whilst not requiring extensive a priori knowledge23,30. Whilst typically applied to the optimization of continuous variables for which structured experimental exploration is more facile, DoE has also been used in the optimization of metabolic pathways at the genetic level31,32,33. This involves an initial screening step in which the factors thought to be most important to the desired outcome are selected, followed by an optimization step whereby the selected factors are adjusted to obtain the desired output, in this case to optimize specific biosensor parameters in order to increase their scope of application. Critically, this technique can be coupled to automated liquid handler platforms to increase the screening throughput up to mid-sized libraries of 103 - 104 to globally optimize biosensor performance on a data-driven basis23. Below, we describe a protocol for the implementation of a DoE driven approach to biosensor optimization, assisted with liquid handling robotics to streamline library generation, screening, and data collection for the global optimization of biosensor sensitivity.

<table><tbody><tr><td>1000 &amp;micro;L CO-RE Tips, sterile non-filter</td><td>Hamilton&amp;nbsp;</td><td>235939</td><td></td></tr><tr><td>2.2 mL 96 Deepwell Plate, Square Wells with V-Shaped Bottoms</td><td>Thermo</td><td>11594754</td><td></td></tr><tr><td>300 &amp;micro;L CO-RE Tips, stacked NTRs, sterile</td><td>Hamilton&amp;nbsp;</td><td>235985</td><td></td></tr><tr><td>96 well clear bottom, black microtitre plates</td><td>Greiner&amp;nbsp;</td><td>655097</td><td></td></tr><tr><td>Agarose&amp;nbsp;</td><td>Invitrogen</td><td>16500100</td><td></td></tr><tr><td>Assembled Plasmid DNA</td><td>User Supplied&amp;nbsp;</td><td>NA</td><td></td></tr><tr><td>ClarioStar Plus Microplate reader&amp;nbsp;</td><td>BMG</td><td>NA</td><td></td></tr><tr><td>Dexynucloetide (dNTP) solution mix&amp;nbsp;</td><td>NEB</td><td>N0447L</td><td></td></tr><tr><td>Dimethyl Sulfoxide (DMSO)&amp;nbsp;</td><td>Fisher BioReaggents&amp;nbsp;</td><td>BP231-100</td><td></td></tr><tr><td>DNA Ladder 100bp</td><td>NEB</td><td>N3231L</td><td></td></tr><tr><td>DNA Ladder 1KB</td><td>NEB</td><td>N3232L</td><td></td></tr><tr><td>DNA Sequencing&amp;nbsp;</td><td>Source Bioscience</td><td>NA</td><td></td></tr><tr><td>DNA Synthesis&amp;nbsp;</td><td>IDT</td><td>NA</td><td></td></tr><tr><td>&lt;em&gt;Escherichia coli &lt;/em&gt;DH5&amp;alpha; Compotent Cells</td><td>NEB</td><td>C2987H</td><td></td></tr><tr><td>Gel Loading Dye, Purple X6 No SDS</td><td>NEB</td><td>B7025S</td><td></td></tr><tr><td>Gene pulser/Micropulser Electroporation Cuvettes, 0.2cm gap</td><td>Biorad&amp;nbsp;</td><td>1652082</td><td></td></tr><tr><td>Graph Pad Prism 10&amp;nbsp;</td><td>GraphPad</td><td>NA</td><td></td></tr><tr><td>Hamilton Star Liquid Handler&amp;nbsp;</td><td>Hamilton&amp;nbsp;</td><td>NA</td><td></td></tr><tr><td>HT multitron Plate Shaker Incubator&amp;nbsp;</td><td>Infors HT</td><td>NA</td><td></td></tr><tr><td>JMP Statistical Analysis Suite&amp;nbsp;</td><td>JMP</td><td>NA</td><td></td></tr><tr><td>LB Broth (Miller)</td><td>Miller&amp;nbsp;</td><td>L3522</td><td></td></tr><tr><td>LB Broth (Miller) with agar&amp;nbsp;</td><td>Sigma</td><td>L3147</td><td></td></tr><tr><td>MicroPulser Electroporator&amp;nbsp;</td><td>Biorad&amp;nbsp;</td><td>1652100</td><td></td></tr><tr><td>NEBuilder HiFi DNA Assembly Master Mix</td><td>NEB</td><td>E2621S</td><td></td></tr><tr><td>Q5 High fidelity DNA polymerase</td><td>NEB</td><td>M0491S</td><td></td></tr><tr><td>QIAprep Spin Midiprep Kit</td><td>QIAGEN</td><td>&amp;nbsp; 12143</td><td></td></tr><tr><td>QIAprep Spin Miniprep Kit</td><td>QIAGEN</td><td>27104</td><td></td></tr><tr><td>QIAquick Gel Extraction Kit&amp;nbsp;</td><td>QIAGEN</td><td>28706X4</td><td></td></tr><tr><td>QIAquick PCR Purification Kit&amp;nbsp;</td><td>QIAGEN</td><td>28104</td><td></td></tr><tr><td>Qpix 420 Colony Picker</td><td>Molecular Devices UK</td><td>NA</td><td></td></tr><tr><td>SOC Outgrowth Medium&amp;nbsp;</td><td>NEB</td><td>B9020S</td><td></td></tr><tr><td>SYBR Safe DNA Gel Stain</td><td>Invitrogen</td><td>S33102</td><td></td></tr><tr><td>TAE Buffer (Tris-acetate-EDTA, 50X)&amp;nbsp;</td><td>Thermo Fisher&amp;nbsp;</td><td>B49</td><td></td></tr><tr><td>UltraPure&lt;sup&gt;TM&lt;/sup&gt; Dnase/Rnase-Free Distilled Water&amp;nbsp;</td><td>Invitrogen</td><td>10977015</td><td></td></tr></tbody></table>

efficient sampling of genetically encoded biosensor design space enabled with a design of experiments and automation workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Genetically encoded biosensors are powerful tools for high-throughput information processing, enabling the transduction of environmental or chemical input signals into a variety of outputs. This allows dynamic sensing, direct control, and fine-tuned regulation of gene expression across a wide range of biotechnological applications, including enzyme optimization, strain development, and microbial process control. To make them fit for purpose, biosensor performance can be refined by modifying the stoichiometry of biosensor circuit components (e.g., transporters, input and output modules), and/or tuning associated host-biosensor intermolecular interactions (e.g., DNA-protein, protein-protein). However, here, the vast number of possible biosensor permutations creates a complex combinatorial design space, necessitating careful optimization of screening strategies to identify configurations that deliver the desired phenotypic performance. This complexity is further compounded by biosensor performance traits, such as tunability, which require effector titration analysis under monoclonal screening conditions. Consequently, the need to explore diverse sequences and experimental space makes fractional sampling methods particularly well-suited for this purpose. Underpinning this workflow are Design of Experiment (DoE) algorithms, which are well-positioned to allow efficient statistically-based structured mapping and fractional sampling of this combinatorial experimental design space. 
Reported within is a combined high-throughput automation and computational approach to efficiently sample the design space of allosteric transcription factor-based biosensors to afford distinct configurations with both digital and analogue dose-response curves. The protocol begins with the creation and automated selection of promoter and ribosome binding site libraries. These libraries, and their corresponding expression data, are transformed into structured dimensionless inputs, allowing computational mapping of the full experimental design space. Fractional sampling is then performed using a DoE algorithm and coupled with effector titration analysis using a high-throughput automation platform. This workflow provides an agnostic framework for the development and optimization of future biosensor systems and genetic circuits, providing a regulatory toolkit for the synthetic biology community.

This protocol provides a method for the systematic global optimization of genetically encoded biosensors through automation-assisted genetic library generation and assessment. This is coupled with design-of-experiment methodologies to streamline experimentation and enable the selection of genetic components to tune biosensors to specific design outcomes.

Genetically encoded biosensors are powerful tools for high-throughput information processing, enabling the transduction of environmental or chemical input ...

The development and optimization of biosensors towards biotechnological applications is the scope of our research. Specifically, we aim to understand how to optimize and engineer biosensors through modulation of their genetic elements. Intuition-driven design choices, such as classical promoter engineering and fluorescence activate cell sorting are techniques routinely applied in the characterization and design of biosensors.Design of experiment methodologies have not yet been widely adopted in genetic circuit design. Through this protocol, we seek to increase the uptake of these types of techniques in genetic circuit and biosensor design. To begin, determine the theoretical library size and calculate the number of individual variants needed to ensure greater than 95%library coverage.Calculate the required volume of antibiotic supplemented lysogeny broth media according to the number of colonies that are to be screened. Open the liquid handler software and click on run next to the MTP liquid transfer program. Place the prepared media in the corresponding reservoir position.Then fill the deck with empty microtiter plates according to the layout. Set the program to dispense 200 microliters of media. Click okay to confirm program start.Now, transfer filled microtiter plate wells to a colony picker platform and unseal and transfer square auger plates of Pseudomonas putida transformed with plasmid variant library DNA into the colony picker platform. Use the colony picker to inoculate each prefilled well with a single colony from the transformant plates. Reseal the inoculated plates and transfer them to an offline shaking incubator.Post-incubation, return the grown plates to the liquid handler platform and unseal. Click run next to the add glycerol to MTP protocol. Ensure that the plate layout on screen matches that of the liquid handler dock.And sequentially, click okay for the protocol to run. Once finished, seal the plates and briefly mix them in an offline shaking incubator at 800 revolutions per minute for five minutes. Barcode the plates and store at 80 degrees Celsius until needed.Calculate the required volume of antibiotic supplemented media for deep-well blocks. Click run next to the DWB liquid transfer program. Ensure that media is added to the correct reservoir.The empty deep-well blocks are in correct layout positions and that an adequate supply of tips is available. Set the program to dispense 495 microliters of media. When ready, sequentially click okay to start the program.Now, seal the filled deep-well blocks with breathable membrane and transfer to temporary storage at 4 degrees Celsius. Next, click run next to the inoculate from thawed MTP program. Ensure that MTP cryo stocks and fill deep-well blocks are transferred to the platform per layout and that sufficient tips are loaded.Sequentially, click okay to initialize. When the program finishes, seal the inoculated overnight deep-well blocks with breathable membrane. Transfer them to an offline plate shaker incubator set for 16 hours at 30 degrees Celsius, 800 revolutions per minute, and 75%humidity.Reseal, mix, and return cryostock microtiter plates to 80 degrees Celsius freezer. Now, calculate the required volume of media supplemented with various effector and antibiotic concentrations for the overnight deep-well blocks to be screened. Click run next to the DWB liquid transfer program.Then ensure effector-supplemented media reservoirs are correctly placed. Add empty deep-well blocks into the liquid handler platform. When sufficient tips are available, sequentially click okay to start the protocol to generate assay deep-well blocks.After filling, seal the assay deep-well blocks with breathable membrane. Refill the liquid handler with empty plates. Repeat inoculation until all assay blocks are filled.Next, click run next to the transfer to assay DWB program. After unsealed assay deep-well blocks with effector-supplemented media are placed correctly, transfer and unseal overnight deep-well blocks containing grown P.putida per layout. Sequentially click okay to start the protocol.When the program has finished, seal transfer assay plates to offline incubator. Discard the overnight deep-well blocks after inoculation. Next, transfer the deep-well blocks into a centrifuge.Pellet cells at 4, 000 G in an ice-controlled rotor at 18 degrees Celsius for five minutes. After discarding the supernatant, place the centrifuge blocks onto the liquid handler platform. Calculate the volume of one times PBS required based on the number of deep-well blocks to be screened.Click run next to the assay set up PBS resuspension DWB program, and set the dispense volume to 500 microliters. Then ensure PBS is added to the correct reservoir, and array the centrifuged plates according to layout. Click okay to start the program after confirming tip availability.Reseal and remove resuspended deep-well blocks from the liquid handler. Check the underside of the plate to ensure pellets are completely resuspended. Click run next to the assay setup cells and PBS edition MTP program.Then transfer resuspended deep-well blocks into the liquid handler according to layout. Load the empty microtiter plates according to layout and set dispense volume to 200 microliters before clicking okay. Transfer the filled microtiter plates to an offline multi-mode plate reader and measure the relative fluorescence and OD600.Automated screening of 5, 000 promoter variants identified top candidates showing greater than 3.6-fold activation. The EC 50 values for 100 unique variants were plotted to visualize the sensitivity distribution and identify robust candidates. EC 50 values were computed for 226 enriched variants and ranked using Lin-log transformation to form a sensitivity-scaled library.A definitive screening design was constructed using 1, 0, and 1 level variance from four modules. Transport, Regulator, P-out, and Output Ribosome-Binding Site model profiles revealed how changes in expression levels of the four modules non-linearly impacted EC 50 and Hill's coefficient. Identifying optimal expression combinations with RBS-Out, showing a strong positive effect on both sensitivity and slope.The globally optimized biosensor variant incorporating ideal module strengths demonstrated enhanced sensitivity, and Hill coefficient compared to both the parental and DSD-optimized versions.

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JoVE Journal

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Discover automated biosensor optimization using design-of-experiments and genetic library screening. This protocol identifies robust candidates with enhanced sensitivity for biotechnological applications.

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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