Integrated Human Practices
Our project aims to provide iGEM teams and synthetic biologists with an easily accessible method to assess the orthogonality of their circuits, which is defined as the lack of unintended interactions among parts of a circuit and between a circuit and host. To accomplish this goal, we conducted an intensive review of the literature and published RNA-sequencing data, designed several sensor circuits, created a mathematical model, and consulted many experts and stakeholders at various levels of our design process. Experts and stakeholders we interviewed include those involved in iGEM, representatives from synthetic biology companies with interest in our toolkit, and synthetic biologists in academia who could benefit from use of our toolkit. Our team was able to incorporate feedback from these experts into every step of our project. Their input aided us in creating a comprehensive sensor circuit system and multilevel model that are easily accessible to all iGEM teams.
Accessibility is a fundamental theme of our project. As circuit-host orthogonality is crucial for ensuring maximal circuit efficiency, modularity, fieldability, and predictability and safety of all genetic circuits, and is therefore relevant to all iGEM teams and synthetic biologists, our team wanted to ensure that our toolkit would be highly accessible, as many existing methods for quantifying orthogonality, such as RNA-Seq and qRT-PCR, are costly and technically challenging. As a result, a key focus of our IHP was increasing the accessibility of our project. Many of the experts we interviewed gave us great feedback on our ideas and were able to increase the accessibility of our project.
As discussed below, our team received contradictory information from our IHP interviews, which led us to conclude that a wide range of experts and stakeholders need to be consulted regarding our project in order to reach a clear consensus on the most accessible and effective implementation method.
An overview of each IHP interview and how they impacted the evolution of our project is summarized below. Please click the dropdown menu to see the full report.
Overall Integration of Human Practices
Our team incorporated input from our interviewees throughout our design process. Dr. Barrick recommended that our team develop sensors of other processes in the cell, such as protein secretion and membrane occupancy, that his 2019 team suspected hindered cell growth. Additionally, Dr. Smith recommended that our team design a sensor that responds to a decrease in cellular concentration not an increase. Antigen 43 (AG43), a biofilm producing gene, was shown to be commonly downregulated in our venn diagram analysis. To incorporate both of these expert’s feedback, our team developed a sensor to respond to decrease in biofilm levels.
Dr. Barrick also suggested improvements for our burden sensors system. He noted that due to the large abundance of phosphate in the cell. Phosphorylation is not an accurate measure of circuit impact on the cell. To incorporate his advice into our design, we developed two additional post translational modification sensors for our central dogma sensing circuit: a protease sensor and a glycosylation sensor.
With accessibility as a key tenant of our project this year, our team sought to make our project accessible to as many teams as possible. As a result, we wanted to develop a circuit testing method which many different individuals would be able to use. Originally our team was deciding between using two different methods: in vitro and genome integration. Mr. Marken suggested for our team to utilize both of these methods in parallel. He noted that while in vitro methods increase accessibility they are difficult to use. Dr. Barrick also mentioned the accessibility of in vitro methods. However, he and Dr. Smith both expressed concern that an in vitro system would not be able to accurately replicate the fitness costs of a cell. As accessibility is crucial to our project, our team wanted to find a way to utilize the in vitro system. To still have the accessibility of an in vitro system, our team decided to use solely cell lysate in our in vitro system without the addition of any other cellular components, thereby eliminating any excess cellular machinery which may alleviate fitness costs.
Mr. Ortiz disagreed with the claim of Dr. Gale and Dr. Smith about the accessibility of in vitro systems. While he agrees that in vitro systems can be very cheap if an individual makes their own mixes, he states that these mixes can be very difficult to make accurately, potentially leading to teams having to incur a large cost to purchase a mix. Further, he states that as both in vitro systems and genome integrations require a specialized skill set, they may not be as widely accessible to teams as an in vivo system. Therefore, he recommended that our team develop this in vivo system, and in response to his feedback, we did.
Our team’s meeting with Mr. Hasnain helped to increase the accessibility of our model. Originally our team intended to use a Koopman operator system to mathematically model orthogonality. However, after meeting with Mr. Hasnain, it became clear that in order for us to use this model system, teams would need to perform RNA-seq to use our model. Due to the cost and time constraints of RNA-seq, this method is not easily accessible and this meeting propelled our team to move in a different direction for our model.
Mr. Marken cautioned against using metabolic models due to the potential inaccuracies associated with representing all host cell functions. To combat this potential inaccuracy our team designed a model that enabled us to extend beyond a simply metabolic model. Mr. Ortiz also offered additional advice to help us combat inaccuracies within our model. He mentioned that noise had a large potential of negatively impacting our model. To help combat this noise, he suggests that our team perform many replicates of our experimental data and test our model on many different real datasets.
Summaries of Individual IHP Interviews
Mr. John Marken
John Marken is a PhD student studying bioengineering in Dr. Richard M. Murray’s research laboratory at Caltech. As a previous iGEM team member and leader, and a current synthetic biologist, he offered insight into the state of the field regarding orthogonality assessment of genetic constructs and the shortcomings of the current debugging techniques for circuit design. He emphasized the need for a tool to assess orthogonality that would be widely available in order to test the assumption that exogenous parts are modular and to truly make synthetic biology an engineering field. In addition, he gave us advice on making our project description and goals more clear, as our initial explanation was insufficient in conveying the bidirectional nature of orthogonality and detailing which particular level we are assessing through our project. When asked about our model, he stated that metabolism-based models are often inaccurate due to difficulties with representing all living functions of the cell. He recommended using a model which is able to reach beyond metabolism. We further inquired about our method of implementation for the diagnostic circuits as we were debating between an in-genome or an in vitro approach. Although he expressed the difficulty of working with TX-TL (in vitro ) systems, he advised us to develop both methods in parallel where we tested our diagnostic circuit designs in both conditions and compared the efficiency and accessibility of these implementation options.
Integration into Project
As a result, our team developed a model that expands past a simple metabolic model. In addition, we designed an in vitro system with which to test the orthogonality of circuits and planned out a method for genome incorporation.
John Marken is a PhD student studying bioengineering in Dr. Richard M. Murray’s research laboratory at Caltech. He was an iGEM team member and leader in his undergraduate years and is currently conducting research in synthetic biology. Mr. Marken is currently working to develop an “open source” toolkit for synthetic biology of soil microbes. He hopes to identify genes that will allow microbes to persist in the soil for extended periods of time and develop a kit of these genes which others will be able to use for circuits intended for long-term soil deployment. Due to the potential for environmental release of his engineered microbes, the concept of orthogonality is crucial to his research, as it is necessary to ensure the safety of all circuits deployed in the real world. Additionally, ensuring maximal orthogonality of his circuits will aid in increasing the efficiency of these circuits by minimising negative effects on cell function, and will assist the long-term deployment potential of these circuits.
When we inquired about his view on our project’s rationale, he strongly agreed that the widely-accepted assumption that synthetic systems are modular, thus isolated from the host interface, needs to be addressed within the field. Rather than thinking of the host cell as an empty chassis, he emphasized that we should be building circuits with the interface in mind to ensure predictability and safety. He further identified a lack of sustainable and scalable methods for measuring orthogonality with the general approach in the field being continuous trial-error during the design process or a highly costly and manual way of performing RNA-seq. He mentioned that our approach to orthogonality assessment seemed intriguing and promising, especially since he finds that within the field of synthetic biology, there is a reliance on the “black box method,” and therefore circuit debugging is often a manual process. When asked about the delivery method of our circuits (both in vitro and genome integration approach), he highlighted that in vitro systems can be very difficult to work with. However, he noted that he saw their benefit for our project in terms of accessibility. He suggested that we work on both of these delivery systems in parallel. As a result, our team investigated both an in vitro and a genome integration system for our orthogonality sensor systems. Additionally, Mr. Marken provided insight to our team concerning our team’s model. Originally, our team planned to build upon existing metabolic models. However, Mr. Marken noted that these models are typically inaccurate due to difficulties with representing all cell functions. In response to his advice, our team decided to develop a model utilizing a different system to prevent this potential inaccuracy.
Mr. Aqib Hasnain
Mr. Hasnain is a PhD student studying mechanical engineering with synthetic biology applications at UCSB. His research focuses on applications of control theory in deconstructing host-circuit interactions, and in particular designed the Koopman operator model, a data-driven model for which RNA-sequencing (RNA-Seq) data is inputted, and returns a dynamical system describing how genes in the host and circuit interact. In addition to clarifying various aspects of his model, he offered us suggestions about what other types of data would be best suited to work with his framework, as well as various methodologies we could verify the accuracy of the model’s results. Accordingly, we looked for datasets like the ones he proposed (finer grained data with less discrete time points and proteomics datasets), and took into account his suggestions on how we could conduct phase plane analysis of the dynamical states of the host-circuit system in our model.
Integration into Project
After meeting with Mr. Hasnain, it became clear that his model would not work for our purposes. Our team aimed to make our mathematical model widely accessible to all iGEM teams. Requiring an input of RNA-Seq data, as his model does, would make this too expensive for many teams, and therefore inaccessible.
August 16, 2021
Mr. Hasnain talked to us in detail about the Koopman operator model and helped to increase our understanding of this system. The Koopman operator mapping and dynamical mode decomposition framework takes a nonlinear function and examines observables (some transformation of linear function). The decomposition transforms a nonlinear function to a linear one, to make fitting data easier, especially when measurements are sparse. Underlying interactions of genes are assumed to be held fixed, which allows us to compare different time points of circuit induction states to isolate which interactions are from the circuit and remove those to uncover the underlying fixed interaction dynamics, which all occur in linear space. Using this method, it is possible to map back to the original space once dynamics are uncovered using a reverse transformation. The system is time invariant, making phase plane analysis valid. The Koopman operator system can propagate forward to a subsequent time step using only data from the previous time step.
Numerous data points are crucial to this model; therefore, if there are sparse time points, it is ideal to generate replicates using samples from present data points and taking the standard deviation and mean (although regression approaches for fitting aren’t particularly effective in this case since data is limited). It is possible to verify the accuracy of our model by combining proteomics and time series data, the former of which is likely to be much less sparse than RNA-Seq.
To generate a continuous time approximation model, many time points of data are necessary, especially if oscillations are present. Sparser data points are not ideal, but still technically fine for bistability. However to get accurate frequency measurements, limit cycles, etc., a continuous measured output such as fluorescence data is best. In a continuous time system, phase plane analysis is valid, but needs to be transformed first through converting from linearized state back to nonlinear state via regression or optimization (least error), completing phase plane analysis, and returning to a linearized state. Otherwise, there will not be interesting dynamical behavior (ex. bistability), as fixed points are confined to the origin. To reintroduce dynamical behavior to the linear transformed system, a noise term (e.g. vector with magnitude 1) can be added to push the system out of a steady state confined to the origin.
Dr. Jeffrey Barrick
Dr. Jeffery Barrick, an advisor of the University of Texas at Austin’s (UT Austin) iGEM team for the past nine years, provided his expertise on circuit design and offered many suggestions for improvement of our sensor systems. Based on the results of the 2019 UT Austin iGEM project, Burdenometer, he noted that protein secretion and membrane occupancy produce large burdens on host cells. He recommended that we design a sensor circuit to measure the orthogonality of these cellular processes. As a result, our team developed our AG43 biofilm sensor for this purpose. Further, due to the wide cellular abundance of ATP, he stated that measurement of phosphorylation would not provide an accurate measurement of bacterial burden.
Integration into Project
As a result, our team designed glycosylation and protease sensors as potential candidates for measurement of the orthogonality of post-translational modifications. Additionally, Dr. Barrick advised our team on the use of in vitro systems. Our team was deciding between using an in vitro or in vivo system. Dr. Barrick highlighted that creating an in vitro system would increase accuracy and accessibility of our orthogonality sensor. Following his advice, our team designed an in vitro version of our Central Dogma Orthogonality Sensor.
August 19, 2021
Dr. Jeffrey Barrick is a professor at the University of Texas at Austin and has been an advisor of the UT Austin iGEM team for the past 9 years. In addition to his expertise within the iGEM competition, his research concerns the concept of evolution, specifically unwanted evolution and preventing evolutionary failures within the field of synthetic biology. He agreed with our assessment that the lack of orthogonality testing is an issue within the field of synthetic biology. Dr. Barrick offered advice on our circuit design at every level of the central dogma. At the transcriptional and translational levels, he recommended testing the two systems individually before using an aptamer fusion system to more easily pinpoint issues. Furthermore, for the post-translational modification level, he noted that the post-translational modification of phosphorylation may not effectively reflect burden as phosphate is not a limiting factor within the cell. In response, our team started to look into glycosylation and proteases as potential candidates for our PTM sensor. In addition to examining our circuit design, he also offered new ideas of other concepts related to orthogonality for potential sensing systems. He suggested that our team design orthogonality sensors targeting protein secretion and membrane occupancy. According to Dr. Barrick, his 2019 team suspected that these cellular processes hindered the cell during their Burdenometer project. Dr. Barrick’s suggestions lead to the development of our AG43 biofilm sensor, protease sensor, and glycosylation sensor.
August 22, 2021
Additionally, Dr. Barrick offered guidance as to the delivery method of our circuit. Previously, our team was deciding between using an in vitro system and an in vivo system. Dr. Barrick provided valuable feedback on the positives and negatives of these two systems. He noted that in vitro systems are easily accessible to a wide audience and allow for rapid testing. However, he also stated that these systems may not be able to accurately reproduce the fitness costs associated with an engineered cell. As accessibility is a crucial component of our team’s project this year, our team sought to find a way to counteract the potential inaccuracies associated with in vitro testing. To address this issue, our team decided to solely use cell lysate in our in vitro system, thereby eliminating any excess cellular machinery which may skew our cellular burden measurements.
Dr. Gale Smith
Dr. Gale Smith, Chief Scientist and Senior Vice President of Vaccine Development at Novavax, provided his opinion on the value of our project in the vaccine and bioengineering industries and offered suggestions for improvement of our project. He expected that our project could allow researchers to “open up the black box” of interactions between a circuit and its host. He stated that for Novavax, “orthogonality is important to us, but is not a level of control that we have right now.” The prospect of identifying problems that can’t be seen was intriguing to Dr. Smith, who foresaw possible applications to research in academia as well as companies such as Novavax that always desire to make products as safe and efficient as possible. Dr. Smith also suggested that our team design a circuit to sense negative feedback in order to broaden our assessment of burden’s impact on the levels of the central dogma. We incorporated negative feedback into the circuit we designed to sense secretion. He supported our decision to assess heat shock protein expression as a measurement of stress that circuits place onto the host. Dr. Smith also warned against an in vitro implementation of our circuits because of the importance of compartmentalization in cell dynamics, which would not be accounted for in a membrane-less cell-free system.
Integration into Project
In response to Dr. Smith’s feedback, we designed a circuit to sense negative feedback within the host cell. This was our Antigen 43 (AG43) sensor circuit, which places sfGFP under the control of the flu promoter, which is the promoter that regulates AG43 expression in E. coli . Through our extensive analysis of published RNA-Seq data, we found that flu , the gene which encodes AG43, was downregulated by the host cell in response to transformation with a circuit.
September 16, 2021
Dr. Gale Smith informed that in pharmaceutical research, they do conduct orthogonality tests to ensure consistency of the product. However, this involves a different definition of orthogonality, as they measure the purity, mass, molecular weight, and charge distribution, of the molecules they produce. After explaining our definition of orthogonality and the details of our sensor circuits, he told us that orthogonality measurement is something that would be important to Novavax if there were an accessible way to assess it, but he felt they currently don’t have as much control over orthogonality as they would like. He likened the design-build-test cycle to a “black box”, where the inner mechanics of the host cell and circuit are not entirely understood, but researchers adjust what they can to control the circuit’s function and tune the output in the desired way. He thought our sensor circuits were very clever, and concurred that heat shock proteins are well known to be linked to stress in cells. He suggested shaping a circuit around the negative feedback of RNAi that downregulates translation. He warned us to be cautious of an in vitro implementation because compartmentalization is an important factor to consider in cell dynamics. According to Dr. Smith, removing barriers within a cell (such as cell membranes) using a cell-free system could affect the accuracy of our assessments, as it does not take cell barriers into account.
Dr. Smith expressed a desire to make products as safe as possible and the best they could be. He saw that this project could have immediate applications in research, and potentially industry down the line. He explained that biotechnology typically makes use of mammalian cells like human 293T or CHO, so strictly assessing orthogonality in E. coli would have limited applications, but if we were to expand the project in the future to eukaryotic cells, he would be in favor of using these tools. He expressed support of anything that allowed more control or consistency to his work at Novavax expressing nanoparticles in insect cells. He was intrigued by the prospect of identifying problems that couldn’t be seen. He admitted that this was the first he had heard of a toolkit like ours. He expected that developing this could open up the “black box” of circuit-host interactions to further analysis.
Dr. Brian Renda
Dr. Brian Renda, Associate Director at Ginkgo Bioworks and author of several orthogonality-related papers, such as Engineering reduced evolutionary potential for synthetic biology, provided his opinion on the value of orthogonality assessment in synthetic biology and offered suggestions for improving our project. He expressed the need for a tool to help circuits reach their optimum intended function by allowing researchers to understand why circuits do not work as intended. He laid out the benefits and drawbacks of several different implementation methods for our design, including some that we had not previously considered. His overarching advice on the decision we faced was to select one implementation approach and acknowledge its shortcomings, as none of the available options are drawbacks or challenges. He found our approach of focusing on protein expression to be practical because of its relative ease to quantify and relevance to teams aiming to yield a product. He corroborated our monitoring of stress proteins and cellular burden to indicate orthogonality after learning that this was supported by our RNA-Seq analysis.
Integration into Project
In response to Dr. Renda’s input, we investigated several methods of implementation for our circuits, including cotransformation of plasmids, genome integration, and in vitro testing. However, we chose to focus mainly on cotransformation of plasmids due to input from other experts indicating that the challenges associated with in vitro testing and genome integration could make these implementation methods less accessible.
October 8, 2021
Dr. Brian Renda emphasized the desire to get circuits behaving optimally and suggested that unintended interactions may impact their intended function and their ability to fulfill that function. He stated that synthetic biology circuits are designed for specific functions, and that although it rarely works as intended the first time, iGEM teams often don’t stop to consider why. He reminded us that orthogonality has many definitions and can mean different things to different circuits. In some cases, burden on the host cell may be caused by unintended interactions between the circuit and the host genome, but in other cases, it may be that certain genetic parts are not compatible with the host. He informed us that from an evolutionary standpoint, plasmids in a cell traditionally indicate a greater problem for the cell than solely the burden they inflict. He also encouraged us to consider using a vector with low copy number to construct our circuits, (such as pSC101, which has a copy number of 1) as it is likely more orthogonal than a high copy vector.
Dr. Renda suggested using a single copy vector for our sensor circuit, as it would be similar to being integrated within the genome, but with a lower risk of recombination. He recognized that high copy plasmids would provide a stronger fluorescence signal but cautioned that this could skew our data, as a high number of promoters could decrease the number of available transcription factors. Related to this, he informed us that transcription factors bind to different regulatory sequences at various rates, which he advised is something that we should either account for or acknowledge. He also stated that high copy plasmids are more likely to break because of the burden they are placing on the host cell. Dr. Renda concluded that an in vitro method of implementation provided a more accurate way of measuring physiological interactions between the circuit and the host without interference of our sensor circuits, but he was not sure whether teams would have the capacity to use such a method because of how challenging cell-free systems are to design and use. One potential problem he mentioned was replicon incompatibility in the cell-free system. Dr. Renda opined that genome integration would inherently skew our results as different bacterial strains may respond very differently to genome integration of our circuits. In addition, our genome integrated sensors would be limited to one strain, while orthogonality is contextual to its chassis strain. According to Dr. Renda, genome integration also introduces potential for mutations by altering the genome. Another alternative implementation option he brought to our attention was conjugation into the genome. Ultimately, he advised us to pick an approach and identify its limitations, since there were benefits and drawbacks to each.
To Dr. Renda, our approach of addressing protein expression was a pragmatic approach to assessing orthogonality, but he stressed that this was only one aspect of orthogonality. He admitted that to some, burden caused by resource competition could be considered separate from unintended interactions. For example, he mentioned that although the T7 phage promoter does not compete with other promoters, it is nonorthogonal in other ways. However, Dr. Renda conceded that the burden measured by our burden sensors was likely due to a lack of orthogonality within the cell. As RNA-Seq data allows us to see how host gene expression is altered by transformation of a circuit, he approved of our use of this data, which would strongly support our argument that the markers we had chosen for our project are indicators of a lack of circuit-host orthogonality. He concluded the interview by generously offering to connect us to more scientists at Ginkgo so we could get further feedback.
Mr. Luis Ortiz
Mr. Luis Ortiz is a scientist on the Selection & Strain Improvement team at Ginkgo Bioworks with experience in developing biosensors. He agreed with our conclusion that there needs to be an assessment method for orthogonality and liked the concept of our project. He offered critical advice on our implementation methods regarding the benefits and disadvantages of each: use of plasmids, in vitro experimentation, and genome integration. Overall, Mr. Ortiz was in support of using the in vivo implementation method. He argued that this method will allow our system to be used by a large number of teams for various bacterial strains and species, as plasmids are so widely used and are relatively easy to work with. Additionally, several teams and synthetic biologists are likely to have access to plate readers, which will allow for fluorescence measurement. However, he did note that using an additional plasmid could inflict greater burden onto the host and potentially skew our fluorescence measurements. He suggested using the MoClo system to put both our cassette and the tested cassette in the same plasmid to help alleviate excess burden.
Integration into Project
In response to Mr. Ortiz’s feedback, our team decided to focus on designing an in vivo circuit system using plasmids. Mr. Ortiz also offered advice on our mathematical model. He asserted that with our current model, aggregating our data and gaining meaningful results could be difficult. He also advised our team to perform experiments with many technical replicates to improve the accuracy of our model. He also recommended that our team test our model on existing data sets to confirm that our results make sense with data other than our own.
October 16, 2021
Mr. Luis Ortiz is a researcher at Ginkgo Bioworks with a specialization in biosensors. Mr. Ortiz agreed with our conclusion that there needs to be an assessment method for orthogonality and liked the concept of our project. He elaborated that this need is more urgent for certain applications in the field. He used his work as an example, where being able to assess and improve orthogonality could lead to higher titers of cannabinoid production in yeast cells, since they could tune the specificity of their biosensors to identify bottlenecks in the production process.
He offered critical advice on our delivery methods regarding the pros and cons of each of the three methods: in vitro , in vivo , and genome integration. He mentioned that the genome integration system poses issues. This system would require different procedures based on the specific organism which it was intended to be used in; therefore, it cannot be used generically across species. This method requires special knowledge of how to utilize this system, creating a barrier for people who are not familiar with this system. He highlighted a similar issue with the in vitro system requiring more specialized knowledge. Although in vitro methods can be cheap when one produces their own mixes, Mr. Ortiz stated that it is very difficult to make these mixes correctly, potentially forcing teams to purchase expensive mixes. He also warned that relying on cell extract alone can result in low fluorescence output that is hard to detect. Beyond this problem, as someone with experience in cell-free systems, he informed us that the cell-free environment is very different from that of a cell, and he called it a “dark art” because of how difficult it is to acquire data in this manner. As an example, DNA is actively expressed as soon as it is introduced to the system because no repressors have been produced yet, unlike the more stable intracellular environment. While in vitro would make sense for our purposes, he has worked with teams in the past that did everything correctly to set up an in vitro experiment, and it still wouldn’t work. Overall, Mr. Ortiz was in support of using the in vivo plasmid method. He argued that this method will allow it to be used by many teams as it is a common synthetic biology method; therefore, teams will not need any special training to use our system. Additionally, many teams will have the appropriate measurement tools for assessing fluorescence from in vivo systems. However, he did note that using an additional plasmid could provide extra burden to the cell and potentially skew our orthogonality measurements. He suggested using the MoClo system to enable both our cassette and the tested cassette to be in the same plasmid to help alleviate this issue.
From the perspective of an expert in working with sensor circuits, Mr. Ortiz provided additional design and testing suggestions. He suggested implementing our in vivo system into plasmid backbones with different origins of replication and antibiotic resistances would allow for more widespread use of our system. This would provide us the possibility to isolate one circuit from the other or select individually for either. Additionally, when told about our phosphorylation sensing circuit, he worried that adding a circuit may negatively impact the EnvZ/OmpR system, even without an active cassette. Because the introduction of any additional plasmid can cause significant changes to a host cell, he recommended that our team run an experiment with our test circuit and a backbone without a cassette to determine if fluorescence decrease is from the additional cassette or simply the backbone. This dummy version of the plasmid as a control would determine the degree to which the burden is caused by the vector backbone and not the particular circuit design, which might produce a product that interacts with the host machinery. While he cautioned against using this method, he told our team that if we do decide to use the genome integration method, we should attempt to integrate our circuit into many different parts of the genome, to ensure simplicity of use.
Finally, Mr. Ortiz offered advice on our model. He suggested a user interface to simplify the experience of entering fluorescence values and receiving an orthogonality score. According to him, RNA-seq measurements are dependent on copy number, so he encouraged us to consider that as a factor. He noted that with our current model design it may be difficult for teams to derive meaningful conclusions from our outputs, especially considering the potential noise in the system. He suggested that to combat this noise for the generation of a single score, our team should perform many technical replicates of our experiments and practice using our model on real datasets to increase accuracy. He reasoned that doing biological triplicates shouldn’t prove too difficult given a 96-well plate. He also provided us with minor suggestions on how to improve the labelling of our graphics to better describe our data.
Mr. Paul Maschhoff
Mr. Paul Maschoff, a mammalian engineer at Ginkgo Bioworks and an author of A data-driven method for quantifying the impact of a genetic circuit on its host agreed that orthogonality assessment is generally important for iGEM teams to incorporate and described our circuit system and modeling approaches as being logical and potentially effective. When asked about which implementation method would be best to use, he felt that a plasmid system would be most accessible, as he was concerned that genome integration would be technically challenging for the end user and strain-limited and that an in vitro system would lack translatability to live systems.
Integration into Project
Based on Mr. Maschoff’s input, our team decided to select our plasmid system of implementation as our main plasmid delivery method. However, we investigated genome integration and in vitro systems as part of future directions for our project. He was also concerned that our use of sfGFP in our sensor circuits would make our sensor circuits incompatible with test circuits that produce GFP, so we investigated different types of markers that could be used in future designs of our circuits in order to make them compatible with a wider range of test circuits.
October 19, 2021
Mr. Paul Maschoff agreed that orthogonality assessment is important for iGEM teams to consider in their projects and that our circuit system and that our modeling approaches are logical and potentially effective. He told us he could not think of an easy way to assess orthogonality in a better way. He foresaw value in the information our project could provide to teams with intermediately burdensome circuits where obvious growth inhibition cannot be observed. When asked about which implementation method for our circuits would be most accessible and effective, he felt that a plasmid system would be most ideal, as plasmids are widely used in iGEM and synthetic biology as a whole. He was concerned that an in vitro system would not be translatable to live systems, and therefore would not be a good fit for our project. He also felt as genome integration is technically challenging, it would not be very accessible to many iGEM teams unless we integrated our circuits into a widely used BSL1 strain of E. coli that would be available for teams and synthetic biologists to use. He was also concerned that our use of sfGFP in our sensor circuits would make our sensor circuits incompatible with test circuits that produce green fluorescence, so we investigated different types of markers that could be used in future designs of our circuits in order to make them compatible with a wider range of test circuits. When asked whether our toolkit could be applicable to industry, Mr. Maschoff replied that Ginkgo Bioworks has access to RNA-sequencing and mass spectrometry, which would make our circuit system less applicable for their projects, but that our model could potentially be applicable as it is compatible with RNA-sequencing data. He reminded us to be explicit in how we distinguish burden from orthogonality. He also expressed interest in our phosphorylation sensor and mentioned that assessment of post-translational burden seems to be less common than assessment of translational burden in the field. He also advised us to induce our phosphorylation system by changing osmolarity to ensure that cellular resources would be expended to express it and we would be able to measure post-translational burden.
Protection of Human Subjects Protocol
To ensure that our interviews with medical doctors were conducted in a professional manner, especially with respect to informed consent, we submitted a protocol for the protection of human subjects to our university. Prior to the submission of the protocol, our entire team underwent on-line CITI training in both protection of human subjects and research ethics. The Protection of Human Subjects Committee (PHSC) at our university approved our protocol, which outlined procedures for obtaining informed consent.