Team:William and Mary/Implementation

Proposed Implementation



In addition to bringing attention to the lack of orthogonality assessment in the field, our overall goal for our toolkit is to have it be used by the scientific community for orthogonality assessment in E. coli -- with the goal of increasing the efficacy and safety of synthetic circuits that address global problems. With this goal in mind, we knew that our methods of evaluating orthogonality had to be both accurate and accessible. As a result, we created a toolkit that includes a mathematical model and several sensor circuits to provide an orthogonality assessment. In this page, we will explain how we envision the implementation of our toolkit within the scientific community.


Orthogonality Toolkit

Our toolkit consists of both a data-driven mathematical model and a system of sensor circuits. Our model has two integrable components; the first is a mechanistic model using an ODE system which dynamically models metabolism of an E. coli cell, and outputs concentrations of molecules involved in metabolic processes. After conducting a literature review of published RNA-seq data, our team found that certain genes were consistently differentially expressed between a host with and without a circuit, and coupled equations describing concentrations of these “marker” genes with the purely metabolic system. We first simulate an unburdened E. coli cell, then simulate induction of a circuit by modifying parameters derived from measurements from our sensor circuits to reflect increased burden due to induction of a circuit. Based on these outputs, we quantify burden-based orthogonality based on changes in their steady states between simulation conditions.

In addition, we derived a completely data-driven “beyond burden” model which directly uses RNA-seq datasets to find correlations between “marker” genes and an overall increase in differential gene expression. This model uses “marker” genes which are highly correlated with overall increased differential gene expression as a proxy for detecting increased non-orthogonality. Our models are data-driven, requiring teams to input data from their circuits to receive an output, and are able to utilize three different kinds of data: RNA-seq data, qRT-PCR data, and fluorescence output data from our sensor circuits. The mathematical model is available on this wiki as well as on GitHub as well and therefore accessible to all teams.

​​Our team designed a system of sensor circuits to be used in conjunction with our mathematical model as the second part of our toolkit. These sensors produce a fluorescence output that when input into our model can provide an orthogonality assessment. Within our system, there are two separate sets of circuits*; the Central Dogma “Burden” sensors and the “Beyond Burden” sensors. The Central Dogma “Burden” system senses burden at the transcription, translational, and post-translational levels. Decreasing levels of fluorescence between the host with and without an additional circuit will indicate levels of burden on the host. Our second set of sensors, the “Beyond Burden” sensors, utilizes the “marker” genes revealed by our RNA-seq analysis. These sensors also produce a fluorescence output in which changing levels indicates a lack of host-circuit orthogonality. Teams can input the fluorescence output from these systems into our model, resulting in an orthogonality assessment.


Proposed Implementation

In order to use our model with our in vivo sensor plasmids, teams will transform one of our sensor plasmids and the circuit to be tested into the host. After growing these cells overnight, teams will measure the fluorescence values of the host at 0, 1, 6, 12, 24, and 48 hour timepoints. Fluorescence values can be measured using a plate reader. As these circuits utilize sfGFP, the excitation and emission values are set to 485 and 528, respectively. Conversion of these fluorescence values to molecules (see directions below) will enable teams to use these fluorescence values as inputs to the model, which is available on this wiki and on Github. Please see the Circuit Distribution section below for information about the distribution of our sensor circuits.


Circuit Chassis Delivery Method: In Vivo vs. In Vitro

As accessibility was a goal of our team, we consulted experts and stakeholders for advice concerning circuit delivery methods. However, the feedback we received was contradictory as to the most accurate and accessible delivery method. As a result, our team designed additional in vitro and genome integration circuit delivery systems. These could be tested for future implementation.

In vitro Paper-Based System

Our team designed an in vitro version of our Central Dogma burden sensors. Testing circuits’ orthogonality with our cell free system (CFS) requires a simple procedure. Once the host cell has been transformed with the circuit and grown, the cell will be homogenized and all cellular components will be added to the CFS. In order to ensure that our CFS is able to accurately assess burden levels, each CFS will contain no additional additives other than the sensor circuit DNA. To produce model inputs, the process will be repeated by adding untransformed host cell lysate to each CFS. By use of a plate reader, teams are able to measure the fluorescence values of these circuits. As these circuits utilize sfGFP, the excitation and emission values are set to 485 and 528, respectively. Comparison of the fluorescence values, after being converted to molecules, of the host with and without a circuit will be used by our model to produce an orthogonality assessment. Please refer to our molecule conversion protocol above for instructions for how to convert fluorescence values to molecules.

While our team was able to design this method and the associated testing protocol, we were not able to complete this testing process in the wetlab. For future steps with our project, we intend to further test this system.

Genome Integration

Integrating our sensor circuits into the host cell genome would decrease the burden caused by plasmid DNA and unintended interactions between the foreign plasmid and the host genome. This integration would also result in less steps required for teams to assess orthogonality, as only the circuit to be tested would need to be transformed into our sensor strain. We decided on a method of integration based on the lambda RED and CRISPR Cas9 systems, avoiding the long process of expressing a burdensome antibiotic resistance selection gene and then potentially having to attempt removing it after selection. Additionally, this method is self-curing, so all plasmids transformed to facilitate integration will cease propagation, preventing further burden to the host.

Once both plasmids have been transformed into E. coli cells, the cells would be plated on LB agar with kanamycin and spectinomycin, since the backbones of these two plasmids encode resistance to those antibiotics. Addition of arabinose induces lambda RED expression on the pCas plasmid to enhance the frequency of recombination, allowing our sensor circuit to recombine into the genome at the yjcS loci. The expression of Cas9 by pCas and sgRNA targeting yjcS by pTargetX would cause double-stranded breaks in the genome at this loci, so the cell would die unless successful recombination occurred with the sensor circuit in pTargetX, facilitated by lambda RED proteins and the homologous arms upstream and downstream of the circuit. Curing of pTargetX is achieved by incubating the strain overnight with IPTG, to induce an sgRNA that targets the pTargetX origin of replication. Curing of pCas is accomplished by growing the colonies at 37 C, because of its temperature sensitive origin of replication (all steps up to this point would be at 30 C) (Jiang et al., 2015).

While our team was able to design this method, we were not able to complete this testing process in the wetlab. For future steps with our project, we intend to further test this system.


Circuit Distribution

As accessibility is a key portion of our project, it is important to our team that our sensor circuits are readily available to other teams for a low cost. While our in vivo system would be available on Addgene for teams who want to avoid the hassle of emailing our team, we plan to make our system available to anyone who asks. When a team asks for our system, we plan to mail our plasmids on 3MM paper. For distribution of our in vitro circuit, we plan to develop a paper strip CFS system. However, our team has not yet currently developed this paper strip method. Finally, if a team were to ask for our genome integration method, we would provide them with a stab of the appropriate bacteria.


Further Steps

Our team has many next steps for the implementation of this toolkit. Our first step is to conduct additional testing. Currently, our team has a successful proof of concept using one test plasmid. However, we would like to test our toolkit on other plasmids to confirm accuracy before widely implementing our system. Including assessment of new test plasmids into our model will help to improve its accuracy on many data sets. While our team applied our model to several RNA-seq data sets, there is a lack of RNA-seq data comparing host circuit expression with and without a host. Therefore, our model would greatly benefit from additional input sets. In conjunction with using the input from researchers assessing their circuit’s orthogonality to improve our model, we also aim to update our model with additional RNA-seq data sets as they become available. In addition to confirming the accuracy of our model, our team also wants to create proof of concepts for our other circuit delivery methods: in vitro and genome integration. Creating these systems will increase team’s access to our toolkit and will enable us to reach our goal of increasing the amount of orthogonality assessment in the field of synthetic biology. Additionally, our team plans to develop additional versions of our in vivo sensor circuits. Developing new circuits with different antibiotic resistance genes, fluorescent reporter genes, and origins of replication would allow our system to be used to test a wider variety of plasmids.


Jiang, Y., Chen, B., Duan, C., Sun, B., Yang, J., Yang S. (2015). Multigene Editing in the Escherichia coli Genome via the CRISPR-Cas9 System. Applied and Environmental Microbiology, 81(7), 2506-2514. DOI:

*Our team understands that many of these circuits can fit into both the Central Dogma sensor circuit category and "Beyond Burden" sensor circuit category; therefore, these circuits were grouped together for clarity.