Team:William and Mary/Poster

Poster: William_and_Mary


Presented by Team William_and_Mary 2020

Beteel N. Abu-Ageel¹, Avery C. Bradley¹, Matt S. Dennen¹, Riya Garg¹, Min Guo¹, Josh R. Hughes¹, Adam J. Oliver¹, Julia A. Urban¹, Wei Wang¹, Hantao Yu¹, Eric L. Bradley², Mainak J. Patel³, Margaret S. Saha§

¹iGEM Student Team Member, ²iGEM Team Secondary PI, Department of Applied Science, ³iGEM Team Secondary PI, Department of Mathematics, §iGEM Team Primary PI, Department of Biology

College of William and Mary

Williamsburg, Virginia 23185, United States


The COVID-19 pandemic has emphasized the urgent need for broad-spectrum antiviral therapies. To address this need, we have 1) designed an antiviral nasal probiotic and 2) investigated its feasibility through extensive mathematical modeling. The designed probiotic secretes polyunsaturated fatty acids (PUFAs), which may lyse viral envelopes and suppress replication by positive strand RNA viruses, in addition to regulating inflammation. Our “smart” probiotic is designed to sense excessive inflammation by detecting high levels of TNF-alpha and IFN-gamma, and to respond by switching PUFA production from arachidonic acid to anti-inflammatory docosahexaenoic acid. To determine our probiotic’s feasibility, our mathematical model quantifies the amount of PUFA produced by the probiotic, and how secreted PUFA affects viral load and cytokine production. This complex model extends beyond current probiotic models by accounting for spatial heterogeneity and transcriptional stochasticity. With our novel design and rigorous modeling, TheraPUFA provides a framework for implementing smart, living antiviral therapies.

Introduction and Goals

Our project contains two goals:
  1. Design a smart nasal probiotic that secretes polyunsaturated fatty acids (PUFAs) to suppress viral infection and regulate inflammation
  2. Determine the feasibility of the probiotic through extensive mathematical modeling
    • If the probiotic is feasible with initial parameters, optimize parameters for effectiveness
    • If the probiotic is not feasible with initial parameters, identify parameters for which probiotic is feasible

Motivation & Inspiration

Our team desired to address a pressing global challenge. In early February, it became clear that no challenge was as urgent as the COVID-19 crisis.

COVID-19 has especially emphasized the need for broad spectrum antiviral therapies, which can serve as a form of pandemic preparedness.

To address this need, we designed a smart nasal probiotic that secretes PUFAs to suppress viral infection and regulate inflammation, and evaluated the probiotic’s feasibility using a complex mathematical model.

  • Why smart: Responsive to the various stages of infection and degrees of inflammation possible with viral infections such as COVID-19
  • Why nasal:
    • Nose as major entry point for respiratory viruses
    • “Dominant” site of early infection for SARS-CoV-2, based on high ACE-2 expression and infectivity observed (Hou et al., Cell, 2020)
    • BSL1, commensal bacteria native to region with probiotic potential
  • Why PUFAs:
    • Antiviral effects against enveloped and positive-strand RNA viruses
    • Regulate inflammation
  • Novel model accounts for spatial aspects and transcriptional stochasticity


Methodology: Circuit Design (Goal #1)

  1. Conducted general literature review for antiviral, nasal, “smart” probiotics
  2. Conducted targeted literature search for nasal probiotic chassis and consulted experts on chassis selection

  3. Conducted targeted literature search for circuit components in over 200 articles
    • Searched literature for PUFA synthesis genes that function at 37 degrees Celsius
    • Searched literature for PUFA and long chain fatty acid export systems
    • Searched literature for bacterial cytokine sensors
    • Searched literature for parts native to Neisseria species for design of kill switches after consulting experts on probiotic safety features
    • Searched literature for regulatory sequences endogenous to gram-negative bacteria and properly repressible to allow for switch in PUFA production

    Methodology: Mathematical Modeling (Goal #2)

    There are two main parts that constitute our model: modeling PUFA production and export ("production and export" part) on an individual cell level, and modeling TheraPUFA's effect on cytokine production and viral load in the human nasal cavity ("inside the body" part). The "inside the body" part of the model is dependent on the "production and export" part, and both parts combined are necessary for testing the feasibility of TheraPUFA.

    1. Created ODEs for PUFA production and export from single bacterial cell ("production and export") using Michaelis-Menten Rate Law
      • Conducted a literature search for parameters
      • Coded the equations using Python
    2. Investigated different types of models to quantify effect of TheraPUFA on cytokine production and viral load
      • Ordinary differential equation (ODE)-based models
      • Partial differential equation (PDE)-based models
      • Agent-based models
    3. Selected ODE-based model with spatial aspect in form of a grid
      • Faster computation time than agent-based and similar accuracy to PDE model
      • ODEs can be more directly applied to a grid
      • Used an adjusted target-cell limited model
      • Incorporated basic drug combination theory
    4. Conducted a literature search for over 30 parameters (see table in modeling section for selected parameters)
    5. Coded equations using Python
    6. Solved equations using the Euler method
    7. Combined “production and export” and “inside the body” parts of model
    8. Simulated the model over 200 times using Google Collaborate
      • Ran the model using original parameters (initially without spatial aspect)
      • Varied parameters one at a time
      • Formed a list of optimized parameters for final model design
      • Incorporated spatial aspect using a 10x10 grid
      • Incorporated transcriptional stochasticity through addition of Gaussian white noise

Results: Design

Animated Overview of Circuit Design

Looped animation of our circuit:

TheraPUFA is a smart nasal probiotic that uses 2 groups of BSL-1 N. Cinerea that differ only in their ability to produce either AA or DHA. Both groups have 2 kill switches each. TheraPUFA reacts intelligently and dynamically to varying cytokine concentrations in the body allowing for an antiviral response tailored to the immune response of the host.

Condition 1: Low Cytokine Concentration

  • Bacterium 1: AA constitutively produced. Absence of a cI repressor allows phospholipase cPLA2 expression which causes AA release.
  • Bacterium 2: DHA constitutively produced but not released in the absence of iPLA2 phospholipase.

Condition 2: High Cytokine Concentration

  • Extreme inflammation activates the cytokine sensor.
  • Bacterium 1: pspA promoter activated and cI expressed. cI represses cPLA2 production and stops AA secretion.
  • Bacterium 2: pspA promoter activated and phospholipase iPLA2 expressed. DHA secretion initiated.
  • When extracellular cytokine concentrations drop below the sensors threshold, DHA secretion is halted and AA secretion is resumed.

Design of Circuit Modules

Module 1: High Pass Cytokine Sensor

  • To prevent extreme inflammation, our circuit switches from producing AA to producing DHA. Using a synthetic sensor created by Aurand and March, concentrations of TNF-alpha and IFN-gamma above 150 pM and 200 pM, respectively, will trigger the production of DHA and inhibit AA.
  • Sensor

Module 2: PUFA Production

  • A Aurespira marina PUFA synthase will produce AA.
  • A Schizochytrium PUFA synthase will make DHA.

Module 3: PUFA Export

  • Our circuit uses the PUFA export system designed by Tong et. al.
  • To modify the system for DHA export, an acyl-CoA synthetase will direct DHA to be used in phospholipid synthesis and a phospholipase will then release DHA from phospholipids.
  • AA does not appear to require additional engineering for incorporation into phospholipids, so it is unnecessary to introduce an acyl-CoA synthetase.
  • DHA Export

    AA Export

Module 4: Safety and Kill Switches

Incorporation of Safety in Overall Circuit Design

  • We chose the PUFAs DHA and AA because these lipids have been demonstrated as safe, antiviral compounds.
  • Additionally we alternate the produced PUFA to ensure a safe level of inflammatory response.

Choice of our Probiotic Chassis

    Image of N. Lactamica, a close relative of N. cinerea. Attribution: Pandey, A.K., Cleary, D.W., Laver, J.R. et al., CC BY-SA 4.0 via Wikimedia Commons
  • We found that Neisseria cinerea is a good choice for a probiotic chassis
    • N. cinerea is a commensal member of the nasal and oral microbiota of healthy adults and children (Custodio et al., 2020)
    • N. cinerea is a BSL1 organism, and is not known to be associated with disease (Custodio et al., 2020)
    • Neisseria species have been used as nasopharyngeal probiotics. N. lactamica Y92-1009 has been used in human clinical trials, with no serious side effects (Deasy et al., 2015)

In-Body Kill Switch

  • Kill switch would allow rapid elimination of our probiotic
  • Uses the Psal / SalR Aspirin-inducible promoter system characterized by Chen et al., 2019.
  • Administration of Aspirin would activate expression from the Psal promoter, producing the zeta toxin ngζ_1.

Environmental Kill Switch

  • Consulting with experts, we realized that TheraPUFA could escape into the environment.
  • To prevent environmental disruption, we designed a kill switch that activates at a temperature < 37C
  • Utilizes the FitAB toxin-antitoxin system (Mattison et al., 2006), combined with the RNA thermometer CssA (Barnwal et al., 2016).
  • FitB (the toxin) is constitutively transcribed and translated
  • FitA (the antitoxin) is constitutively transcribed, but only translated above 37C
  • Therefore, our probiotic will die if it escapes into the environment.

Results: Model

Goals of Model and Overview of Results

We aim to answer three questions concerning the feasibility of TheraPUFA using our model:
  1. Does the model indicate feasibility of TheraPUFA using initial parameters from literature?
    • Answer: No. The effect of TheraPUFA in terms of decreasing peak viral load is insignificant.
  2. Does the model indicates feasibility of TheraPUFA after optimizing parameters?
    • Answer: Yes. Simulation results show TheraPUFA is able to decrease peak viral load from 10 to 8 orders of magnitude.
  3. Does the model still indicates feasibility of TheraPUFA after incorporation of a spatial aspect and transcriptional stochasticity?
    • Answer: Yes.

Description of Model

  • Adapted the target cell limited model (Zitzmann & Kaderali, 2018) to describe the antiviral effect of PUFA on cells in the nose
  • Modeled using a system of ODEs
  • S represents the number of uninfected cells (susceptible), I represents the number of infected cells, V is the concentration of virus, and T & F represent the concentration of TNF-alpha and IFN-gamma
  • The arrows represent interactions of these groups during viral infection. Their meaning is summarized in our parameter table below

The following three sections show how we applied the model to answer the three questions.

Initial Results With Parameters From the Literature

Does the model indicate feasibility of TheraPUFA using initial parameters from literature?

  • We picked one representative dimension of results, peak viral load, as an interpretation of whether the model is feasible or not
  • Peak viral load w/o PUFA: 1.73e10
  • Decrease of peak viral load after the incorporation of probiotic: 2.4e5, which is safe to be concluded as insignificant

Parameter Optimization

Does the model indicates feasibility of TheraPUFA after optimizing parameters?

Since the initial result did not indicate feasibility, we identified parameters with evidence to be varied biologically to modify for feasibility. In each of the figures below, we varied one parameter at a time and examined its effect on peak viral load difference between the scenario with and without PUFA.

Examples of Parameter Analysis

  • Observation:The effect on peak viral load increases at an increasing rate as replenish interval is decreased. This suggests lowest replenish interval that is possible.

  • Observation:The effect on peak viral load difference increases as the probiotic clearance rate is decreased. This indicates that we should aim to make the probiotic clearance rate as low as possible. Biologically this can be achieved via mucoadhesive gel.

  • Observation:There is a more significant effect on viral load before 20 folds increase of the original value of AA production rate. Biologically this can be achieved through FadE knockout and manipulation of acyl carrier proteins (ACP).

    Adjusted Parameters

    We optimized parameters based on observations of simulation results.

    Simulation Result Based on Adjusted Parameters

    The adjustment results in a significant decrease of peak viral load from 1.7e10 to 8.7e8 RNA copies/mL, which indicates feasibility.

    Stochasticity and Spatial Environment

    Does the model still indicates feasibility of TheraPUFA after incorporation of a spatial aspect and transcriptional stochasticity?


  • Gaussian white noise added to each equation involved in the synthesis and export of PUFA
  • Our equations reflect the stochasticity involved in transcription in bacteria
  • Our probiotic was found to be effective even with stochasticity incorporated

Spatial aspects

  • Our ODEs were implemented on a 10X10 grid
  • Diffusion was modeled based on the concentration of the neighboring cells
  • Incorporated heterogeneity of viral load
  • The difference in concentration between a cell and its neighboring cells was multiplied by a diffusion coefficient to obtain the rate of diffusion
  • Our probiotic was found to still be effective with spatial aspects incorporated

Modified Equations with Spatial Aspects

Overall Model Conclusion

We modeled via ODEs our probiotic with parameters from the literature and found it ineffective. We varied parameters and found our probiotic effective with certain changes. With the varied parameters our probiotic is robust against stochasticity and a spatial environment.

Education and Public Engagement

Open Labs (Pre-Pandemic)

  • Continued annual tradition of open labs for high school students
  • Hosted before the COVID-19 pandemic affected our area!
  • Introduced techniques used in synthetic biology labs

Virtual Presentations

  • We gave virtual presentations to:
    • William & Mary Alumni
    • Rising first generation college students in the PLUS-S program
    • Middle school students from low income backgrounds participating in camp launch

TikTok Video Series

  • Created a six-part video series where we:
    • Talked about synthetic biology and how its applications relate to the COVID-19 pandemic
    • Introduced our team and project
    • Discussed our work on viral recombination
    • Follow us at @igem_wm

Modeling for the Masses

  • 4-part video series for general audiences
  • Introduces modeling concepts
  • Seeks to resolve the confusion that surrounds COVID modeling
  • Addresses the questions:
    • Why are COVID-19 models so contradictory?
    • Why were models wrong with their predictions?
    • How can we better understand results from models?

Integrated Human Practices

Integrated human practices played a fundamental role in our project this year, guiding our circuit design, safety considerations, proposed plan for implementation, and perspectives on accessibility and probiotic distribution.

Integrating Feedback into Design

Integrating Feedback into Safety Considerations and Proposed Implementation

Integrating Feedback into Accessibility Considerations

Excellence in Another Area

    In addition to exploring preventative treatments for future outbreaks, our team investigated ways of identifying possible spillovers. Viral recombination is critical to viral evolution. With accurate identification of recombination in viruses, synthetic biologists can create sensors to identify recombination. This is another powerful combination of bioinformatics and synthetic biology.

Identifying Recombination Breakpoints

  • Characterized the current ability of bioinformatic programs to identify recombination events
  • Focused on Recombination Detection Program (RDP) (Bertrand et. al 2016)
  • Offered advice for future researchers to optimize RDP
  • Created simulated genomes to evaluate RDP

Problems with Identifying Breakpoints

  • Parameter choices for RDP vary among papers
  • Single parameter changes give large variation
  • Different alignments give vastly different results
  • Below is the distribution of recombination breakpoints of the same Delta Coronaviruses using alignment programs Mafft (Katoh et al. 2013) and Muscle (Edgar 2004)
  • Note the difference in distribution

RDP Breakpoint Distribution Using Mafft

RDP Breakpoint Distribution using Muscle

Literature Review of RDP Analysis

  • There exists methods to retroactively check identified breakpoints
  • RDP often fails to identify recombination breakpoint location
  • RDP is prone to false positives

    RDPv4 home screen

Simulations to Evaluate RDP Performance

  • 4 different simulated genomes of Polio, Hepatitis C, HIV, and Influenza A
  • Used simulator NetRecodon (5.0.6) (Arenas, Posada 2010)
  • RDP breakpoint identification analyzed in true and false positives rates

Simulation Results

  • False positive to true positive rate around 1:1
  • Power increased with an increased number of recombination events
  • Accuracy and power increased with more recombination events
  • Lower false positive rate with a higher mutation rate and genetic diversity

Our Advice for Future Researchers

  • Parental strands and the daughter recombination should be aligned in isolation and rerun on RDP
  • Multiple alignment programs can be compared to find general patterns in recombination
  • Use multiple alignment programs and choose the one which gives the least number of identified breakpoints
  • A simulated genome may be used to test and calibrate the parameters in RDP


We succeeded in meeting our two goals:
  • Designed a smart nasal probiotic that secretes PUFAs to suppress viral infection and regulate inflammation
  • Determined the probiotic to be feasible through extensive mathematical modeling and identified the optimal parameters for an effective probiotic

  • Fulfillment of Medaling Requirements

    Bronze medal:

  • Competition deliverables: Wiki, Poster, Presentation Video, Project Promotion Video, and Judging Form
  • Attributions
  • Project description: motivation, inspiration, goals, and impact of COVID-19
  • Contribution:
    1. Characterization of parts pages
    2. Systems for PUFA synthesis and export
    3. Troubleshooting for circuit design and modeling
    4. Analysis of recombination detection programs

    Silver medal:

  • Engineering Success: design-build-test cycle in extensive detail (Found at as opposed to the usual page)
  • Collaboration:
    1. Video series with Purdue
    2. Podcast with Pittsburgh
    3. Synthetic biology informative video with TU Delft
    4. Hosted virtual MidAtlantic Meetup with 6 teams
  • Human Practices: interviewed stakeholders and experts to inform design
  • Proposed Implementation: Probiotic administration, safety elements, and testing (Found at as opposed to the usual page)

  • Gold medal:

  • Integrated Human Practices: in depth interviews with 10 experts and stakeholders and how their input changed our project direction
  • Project Modeling: to test the feasibility of our probiotic and improve upon the design
  • Proof of Concept: using mathematical modeling, since we did not have wet lab access (Found at as opposed to the usual page)
  • Partnership: Crash course YouTube video series with Purdue iGEM for molecular and synthetic biology
  • Science Communications:
    1. TikTok video series educating on synthetic biology
    2. Modeling for the Masses video series explaining COVID-19 models
  • Excellence in Another Area: in depth analysis of the current state of recombination detection programs and how to optimize the performance of Recombination Detection Program version 4

  • Special prizes:

  • Education:
    1. Conversations with student groups underrepresented in STEM or from low income backgrounds
    2. TikTok video series introducing our project and overviewing how synthetic biologists can counter COVID-19
    3. Modeling for the Masses to describe how COVID-19 models work and why predictions contradict
  • Integrated Human Practices:
    1. Interviewed 6 medical doctors and 4 drug/microbiome experts in an interactive cycle that continually changed the direction of our project
    2. Helped us select safe chasses, design smart circuits, develop probiotic administration and manufacturing, and learn about drug regulations and accessibility
  • Model:
    1. Modeled the synthesis and export of PUFA and added a sensor to respond to specific levels of TNF-α and IFN-γ
    2. Adapted the target cell limited model to represent viral infection inside the nose and modeled cytokine production along with effects of PUFA
    3. Incorporated antiviral effects of PUFA and converted our equations into a stochastic spatial grid for diffusion


    Funding: Thank you to Vice Provost Dennis Manos, Dean Maria Donoghue Velleca, Dean Teresa Longo, and Dr. Dan Cristol

    Researchers: Thank you to Dr. Jeong Lee, Dr. Tohru Dairi, Dr. Arnold Driessen, and Dr. James Shelhamer

    Interviews: Thank you to Dr. James Shelhamer, Dr. Cecilia Mikita, Dr. Anders Cervin, Dr. Alan Shikani, Dr. Ronald Turner, Dr. Matthias Kramer, Dr. Rachel Lappan, Ms. Lydia Mapstone, Ms. Karolyn Gale, and Dr. Shen Xiaokun

    Sponsors: Thank you to the iGEM Foundation, Integrated DNA Technologies, and Twist Bioscience


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