iGEM 2025 — Team virginia

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block page\_content %}

* [Week 1](#1)
* [Week 2](#2)
* [Week 3](#3)* [Week 4](#4)
  * [Week 5](#5)
  * [Week 6](#6)
  * [Week 7](#7)
  * [Week 8](#8)
  * [Week 9](#9)
  * [Week 10](#10)
  * [Week 12](#12)* [Week 13](#13)
    * [Week 14](#14)
    * [Week 15](#15)
    * [Week 16](#16)
    * [Week 17](#17)
    * [Week 18](#18)

#### Week 1

Week 1 was spent developing our initial approach and wetlab design.

#### Week 2

**Thursday 6/12/2025**

Selected TSS method for preparing competent cells

Prepared buffers for TSS method

#### Week 3

**Sunday 6/15/2025**

Prepped 3 overnight cultures of *E. coli* BW25113 (BW25113)

**Monday 6/16/2025**

Inoculated *E. coli* BW25113 into 1200 mL LB medium

Original plate with the *E. coli* BW25113 was re-streaked

Poured plates with kanamycin antibiotic

**Tuesday 6/17/2025**

SM-1 media fell out of solution

Prepared TSS buffer

Made glycerol stocks of *E. coli* BW25113

**Wednesday 6/18/2025**

Organized lab setup and labeled primary workstations.

Cleaned countertops and lab equipment

Ordered remaining reagents for SM-1 Media

Performed a transformation of *E. coli* BW25113 using the TSS method

**Thursday 6/19/2025**

Analyzed results from 6/18 Transformation

Performed a transformation of *E. coli* BW25113 using the TSS method with different ampicillin plates

**Friday 6/20/2025**

Analyzed results from 6/19 transformation.

Obtained new plates of *E. coli* MG1655 and *E. coli* JM109.

Re-streaked colonies of *E. coli* BW25113, *E. coli* MG1655 and *E. coli* JM109

**Saturday 6/21/2025**

Analyzed re-streak of *E. coli* BW25113, *E. coli* MG1655 and *E. coli* JM109

Concluded that the *E. coli* BW25113 was not *E. coli* BW25113

#### Week 4

**Monday 6/23/2025**

Sample transformation with *E. coli* JM109

**Tuesday 6/24/2025**

Analyzed results from 6/23 transformation

Transformed L-cysteine overexpression plasmid from Twist Bioscience (TV1) into *E. coli* JM109

**Wednesday 6/25/2025**

Analyzed results from 6/24 transformation.

PCR of L-cysteine overexpression plasmid.

Ran gel electrophoresis for the PCR product.

Colonies from 6/24 transformation were re-streaked on to new ampicillin plates.

**Thursday 6/26/2025**

Analyzed 6/25 re-streaked plates.

Prepared large volume of SM-1 media.

Transformation of TV1 into *E. coli* JM109

**Friday 6/27/2025**

Analyzed results from 6/26 transformation.

#### Week 5

**Sunday 6/29/2025**

Prepared overnight cultures of transformed *E. coli* JM109 containing TV1.

**Monday 6/30/2025**

Mini-prepped the E. *coli* JM109 from 6/29 overnight cultures.

Ran a restriction digest of TV1 with PstI and XbaI.

Prepared and froze stocks of from 6/29 overnight culture.

Ran a ninhydrin calibration curve.

Prepared 100X stock solution of L-cysteine-hydrochloride.

**Thursday 7/3/2025**

Additional 100X L-cysteine-hydrochloride stock prepared.

Prepared overnight cultures of transformed *E. coli* JM109 containing pUC19 and TV1.

#### Week 6

**Sunday 7/6/2025**

Transformed *E. coli* JM109 colonies with pUC19 and TV1.

**Monday 7/7/2025**

Checked on the 7/6 transformed *E. coli* JM109 cultures

Inoculated the transformed *E. coli* JM109 cultures into SM-1 media, with OD600 readings taken every hour.

Ran ninhydrin assay with calibration curve

Obtained a plate of *E. coli* BW25113 and re-streaked colonies onto a new plate.

**Tuesday 7/8/2025**

Inoculated *E. coli* JM109 containing pUC19 and TV1 into SM-1 media.

Prepared fresh ninhydrin reagent 2.

Analyzed re-streaked 7/7 *E. coli* BW25113 and prepared overnight cultures.

**Wednesday 7/9/2025**

Prepared fresh ninhydrin reagent 2

Mini-prepped 7/8 *E. coli* JM109 and sent for DNA sequencing.

Ran a restriction digest from mini-prepped DNA.

Ran a ninhydrin assay calibration curve.

Prepared fresh L-cysteine-hydrochloride stock solution.

**Thursday 7/10/2025**

Prepared fresh ninhydrin reagent 2

Ran ninhydrin assay

Changes to calibration curve methodology; made L-cysteine-SM-1 stock.

**Friday 7/11/2025**

Observed that 7/10 L-cysteine-SM-1 stock had fallen out of solution.

Made additional changes to calibration curve methodology, switching back to L-cysteine-hydrochloride stocks. Performed a calibration curve with the changes.

Performed various tests to refine the steps of the ninhydrin assay.

Analyzed sequencing data from Azenta.

Transformed TV1 and pUC19 into *E. coli* DH5α cells.

**Saturday 7/12/2025**

Analyzed results from 7/11 transformation.

Prepared overnight cultures from 7/11 transformation product.

#### Week 7

**Sunday 7/13/2025**

Glycerol stocks of *E. coli* DH5α containing TV1 from 7/11 overnight culture were prepared and stored in -80 °C freezer

Mini-prepped *E. coli* DH5α containing TV1 from 7/11 overnight culture.

Mini-prepped DNA was transformed into *E. coli* JM109 cells.

Ran more tests to optimize ninhydrin assay calibration curve to understand impact of each step.

**Monday 7/14/2025**

Restriction digest of 7/13 mini-prepped TV1 from DH5α cells

Second digest was run under the same conditions

Analyzed 7/13 *E. coli* JM109 transformation product

Prepared overnight cultures of pUC19 and TV1 from 7/13 *E. coli* JM109 transformation product

**Tuesday 7/15**

Overnight colonies from 7/14 were inoculated into SM-1 media

Prepared fresh ninhydrin reagent 2

Ran ninhydrin assay

Ran ninhydrin assay calibration curve

Mini-prepped overnight colonies from 7/14

**Wednesday 7/16**

Inoculated overnight cultures into SM-1 media

Prepared fresh ninhydrin reagent 2

Ran ninhydrin assay calibration curve

**Thursday 7/17**

TV1 DNA from *E. coli* DH5α (7/13) and *E. coli* JM109 (7/15) sent for Sanger sequencing

Prepared overnight cultures of *E. coli* JM109.

**Friday 7/18**

Performed linearization of pUC19 vector via restriction digest at XbaI site

Transformed TV1 into NEB® Turbo Competent E. coli (High Efficiency) (Turbo)

**Saturday 7/19**

Analyzed results from 7/18 transformation.

Prepped 8 overnights from the transformed *E. coli* DH5α plate of TV1

PCR and gel electrophoresis imaging of L-cysteine triggered kill switch plasmid from IDT (Insert 2)

Prepared overnight cultures of Turbo *E. coli* containing TV1.

#### Week 8

**Sunday 7/20**

Mini-prepped overnight cultures of Turbo *E. coli*

Purified 7/19 Insert 2 PCR product

Created a stock of competent *E. coli* MG1655 using the TSS method for competent cells. The stocks were stored in an –80oC freezer.

Transformed *E. coli* JM109 with TV1 from 7/20 miniprep.

Stocks of DH5α were prepared and stored.

Transformed Stellar competent *E. coli* cells with In-Fusion Insert 2 product.

**Monday 7/21**

Analyzed results from 7/20 *E. coli* JM109 TV1 transformation.

Analyzed results from 7/20 Stellar cells Insert 2 transformation

TV1 in Turbo cells (7/20) sent for sequencing through Azenta

Performed restriction digest of TV1 in Turbo cells with BbsI.

**Tuesday 7/22**

Performed In-Fusion assembly for Insert 2

Ran test live/dead assay for Insert 2

Prepared overnight cultures of Stellar cells containing Insert 2

**Wednesday 7/23**

Performed In-Fusion assembly for Insert 2

Ran live/dead assay for Insert 2

Collected cell lysates and started Western Blot

Transformation of TV1 into Turbo cells

Transformation of Insert 2 into Stellar cells

**Thursday 7/24**

Analyzed results from 7/23 Western Blot

Analyzed results from 7/23 TV1 transformation into Turbo cells.

Analyzed results from 7/23 Insert 2 transformation into Stellar cells.

**Saturday 7/26**

Overnight cultures prepared from 7/23 transformation products of both TV1 and Insert 2

#### Week 9

**Sunday 7/27**

Mini-prepped both TV1 and Insert 2 from 7/26 overnight cultures.

Transformed mini-prepped TV1 into *E. coli* JM109.

Transformed mini-prepped Insert 2 into *E. coli* MG1655.

**Monday 7/28**

Analyzed both 7/27 transformation results

Performed restriction digest of TV1 using BbsI

Performed restriction digest of Insert 2 using XbaI

Performed colony PCR on *E. coli* JM109 transformed with TV1.

Resuspended and transformed Anderson promoters from the iGEM 2025 Distribution Kit into *E. coli* DH5α.

Sent TV1 and Insert 2 DNA from 7/27 mini-prep for sequencing.

Prepared overnight cultures of *E. coli* MG1655 and Stellar cells, both transformed with Insert 2

**Tuesday 7/29**

Ran colony PCR products via gel electrophoresis.

Created a new method for validating Insert 2.

Ran cell viability assay and optical density readings with Stellar cells containing Insert 2.

Transformed resuspended Anderson Promoters J23102, J23105, J23109 from the iGEM 2025 Distribution Kit into Turbo cells

**Wednesday 7/30**

Officially confirmed via sequencing results that the DNA sent to us by Twist Bioscience was incorrect, as it contained no homology to our intended insert.

Prepared overnight cultures of *E. coli* MG1655 containing Insert 2.

Prepared competent *E. coli* MG1655 cells using the TSS method.

**Thursday 7/31**

Mini-prepped from 7/30 overnight *E. coli* MG1655 Insert 2 culture.

Digested mini-prepped DNA with XbaI.

Sent mini-prepped Insert 2 for sequencing.

Test transformation of *E. coli* MG1655 competent cells from 7/30 with pUC19.

Prepared overnight cultures of Stellar cells transformed with Insert 2.

Prepared overnight cultures of Turbo cells transformed with Anderson promoters.

**Friday 8/1**

Mini-prepped from 7/31 overnight Stellar cell Insert 2 cultures

Digested mini-prepped DNA with XbaI

Sent mini-prepped Insert 2 for sequencing.

Transformed DNA from selected colonies into *E. coli* MG1655 based on digest results.

**Saturday 8/2**

Analyzed results from 8/1 transformation

Prepared overnight cultures of *E. coli* MG1655 from 8/1 transformation.

#### Week 10

**Sunday 8/3**

Insert 2 Assay including cell viability and optical density readings from 8/2 *E. coli* MG1655 overnight cultures was performed.

Prepared overnight cultures of *E. coli* MG1655 from 8/1 transformation.

**Monday 8/4**

Ran Insert 2 bacterial viability assay

Performed In-Fusion assembly for Insert 2

Prepared overnight cultures of *E. coli* containing Insert 2

**Tuesday 8/5**

Transformed 8/4 In-Fusion product into Turbo competent cells.

Performed bacterial viability assay calibration curve.

Prepared overnight cultures of Turbo cells containing Insert 2

**Wednesday 8/6**

Mini-prepped DNA from 8/5 overnight cultures containing Insert 2.

Performed restriction digest on mini-prepped Insert 2 DNA using XbaI.

Sent mini-prepped DNA for sequencing based on digest results.

#### Week 12

**Monday 8/18**

Received *E. coli* CcdB survival cells (CcdB survival).

Performed restriction digest of the L-cysteine overexpression plasmid from ANSA Biotechnologies (Insert 1).

PCR of Insert 1 and linearized pACYC to add homologous ends.

**Tuesday 8/19**

Performed restriction digest of Insert 2 DNA from 8/1 and 8/6 mini-preps using BbsI and BtgI.

Performed ligation of Insert 2 using 8/19 digest product.

Transformed ligation product into CcdB survival cells.

**Wednesday 8/20**

pACYC from GoldBio was transformed into Turbo competent cells.

Analyzed results from 8/19 CcdB survival cell transformation with Insert 2 ligation product.

Prepared overnight cultures from 8/19 CcdB survival cell transformation with Insert 2 ligation product.

**Thursday 8/21**

Analyzed results from 8/20 Turbo cell transformation with pACYC.

Mini-prepped 8/20 CcdB survival overnight cultures containing Insert 2.

Performed restriction digest of mini-prepped Insert 2 DNA using BbsI and BtgI.

Sent mini-prepped Insert 2 DNA for sequencing based on digest results.

Prepared overnight cultures of 8/20 Turbo cell transformation with pACYC.

**Friday 8/22**

Mini-prepped 8/21 Turbo cell overnight cultures containing pACYC.

Performed PCR of Insert 1.

Prepared overnight cultures of 8/20 Turbo cell transformation with pACYC.

Analyzed sequence results from 8/21 mini-prep.

**Saturday 8/23**

Mini-prepped 8/22 Turbo cell overnight cultures containing pACYC.

Performed restriction digest of mini-prepped pACYC DNA using XbaI.

Performed restriction digest of Insert 2 from 8/1 and 8/6 mini-preps using BbsI and BtgI.

#### Week 13

**Wednesday 8/27**

Performed restriction digest of Insert 2 DNA from 8/1 and 8/6 mini-preps using BbsI and BtgI.

Performed gel extraction of restriction digest product.

Performed ligation of Insert 2 using 8/27 digest product.

Transformed ligation product into CcdB survival cells.

**Thursday 8/28**

Analyzed results from 8/27 transformation.

**Friday 8/29**

Performed restriction digest of Insert 2 DNA from 8/1 and 8/20 mini-preps using BbsI and BtgI.

Performed ligation of Insert 2 using 8/29 digest product.

Performed In-Fusion assembly of Insert 1.

**Saturday 8/30**

Transformed 8/29 In-Fusion product into Stellar cells.

Transformed 8/29 ligation product into CcdB survival cells.

#### Week 14

**Sunday 8/31**

Analyzed results from 8/30 transformations.

**Monday 9/1**

Secondary analysis of results from 8/30 transformations.

Transformed 8/29 In-Fusion product into Stellar cells.

Transformed 8/29 ligation product into CcdB survival cells.

**Tuesday 9/2**

Analyzed results from 9/1 transformations.

Transformed 8/29 In-Fusion product into Stellar cells.

Prepared overnight cultures of 9/2 Stellar cell transformation with Insert 1 and pACYC.

**Wednesday 9/3**

Mini-prepped 9/2 Stellar cell overnight cultures containing Insert 1 and pACYC.

Performed restriction digest of Insert 1 and pACYC DNA from 9/3 mini-prep using EcoRI-HF.

Sent mini-prepped Insert 1 DNA for sequencing based on digest results.

**Thursday 9/4**

Prepared overnight cultures of 9/2 Stellar cell transformation with Insert 1 and pACYC.

**Friday 9/5**

Mini-prepped 9/4 Stellar cell overnight cultures containing Insert 1 and pACYC.

Performed PCR for both Insert 1 in pIND backbone and Insert 2.

Prepared overnight cultures of pACYC (in turbo cells, 8/20) and pUC19 (in CcdB survival, 8/19) for use in digest experiment and vector linearization.

**Saturday 9/6**

Mini-prepped overnights cultures of CcdB survival cells and Turbo cells with empty vector.

Performed restriction digest of pUC19 using XbaI.

Performed restriction digest of pACYC using EcoRI.

#### Week 15

**Sunday 9/7**

Ran products from 9/6 digests via gel electrophoresis.

Ran products of 9/5 PCR of Insert I and Insert 2 via gel electrophoresis and analyzed results.

Performed PCR for both Insert 1 in pIND backbone and Insert 2.

**Monday 9/8**

Ran products of 9/7 PCR of Insert I and Insert 2 via gel electrophoresis and analyzed results.

**Wednesday 9/10**

Performed PCR of Insert 1 in pIND backbone.

Performed restriction digest of pUC19 and pACYC using XbaI.

Performed second restriction digest of pUC19 and pACYC using XbaI and fresh nuclease-free water.

Performed gel extraction of PCR product and pUC19& pACYC digest product.

Performed In-Fusion assembly of Insert 1 and Insert 2

Transformed Insert 1 into Stellar and Insert 2 into CcdB survival cells using 9/10 In-Fusion products.

**Thursday 9/11**

Analyzed results from 9/10 transformations.

**Saturday 9/13**

Secondary analysis of 9/10 transformation product.

Transformed Insert 1 into Stellar and Insert 2 into CcdB survival cells using 9/10 In-Fusion products.

#### Week 16

**Sunday 9/14**

Analyzed results from 9/13 transformations.

**Monday 9/15**

Performed colony PCR of In-Fusion transformed plates.

**Tuesday 9/16**

Prepared overnight culture of Stellar cells containing Insert 1.

**Wednesday 9/17**

Mini-prepped 9/16 Stellar cell overnight cultures containing Insert 1.

Performed restriction digest of pUC19 and pACYC using XbaI and Calf Intestinal Phosphatase (CIP).

**Thursday 9/18**

Performed gel extraction for pUC19 and pACYC 9/17 digest product.

#### Week 17

**Sunday 9/21**

Transformation of linearized pUC19 and pACYC into Stellar cells using 9/18 gel extraction product.

**Monday 9/22**

Analyzed results from 9/21 transformation.

Performed In-Fusion assembly of Insert 1 and Insert 2.

Transformed Insert 1 into Stellar and Insert 2 into CcdB survival cells using 9/22 In-Fusion products.

Transformed linearized pUC19 and pACYC into Stellar cells

**Tuesday 9/23**

Analyzed results from 9/22 transformation.

Prepared overnight cultures of 9/22 Stellar cell transformation with Insert 1.

**Wednesday 9/24**

Mini-prepped 9/23 Stellar cell overnight cultures containing Insert 1.

Performed digest of Insert 1 using XbaI.

Transformed Insert 2 into CcdB survival cells.

**Thursday 9/25**

Prepared overnight cultures of 9/24 CcdB survival cell transformation with Insert 2, Insert 2 negative control and Insert 1 negative control.

**Friday 9/26**

Mini-prepped 9/25 overnight cultures.

Performed digest of Insert 2 DNA using BbsI and BtgI.

#### Week 18

**Sunday 9/28**

Transformed Insert 1 DNA from ANSA into Turbo cells.

**Monday 9/29**

Re-streaked 9/28 transformation product onto a new plate.

**Tuesday 9/30**

Prepared overnight cultures from 9/29 re-streaked plate of Turbo cells containing Insert 1.

**Wednesday 10/1**

Cloned Insert 1 and Insert 2 into pUC19 via traditional restriction cloning to reduce background and utilize blue-white colony screening.

Mini-prepped 9/30 overnight cultures.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block page\_content %}

* [Introduction](#intro)
* [Engineering the Problem](#problem)
* [Goal 1: Validate Device Functionality](#goal1)
* [Goal 2: Understand Device Implementation](#goal2)
* [Goal 3: Evaluate Device Utility](#goal3)

## Introduction

Scroll down to learn about how we used the engineering cycle to:

1. Engineering the Problem
2. Goal 1: Validate Device Functionality
3. Goal 2: Understand Device Implementation
4. Goal 3: Evaluate Device Utility

## Engineering the Problem

**Introduction**

Responsible engineering requires a comprehensive understanding of the problem being addressed and how it fits into broader goals. Our team decided to tackle climate change and decided that addressing wastewater treatments would be the modality in which a biological device would serve the most utility. Wastewater expenditures in the U.S. total nearly one-trillion dollars, mostly due their extensive energy demand. We started by looking into biological systems that could serve to decrease this energy demand.

**Electricity and Microbes**

Our first thought was Microbial Fuel-Cells (MFCs). MFCs produce electricity by catalyzing the release of electrons from energy rich bonds of organic substrates under anoxic conditions [1]. Under non-anoxic conditions the efficiency of MFCs is severely degraded, resulting in a system that is not commercially viable [1]. To synthetically produce anoxic conditions in MFCs, high concentrations of L-cysteine are used [2]. In large scale reactors this becomes a limiting expense and creates excessive start-up costs [3].

To address this shortfall, we proposed a biological device able to supplement the medium of MFCs with 100 mg/L of L-cysteine before undergoing cell lysis. Cell lysis was deemed necessary to not upset the microbial balance in MFCs and ensure maximum efficiency. A literature review revealed E. coli K-12 strains as the most engineered for biological L-cysteine production, and corresponding genetic circuitry was designed.

Engaging with MFC experts made us realize that there were fundamental issues with MFCs, namely that theoretical yields were not high enough for market viability. For that reason, stakeholders like Dr. Daniel Bond (read more about our conversation with him on our IHP page) encouraged us to look towards Microbial-Electrolysis Cells (MECs). MECs take organic material and produce H2, which can then be transformed into energy.

However, MECs face two major challenges. Firstly, methanogens often overtake these systems resulting in more methane production than hydrogen production. Because methane is a greenhouse gas, and hydrogen production is suppressed, this impacts the commercial viability of the system. Secondly, MECs only work at scale when fed with acetates. This severely limits its practicality.

**The Road to Dark Fermentation**

We learned from Professor Sovik Das about how he uses Dark Fermentation (DF) with MECs to increase hydrogen yields. DF is a light-independent process in which anaerobic bacteria convert glucose into H2 and acetate [4]. DF commonly utilizes organic waste feedstocks and can be waste-to energy (WTE) streams. This allows a combination DF-MEC system that can both increase hydrogen yields and make the system more commercially viable. However, as Professor Das discovered, this did not tackle the methanogens.

To deal with methanogens and other hydrogen-consuming bacteria (HCB) during DF, Professor Das uses a variety of feedstock pre-treatments (pH, chemical, temperature, loading shock, etc.). Even still he faces difficulties with propagating hydrogen-producing bacteria (HPB). Literature reviews revealed that oxidation due to high oxidation-reduction potential (ORP) environments inhibit hydrogenase activity and HPB propagation [5].

Our first device construct was built to address excess oxidation, and with a detailed literature review we revised our original construct. Our new device, the Cysteinator, is a biological device that can supplement dark fermentation with L-cysteine up to a concentration of 300 mg/L and then undergo cell lysis. The revised concentration reflects the optimal oxidation-reduction potential, and cell lysis remains essential as to not have our device become a metabolic competitor to HPBs.

**Defining What the Cysteinator Will Do**

While we defined what the Cysteinator does, we also thought that it would be necessary to define how it would be implemented into DF process flows. Figure 1 shows the four parts of the DF process flow, and how they interact. As the Cysteinator is aimed to help propagate HPBs and increase hydrogenase activity, we found that its implementation best fits in Stage 1 (feedstock to inoculum conversion).

**Figure 1**

![DF Process Flow](https://gitlab.igem.org/2025/virginia/-/raw/272b0277f566aa5831a3eb5dccaee6b2e5613f50/images/Project_Description_Figure_2_-_Final.png)

**Figure 1**
shows the four stages in the DF process flow. Stage 1: Inoculum Preparation consists of the preparation of feedstocks with various pretreatments. This process converts the HPBs in feedstocks into bacterial inoculum where HPBs propagate and are ready to be introduced into the bioreactor. Stage 2: Substrate Preparation consists of the hydrolysis of substrates to convert organic wastes to H2 precursors (eg. glucose) and mechanical shredding to increase surface area. Stage 3: Dark Fermentation consists of the combination of the inoculum and prepared substrate to initiate DF. At this stage the HPBs in the inoculum convert glucose and other sugars to H2 and VFAs. Stage 4: Downstream Processing consists of VFAs being used as precursors in photo-fermentation, microbial electrolysis cells, and other systems to produce more H2 among other useful byproducts.

**Stage 1 is where the greatest barrier to market viability for DF and its conjugate systems lies and is where the Cysteinator would be the most impactful.** Feedstocks in Stage 1 range from different wastewaters to solid wastes and its resultant inoculums are affected by bioreactor type, climate, and specific process flows among many other factors. Increasing inoculum quality means that the Cysteinator must 1) be integrated into the Stage 1 Process Flow (see Figure 2) and 2) be modular to adapt to the diversity of these processes.

**Figure 2**

![DF Stage 1 Process Flow](https://gitlab.igem.org/2025/virginia/-/raw/272b0277f566aa5831a3eb5dccaee6b2e5613f50/images/Engineering_Figure_2.png)

**Figure 2**
shows the process flow of Stage 1: Inoculum Preparation. The feedstock is pretreated to enhance the propagation of HPBs while simultaneously inhibiting the propagation of Hydrogen-Consuming Bacteria and Metabolic Competitors. Once the feedstock is pretreated it becomes inoculum which is then transferred into the DF bioreactor to start the process of DF.

Stakeholder feedback led us to realize that we needed to prevent horizontal gene transfer between our device and native bacteria. Our modified process flow (see Figure 3) does this in two ways 1) we introduce the Cysteinator after pretreatment to minimize exposure to different bacterial strains, and 2) we advocate that our modified process flow includes a heat treatment step after Stage 3 (DF) to prevent any accidental release of the device and bacteria that may be carrying our engineered circuitry.

**Figure 3**

![Modified DF Stage 1 Process Flow](https://gitlab.igem.org/2025/virginia/-/raw/272b0277f566aa5831a3eb5dccaee6b2e5613f50/images/Engineering_FIgure_3.png)

**Figure 3**
shows how the Cysteinator will be integrated into Stage 1 of the DF process flow. It will be introduced after the pretreatment of the Feedstock to reduce the oxidation-reduction potential (ORP) of the Feedstock as it becomes inoculum.

**Bringing it Together**

In summary our project helps make H2 energy a commercially viable option. By sustainably enhancing the efficiency of DF-originated H2 production, our device will help accelerate the global transition to sustainable energy. By supplementing DF feedstocks with precisely 300 mg/L of L-cysteine, the Cysteinator will be the key to making DF-based WTE streams a viable future energy stream.

## Goal 1: Validate Device Functionality

The objective of this first goal is to build and test a modular device that can supplement DF feedstocks with 300 mg/L of L-cysteine. Along with the device we determined that a comprehensive model able to simulate device behavior would be beneficial for the implementation of the device. The below sections detail the Design-Build-Test-Learn cycles of our device and its accompanying model.

***Design***

We decided to segment the functionality of the Cysteinator into **two plasmid components**: (1) the L-cysteine overproduction plasmid and (2) an L-cysteine regulated kill-switch. This segmentation allows us to more effectively build circuitry and fine-tune L-cysteine overproduction and export. Modularity was built into every aspect of this project, synthesizing both parts with flanking restriction enzyme sites around every gene segment, allowing for ease of swapping between mutants in future designs, to optimize function in different bioreactor systems.

Our goal was to design a bio-based device that could induce a large change in L-cysteine concentration in a medium yet simple enough to leave cell fitness relatively unaffected. It was shown that bioreactor systems functioned optimally within a range of 300-500 mg/L of added L-cysteine, and that exceeding this range would work against the efficiency of the system [6]. Thus, our overexpression plasmid is designed to produce up to 300 mg/L of L-cysteine.

Because of this constraint, we also wanted our device to stop functioning before it adds too much L-cysteine to the medium. Thus, another target was to design a kill-switch mechanism that would eliminate our device from bioreactor systems. This design prevents the Cysteinator from overstepping the threshold and from outcompeting the dark fermentative bacteria themselves. Thus, a dual-plasmid system was proposed, with each device-bearing plasmid tested individually before co-transformation into our final chassis, BW25113, which is known to be optimal for L-cysteine overproduction using plasmids similar to our own [7]. In the next section, we explain why the two plasmid system is superior to the current state-of-the-art.

***L-cysteine Overproduction Plasmid***

The L-cysteine overproduction plasmid is designed to accomplish the overproduction and export of L-cysteine into the medium. The native mode of expression and export of L-cysteine in E. coli functions through a series of complex but well-studied metabolic pathways. There is a significant body of previous work to manipulate and optimize the metabolic production of L-cysteine, which our team drew on to design a plasmid with high L-cysteine yields and low complexity [8]. This combination is important, as it has been found that high L-cysteine yields often come at the expense of cell fitness [7]. Our final plasmid design manipulates cysE, serA, and eamB—three key genes in the native enzymatic pathways of E. coli cysteine production and export [8]. While to our knowledge these genes and their variants have never been used in the same construct, they have all been well studied and used individually in various designs for producing L-cysteine. Our team placed mutant versions of these genes under the control of strong constitutive promoters to build the cysteine overexpression plasmid.

The cysE gene encodes serine O-acetyltransferase, which catalyzes the rate-limiting step of the synthesis of L-cysteine from L-serine in E. coli [8]. This enzyme in its native form is strongly feedback-inhibited by L-cysteine, which posed an issue for our design. Luckily, this problem has been tackled by metabolic engineers. Scientists researching the industrial overproduction of L-cysteine have identified several cysE mutants that are insensitive to feedback inhibition, one of which was even used by a past iGEM team [9, 10]. We decided to use the recently identified A237V mutant, which was used in a chassis optimized for L-cysteine overproduction to produce an L-cysteine concentration of 234.9 mg/L, which was 2.67 times higher than the basal L-cysteine concentration achieved by the optimized strain (88.1 mg/L) [11].

The serA gene encodes phosphoglycerate dehydrogenase, which catalyzes the rate- limiting step of the synthesis of L-serine from 3-phosphoglycerate [8]. This gene is critical to achieve L-cysteine overproduction because it ensures that adequate levels of the precursor, L-serine, exist in the cell. Similar to cysE, native serA is feedback-inhibited by L-serine. Again, mutants have been identified which greatly reduce this feedback-inhibition. Upon reviewing literature detailing these mutants, our team encountered some confusion: a commonly cited T410Stop mutation does not align with the E. coli serA gene, which contains a tyrosine residue at position 410 and only contains 411 residues [7]. We believe this is instead the serA gene native to C. glutamicum, but could not confirm this. We instead decided to use a triplet of mutations, H344A/N346A/N364A, which were shown to entirely remove the ability of phosphoglycerate dehydrogenase to bind serine in E. coli [12].

The EamB gene encodes the EamB inner-membrane transporter formerly known as YfiK, which exports L-cysteine and O-acetylserine in E. coli [13]. This prevents the toxic accumulation of L-cysteine within the cell and will help to avoid the induction of L-cysteine degradation pathways. Our team considered several options for transporters to overexpress, namely a similar inner-membrane exporter EamA (formerly YdeD) and an outer-membrane exporter TolC [8]. Ultimately, we chose to overexpress EamB due to the fact that a mutant version of the gene was shown to increase L-cysteine yields of an overexpressing strain by up to 70% [14]. We implemented this mutation in our design: N157S/G156S.

We considered size constraints while designing this plasmid, as past Virginia iGEM teams have had severe issues with assembly of large parts. We considered entirely removing genes like serA or EamB from the circuit, which would decrease L-cysteine output but still likely result in a functional device while also reducing the metabolic strain. Ultimately, the three-gene system of serA, cysE, and eamB was small enough that all three genes could be retained in a 3428 base pair insert.

We also considered alternative methods for overexpressing L-cysteine. Many strains engineered to overexpress L-cysteine include knockouts of key L-cysteine degradation pathway genes such as TnaA and YhaM to achieve much higher rates of L-cysteine overexpression [7, 8, 11]. For instance, one construct using gene knockouts as well as serine acetyltransferase mutants was able to achieve an L-cysteine concentration of 621 mg/L in the medium [15]. Future iterations of our device could include elements such as these knockouts to enhance L-cysteine production.

***Cysteine Overexpression Plasmid Timeline:***

***Build*  (1)**

To save time, we ordered the first build of our cysteine overexpression plasmid ready to transform in the pTwist Amp medium-copy vector from Twist Biosciences. Unfortunately, multiple transformations of this vector in 2 different lab spaces and by multiple people failed, yielding instead a 7736 base pair plasmid with no sequence homology to our insert (except in the antibiotic resistance gene). We believe this plasmid was introduced somewhere in the commercial assembly pipeline.

***Build*  (2)**

We then tried ordering the cysteine overexpression genes as a single gBlock from IDT, with the plan to clone the insert into a low-copy pACYC plasmid. However, IDT spent several weeks trying to assemble the insert before canceling the order due to a failed assembly. This was likely due to the length and high complexity of the sequence (although the complexity was within the parameters for assembly).

***Build*  (3)**

After speaking with a representative from IDT, we codon-optimized the coding regions to greatly reduce the complexity of the insert and ordered again. Again, the assembly failed and the order was canceled.

***Build*  (4)**

Finally, we ordered our insert from Ansa Biotechnologies after being awarded their grant for challenging sequence assembly. They assembled our insert in a large plasmid (pIND) designed for challenging sequences. We amplified this insert and added homologous overhangs via PCR and attempted to clone it into a low-copy pACYC plasmid via In-Fusion assembly. Unfortunately, several ligation attempts yielded high numbers of colonies containing unmodified vector, and a colony PCR was unable to identify any colonies with a successfully assembled construct.

***Build*  (6)**

We then attempted to clone our insert via traditional restriction cloning, using cut sites already present in our insert. We performed this assembly with 19, a high-copy plasmid we had on-hand that enabled blue-white colony screening. This, finally, yielded a large number of colonies containing our insert, which we verified by sequencing.

**Test**

To assay cysteine overexpression, we used a commonly cited protocol known as a ninhydrin assay to screen for amino acid concentrations[16]. Ninhydrin, a yellow crystal, interacts with the free alpha-amino group contained in amino acids through a temperature-induced reaction, and results in the formation of a brilliant deep pink/purple color compound known as Ruhemann's purple [16]. Absorbances are read at 560 nm to quantify the amount of intra/extracellular cysteine present against a blank. Similar protocols have been demonstrated and adapted from prior papers modifying the Gaitonde protocol, and additionally from iGEM teams in past years, such as the iGEM Trento 2012 team [17, 18].

Inoculation and growth conditions were adapted from previous studies that quantified cysteine outputs [14, 15]. SM1 is a minimal media with prior validation as an optimal medium for growth of cysteine-producing chassis, although M9 media was considered as well [13, 7]. The device was cultured in 50 mL of SM-1 in 250 mL Erlenmeyer baffled shake flasks with a thiosulfate additive acting as a sulfur source for L-cysteine synthesis [14].

The figure above demonstrates a ninhydrin assay performed in our lab with varied cysteine concentrations, functioning as a calibration curve for spectrophotometric readings with standard concentrations. This calibration curve demonstrated is qualitatively of weaker coloration than spectrophotometric readings taken, as the pigment is prone to degradation. Optimizing the lack of degradation has been a focus of our lab as well. Future iGEM teams should know that the sensitivity of this assay is greatly enhanced by prepping new Ninhydrin Reagent 2 fresh with every iteration of the assay and minimizing time spent between water bath/ice bucket, along with ethanol addition to just-boiled sample tubes, to reduce degradation (see Experiments page for more details).

Optical densities of our bacteria were measured every 1-2 hours at 600 nm to obtain an approximation of the growth rate and examples of metabolic strain on our chassis, as well as primarily functioning to inform our model. In this way, we can create an understanding of the way in which our device functioned as well as how best to modify our designs for optimal function in future iterations.

***L-cysteine Regulated Kill-Switch***

Our L-cysteine regulated kill switch is designed to induce cell death once enough L-cysteine has been exported into the medium. This is accomplished with a plasmid consisting of three genes: the CcdB toxin, the CcdA antitoxin, and the CcdR transcription factor.

CcdR is a small ligand-binding transcription factor in the family of feast/famine regulatory proteins [19]. It is natively found in P. ananatis, although it has a homolog, DecR, in E. coli [20]. CcdR exists as a tetramer but will dimerize upon binding cysteine to form an octamer that can act on binding motifs present in the promoter of the (unfortunately named) CcdA gene [11], which must not be confused with the CcdA antitoxin present in our kill-switch device. The CcdA gene in P. ananatis encodes cysteine desulfhydrase—a necessary enzyme for the degradation of L-cysteine [19]. It is referred to as CcdA\* in this project to avoid confusion with the antitoxin CcdA. Interestingly, in P. ananatis the CcdR and CcdA\* genes are located antisense to each other and are separated by an intergenic region only 117 nucleotides in size [11]. We used a mutant of CcdR, V84I, in our construct, which was identified to have a lower Km than the wild type, and a higher signal-to-noise ratio [11]. It was also shown that a combination of the low-strength promoter and RBS, J23114 and B0034, displayed the highest signal-to-noise ratio among combinations tested, so we used these elements in our construct as well [11].

CcdB is a well-studied toxin in E. coli, and has been used both in kill switches and, more commonly, in gateway cloning [21, 22, 23]. CcdB kills E. coli by inhibiting DNA gyrase, leading to double strand breaks during replication [21]. Natively, this toxin exists in the F’ plasmid, where it works in conjunction with the CcdA antitoxin to select against plasmid loss [21]. In our construct, CcdB is placed under the control of the CcdA\* promoter to make it L-cysteine-inducible. Because the regulatory regions of the CcdA\* promoter are not perfectly understood, this was accomplished by placing the CcdB coding sequence under the control of the entire CcdR-A\* intergenic region. A similar strategy was used to create an L-cysteine mediated biosensor by allowing the intergenic region to control GFP expression, and we implemented this method following discussion with one of the scientists who created this biosensor [11]. We chose this toxin because it is relatively well-studied, and because there are several E. coli strains on the market that are CcdB insensitive and can therefore be used to propagate our kill-switch before it has been tuned [25]. We took this coding sequence from the iGEM registry (BBa\_K3512001) and synthesized it with a silent point mutation, V73V, to remove an illegal BsaI cut site.

The CcdA antitoxin negates CcdB action by directly complexing with the toxin protein, preventing it from binding DNA gyrase [21]. In the native system, cell death is prevented only if CcdA on the F’ plasmid is being expressed. If the F’ plasmid is lost, residual CcdB remains active longer than CcdA and leads to cell death [18]. We included this antitoxin in our kill-switch design to ensure that “leaky” expression of CcdB does not lead to cell death in the absence of L-cysteine. Inclusion of this antitoxin also allows us to tune our kill switch to different cysteine concentrations. We included BsaI restriction sites flanking the CcdA promoter, allowing us to quickly swap Anderson promoters of different strengths into our construct. We will use this to perform parallel testing to identify what level of basal CcdA expression will lead to cell death at our desired L-cysteine concentration of 300 mg/L. We took this coding sequence from the iGEM registry (BBa\_K4907032).

***Cysteine-Induced Kill-Switch Plasmid Timeline:***

***Build*  (1)**

We intended to order our kill switch plasmid from Twist Biosciences in similar fashion to the cysteine overexpression plasmid, but Twist was unable to accept the order due to the presence of the CcdB toxin in the construct.

***Build*  (2)**

We then ordered the kill switch plasmid as a single gBlock from IDT to conserve time. We added homologous overhangs via PCR, cloned the insert into pUC19, and transformed it into competent cells that contained the F’ plasmid to avoid CcdB-associated toxicity. This also required several attempts due to high numbers of colonies containing unmodified vector or vector containing a primer-dimer, but screening via restriction digest allowed us to identify colonies that contained the insert. However, upon sequencing we noticed that all colonies contained 2-3 frameshift mutations in the CcdB or CcdR coding regions.

***Build*  (3)**

Suspecting selection against toxic genes could be the issue, we performed the assembly again and transformed into Invitrogen’s Oneshot CcdB Survival 2 T1R competent cells, which are designed to propagate plasmids containing CcdB. Sequencing of these colonies yielded mutations in similar locations to previous attempts.

***Build*  (4)**

Another IDT representative suggested that these mutations could be caused by the PCR step and offered to resynthesize our sequence for free, with the overhangs required for In-Fusion already added. We accepted this offer and removed an internal cut site that will make cloning easier should it become necessary to explore other cloning strategies.

***Test***

We cultured chassis containing our kill switch in 15 mL culture tubes, inoculating at a standard optical density and taking a final O.D at 600 nm after roughly 4 hours. To quantify cell death over time, we used a fluorescent bacterial live/dead screening assay [20]. Initial modeling and literature predict that cell death occurs at around 3 hours after CcdB concentration exceeds CcdA.

Our modeling also relies on quantification of the toxin/antitoxin CcdB/CcdA system, and so we are removing small aliquots for Western blotting to quantify the levels of CcdB protein in the cell over time. These readouts give insight into how our toxin/antitoxin system works, and are extremely useful to characterize for future iGEM teams and future applications over a broad range of use, as both a mechanism for controlling concentration, and also for safety and biocontainment purposes.

***Learn***

The iterative device assembly process has taught us about the complexities of in-fusion assembly and toxin synthesis, insight that is valuable for future teams. While DNA synthesis companies might offer to synthesize entire constructs, the cost of failure can greatly outweigh the benefits of success. Whenever possible, teams should order individual genes and subclone them in-lab. This is relatively simple with assembly methods such as Golden Gate and allows for faster turnaround time of DNA orders. More than that, this lowers the chances of failed syntheses and prevents one failed synthesis from setting a project back by months.

Although our planning was meant to offset any experimental errors that arose, the volume of difficulties with our assemblies has led to delays in our Build phase. The incorrect vectors received and ligation attempts with high mutation rates did slow down the impact and depth of data we were able to collect. As we are still in the process of acquiring full understandings of our functioning device, there is much to be gained from the further study of these parts through future testing schemes.

Currently, our team is continuing to test the now-ligated cysteine overexpression plasmid while simultaneously working on the assembly of the toxin/antitoxin kill switch. Our new method is based on what we have now learned from success with traditional restriction cloning methods, along with cloning into CcdB-resistant cells to minimize any potential selection occurring prior to our tuning of the kill switch. We will perform our ninhydrin assay scheme to determine the L-cysteine concentration our overproduction plasmid can achieve. Once our kill-switch plasmid is successfully ligated, promoters on CcdA can be swapped to tune the kill switch. In this way our project will undergo further iterations of the DBTL cycle leading up to the Jamboree.

The plasmid design process as a whole required significant creativity, and we acknowledge that there is future potential for optimizing design as further variants are discovered with higher rates of efficiency that may be better for certain kinds of dark fermentative application over other bioreactor systems. For example, the team abstained from using gene knockouts throughout this process, which would even further increase the efficiency of L-cysteine production in future iterations of this design [2]. The preliminary data collected will already better inform further design or build schemes.

Once our initial goals have been reached, the Cysteinator’s interactions with other bacteria in true bioreactor systems should be tested. Coculture interactions are imperative to study, either through wet lab or modeling analytics, to further characterize the device’s behavior in real-world environments. Additional future steps will investigate growth-phase and timing optimization in some capacity along with testing the quantity of bacteria needed for a bioreactor of a particular size to obtain a target concentration in a specific timeframe or even testing impact on optimal feedstock or inoculation conditions. The device will also be further optimized to prolong plasmid maintenance and decrease metabolic strain. Future modeling applications can also be implemented to examine these parameters, which will optimize the final product ultimately generated for the bioreactor environment.

Our assembly difficulties, particularly with the cysteine-induced kill switch, highlight the complexity associated with cloning toxic genes and transcription factors. It is likely that the high mutation rates in the kill switch were at least in part caused by either the toxicity of CcdB or the off-target effects of CcdR. While we negated the effects of CcdB toxicity by cloning in CcdB resistant E. coli, a previous study that used a CcdR-based L-cysteine sensing circuit described difficulties in achieving a plasmid containing CcdR expressed at high levels, possibly due to off-target effects of the transcription factor [6]. While our construct did use the same low-strength promoter and RBS that were successfully used in this study, future iGEM teams should take note of these difficulties. Transcription factors must be considered in the context of the entire chassis, not just the synthetic gene circuit. Future iterations of this L-cysteine sensing circuit would be greatly improved by enhancing the orthogonality of the cysteine-sensing transcription factor.

**Integrated Multi-Scale Model:**

Similar to the circuitry, we decided that our model should also be segmented to model the L-cysteine overproduction and kill-switch insert separately. This was done for two reasons, 1) so that we could build, design, and tune a model using different assumptions for both parts, and 2) we could predict how design decisions reflect themselves using the independent parts of the model.

***Design***

We used **constraint-based modeling** to model the effects of L-cysteine overproduction on the metabolic network of E.Coli and **mechanistic/stochastic modeling** to understand the effects of the kill-switch. Afterwards, we integrated the previous models into a dynamic flux balance analysis (dFBA) to understand how the genetic circuits implemented in one plasmid would function together and predict device behavior in a bioreactor.

The constraint-based model with flux balance analysis (FBA) played a large role in validating our device design and the genetic modifications made. We wanted to understand how L-cysteine production pathways would change as a result of the modifications made with the ultimate goal of increased L-cysteine export. The mechanistic model was used to aid in the prediction of testing schemes in wet lab procedure allowing for us to predict with some degree of accuracy the desired promoter on CcdA to reach our target concentration upon kill switch activation as well as how long our device would take to reach this target concentration.

***Build + Test***

We began with the baseline iML1515 genome scale metabolic model (GEM) for E.Coli K-12 MG1655 and then incorporated enzyme constraints using the ECMpy workflow to increase the accuracy of the predictions made with FBA. The media conditions were set to reflect the conditions used to culture the cells in wet lab with SM1, Luria-Bertani broth (LB), and thiosulfate. We ran FBA, optimizing for L-cysteine export, while requiring a certain level of biomass growth, with the initial conditions to use as a point of comparison. Afterwards, the GEM was altered to reflect the genetic modifications made experimentally. Kcat values and gene abundance values were specifically modified to reflect mutated enzymes and modified promoters. The FBA was run with the corresponding modifications to observe fluxes through relevant pathways involved in L-cysteine production and understand its impact on biomass growth. Separate simulations were run and used to validate L-cysteine overproduction. The exact concentrations of L-cysteine produced cannot be determined from the FBA due to underlying steady state assumptions but were approximated concentrations in later parts of the model.

***Learn***

From testing the device design using FBA, we learned that our design leads to a 1.93 fold increase in L-cysteine production. We also understood the limitations of the constraint-based modeling, specifically due to steady state assumptions. The model was not able to account for dynamic behavior over time or predict accurate concentrations of relevant metabolites and media components. Thus, we iteratively developed a hybrid multiscale model design to incorporate other models to characterize the various aspects of the device design.

***Design***

In addition to validating L-cysteine overproduction, we wanted to ensure our kill switch mechanisms and toxin-antitoxin system would work as expected. The kill switch was designed under the assumption that intracellular L-cysteine accumulation would bind to form a transcription factor with CcdR octamers and then activate the CcdB toxin. Mechanistic modeling with stochasticity was implemented to model the processes involved in activating the kill switch over time. The design of the model was developed using Hill-like approximations to reduce the number of kinetic parameters needed. Furthermore, we used molecular docking simulations to approximate the binding affinity values required for the differential equations used.

***Build + Test***

The formation of equations used in the mechanistic model were split into sections based on the various components involved in activating the toxin. We iteratively built and rewrote the equations incorporating assumptions from literature and receiving feedback from professionals in the field. The three sections of equations can be split into CcdR oligomerization/transcription factor formation, stochastic equations for the binding of the transcription factor to the CcdA promoter on the CcdB toxin, and CcdA/CcdB pathways for the toxin-antitoxin system. We originally intended to incorporate Michaelis Menten kinetics, however due to the lack of characterization on the CcdR system, we developed modified Hill equations instead. While working through the processes involved, we realized that we cannot assume the CcdB toxin is being expressed at any given time because of low expressions of the transcription factor, requiring us to factor in stochasticity. We tested the model by running simulations and evaluating the number of CcdA, CcdB, TA complexes, TAT complexes over time.

***Learn***

Through the process of designing the mechanistic and molecular docking models, we were able to understand the activation of the kill switch as well as the specific mechanisms involved in each component of this insert to further characterize the novel composite part. We found that the average passing time for the kill-switch activation was around 30 minutes. Through molecular docking simulations, we were able to explain binding patterns for CcdR, which was previously not well characterized. We found the binding affinity values for each step in the octamerization process and that 8 L-cysteine molecules are required to bind to form the final transcription factor.

## Goal 2: Understanding Device Implementation

The objectives of our second goal are to 1) identify potential roadblocks in future implementation and 2) ensure that our device is compatible with the current process flows and bioreactor infrastructure. Since the Cysteinator is meant to be integrated into current DF process flows, it is important that we assess how easily that can be done. To do this we decided to use the FBA from the model in Goal 1 to assess the maximum flux of L-cysteine production in different feedstock environments. Combining this with continued stakeholder engagement and background research we developed a thorough understanding of the challenges that may arise with the Cysteinator’s integration into DF process flows.

***Design***

The final design of the device incorporates the L-cysteine overproduction insert and kill-switch insert in one plasmid. The previous models model these inserts separately, but it does not reflect the overall device design. Thus, we designed a **final dynamic flux balance analysis (dFBA) model** as a method to model and predict the behavior of the overall device over time in a bioreactor setting. Furthermore, we used the previous FBA model to test various conditions characteristic of wastewater used in dark fermentation bioreactors to inform how our device is expected to behave in varying environments. The average passing time determined from the mechanistic model was used to understand how long the device would need to produce the required levels of L-cysteine and then lyse before dark fermentative bacteria is added to the bioreactor. We also designed the mechanistic model to be able to reverse engineer based on our desired concentration and determine the most effective promoter strength to influence wet lab design before device implementation.

***Build***

The dark fermentation bioreactors our device is meant to be implemented in will likely contain wastewater and other components. We altered and tested the media conditions in the FBA to understand these effects. We selected two representative media conditions for real/synthetic wastewater and solid waste. These sources mainly differ from our media conditions used in lab as they contain other carbon sources and could impact growth and L-cysteine export. The real/synthetic wastewater conditions varied in that the main carbon source was sucrose and the main carbon source in solid waste was fructose and xylose. The dFBA was built using time steps based on Euler’s method to resolve the FBA at various time points while updating extracellular metabolite and biomass concentrations. This was done to be able to approximate concentrations of relevant metabolites in the system, which was previously not possible. The dFBA was integrated with the mechanistic model by using the calculated intracellular L-cysteine concentrations to update the intracellular L-cysteine concentrations used to form the transcription factor.

***Test***

From initial testing of the effects of glucose and thiosulfate uptake on L-cysteine export and biomass growth, we noticed that the uptake values plateaued and no longer contributed to increase flux through the pathway. While testing the various wastewater conditions, we found that E.Coli K-12 derivatives and similar strains are not capable of utilizing sucrose as a source for growth because they lack the mechanisms to metabolite it. However, fructose was shown to lead to increased growth and L-cysteine export. Even in conditions without carbon sources present, the bacteria are able to utilize amino acids and other components for growth. After running the mechanistic model, it was found that the average kill switch activation time is around 30 minutes. The dFBA is currently still in testing, but similar results to the mechanistic model are expected.

***Learn***

Overall, the various types of models informed different aspects of how we expect the device to behave in industrial application. From the FBA, we learned that our model will not continue to increase L-cysteine export and continue growing but continually adding media components like thiosulfate and glucose. After reaching a certain threshold, the media components are no longer the determining factors for the efficacy of the device. Given the cost-effective appeal of the device, we can use this to inform downstream application by optimizing the concentration of carbon sources and thiosulfate added to the media while minimizing costs. Furthermore, the model highlighted the flexibility of our device to continue functioning over a wide range of conditions, even when no carbon sources were present. The time for kill switch activation predicted by the mechanistic model/dFBA informs the period of time necessary for the device to be in the bioreactor in the pretreatment phase before the dark fermentation bacteria can be added. The results from the dFBA integrated with the mechanistic model will be used to predict the time it takes to reach the desired L-cysteine concentration to further refine the predicted time necessary for the device to function as expected.

## Goal 3: Evaluating Device Utility

Our final goal aims to ensure that the device’s intended impact is realistic. Specifically, we evaluated whether our device was truly sustainable and can increase the commercial viability of DF and its conjugate systems. To do this, we segmented our analysis into four parts: 1) a comprehensive meta-analysis of the Cysteinator’s alignment with the UN Sustainable Development Goals (SDGs), 2) a life-cycle analysis to analyze the end-to-end emissions of our device, 3) a techno-economic analysis to determine the specific economic impact of the Cysteinator on the commercial market viability and scalability of the Cysteinator integrated DF process flows and, 4) continued literature review and stakeholder engagement to ensure that our assessments were reasonable and consistent with best practices.

***Design***

During our meeting with Dr. Lisa Colosi-Peterson, we were introduced to a tool for assessing resource consumption and their resulting emissions, **Life Cycle Assessment (LCA)**. She also specializes in a tool to evaluate the economic feasibility and viability of a technology, a **Techno-Economic Assessment (TEA)**. The combination of these assessments allows us to make estimations on both the environmental and economic impacts of the Cysteinator. We worked with Dr. Colosi-Peterson throughout the process of conducting our LCA and TEA to implement her feedback in our data sourcing and calculations. See section SDG 13.2 on the SDG page for a more detailed overview of her support.

***Build***

We conducted our LCA and TEA based on existing studies that model similar assumptions as those of the Cysteinator. For the LCA, the base study was constrained by a Dark Fermentation-Microbial Electrolysis Cell (DF-MEC) system that uses wastewater as a feedstock. For the TEA, we considered the application of E. coli in producing high value chemicals. To better model our device, we factored into our calculations the additional predicted H2 production as well as the avoided emissions from bio-based L-cysteine production and the need for wastewater treatment.

***Test***

From the LCA, we saw significant reductions in CO2 emissions per kg of H2 for a DF-MEC system compared to all current forms of H2 production, including steam methane reforming (SMR) and two forms of electrolysis (PEM and SOEC). It was also clear that wastewater utilization as a substrate makes a major impact in reducing emissions. Meanwhile, while bio-based L-cysteine supplementation makes a positive impact on H2 yields, the way in which it is sourced does not cause a prominent reduction in CO2 emissions. The TEA, furthermore, presented a 17% decrease in cost per kg of hydrogen with the implementation of the Cysteinator compared to a standard DF-MEC system. Hence, the boost in H2 production is significant enough to not only mitigate but reduce the net costs of our optimized system.

***Learn***

As a result of the similarities in direct emissions associated with industrial production, which relies on the process of keratin hydrolysate, and Cysteinator-derived L-cysteine production, which relies on metabolic engineering strategies, the LCA led us to further consider the ethical and environmental factors that are not accounted for by its scope. For instance, the keratin used in industrial processes is derived from animal sources and regulating their treatment is not well documented nor controlled. Further, it releases a by-product of hydrochloric acid waste, which is energy-intensive to treat and environmentally deleterious when untreated. The LCA ultimately validated the net positive impact of Cysteinator in terms of sustainable H2 production, as well as allowing us to realize factors of sustainability that go beyond the units of CO2 emissions per kg of H2. While it will take time for industrial development to make DF-MEC H2 production cost-effective in comparison to current methods, such as SMR and PEM electrolysis, the TEA demonstrates that the Cysteinator offers a promising tool for both sustainable and economical H2 production.

## References

## Click to Expand

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block page\_content %}

# **Protocols**

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block page\_content %}

* [Results](#intro)
* [Wet Lab results](#wet)
* [FBA Results](#fba)
* [Molecular Docking Results](#mdoc)
* [Mechanistic Model Results](#mech)
* [dFBA Results](#dfba)

### Results

We hypothesize that adding the Cysteinator to dark fermentative feedstocks will strengthen hydrogen-producing bacteria (HPB) propagation, enhance HPB hydrogenase activity, and weaken hydrogen-consuming bacteria (HCB) propagation. To validate this hypothesis, we conducted the experiments listed on our notebook and modeling pages. With their respective procedures described in the experiments and modeling pages.

The results detailed below serve as a proof-of-concept to inform future iterations of the Cysteinator, and validate that a both the L-cysteine overexpression plasmid and L-cysteine activated kill-switch are functional together in an engineered *E. coli* K-12 *BW25113* strain. The standards for device functionality are as follows:

1. To produce excess L-cysteine extracellularly to a concentration of 300 mg/L of media.
2. L-cysteine concentration triggers device growth arrest in the bioreactor and enhances fermentative bacteria to propagate.

To best characterize device behavior, we first simplified testing into validating the two plasmids separately. The L-cysteine overexpression plasmid was tested through the ninhydrin assay, a quantitative measurement of L-cysteine concentrations and optical density measurements. The L-cysteine activated kill-switch was tested with a bacterial viability, western blot, and optical density measurements.

### Wet Lab

#### Ninhydrin Assay

As our [engineering page](https://2025.igem.wiki/virginia/engineering) details, ligation and synthesis of our DNA has inhibited our ability to perform assays on bacteria transformed with correct plasmid constructs. However, we have still performed many iterations of the ninhydrin assay. Through extensive testing we were able to create valuable modifications to the 1967 Gaitonde Protocol [1] that optimized efficiency and minimized degradation of the final Ruhemann's Purple (RP) solution resulting in more accurate L-cysteine concentration readings.

The conversion of the raw qualitative findings of the ninhydrin assay to more useful quantitative data happens by running a calibration curve. A calibration curve takes known concentrations of L-cysteine and their OD560 values to approximate a mathematical equation capable of relating OD560 values to true L-cysteine concentration. To produce the most accurate calibration curves, we questioned four steps in the Gaitonde protocol [1]:

1. If the *intermediate ice bath*step is necessary.
   1. Our specific concern was that the ice bath step contributed to degradation due the time between the boiling step and optical density reading.
2. If the *adding ethanol*step is necessary.
   1. The ethanol is used to dilute the RP solution, and we surmised that our low optical densities were an effect of this over dilution.
3. If the *preparing the ninhydrin reagent*fresh every day is necessary.
   1. Since the ninhydrin reagent does not react with the other reagents in the sample until the boiling step, we thought that we would decrease the time burden of this assay by preparing the reagent days before the assay is run.

After our testing we concluded:

1. The ten-minute incubation period **does not have a significant impact** on the protocol. We observed subtle decreases in OD560 values when compared to the Gaitonde Protocol. However, because the ninhydrin assay is long and time-sensitive, it was determined that the ten-minute incubation period was unnecessary.
2. The exclusion of the addition of ethanol resulted in OD560 readings outside of the linear range of absorbance. This led to inaccurate readings and unreliable results. For this reason, we concluded that the addition of ethanol **has a significant impact** on the protocol.
3. As the prepared ninhydrin solutions had crystals that fell out of solution, it was determined that preparing the ninhydrin solution fresh every day **has a significant impact** on the protocol.
4. The best blank for the Ninhydrin assay is all the reagents of a typical sample without HCL. This solution accounts for the change in OD560 readings that occur independent of HCL addition.

Additionally, we decided to blank our spectrophotometer with a solution containing all reagents of a typical sample, excluding HCL (acetic acid, ninhydrin reagent 2, and SM1 media), because HCL acts as an activating reagent. By blanking with this solution instead of solely SM1 media, we accounted for any changes in the OD560 absorbance that may have been introduced by the assay conditions.

The experience of optimizing a time-sensitive ninhydrin assay led us to determine unique techniques that maximize accurate readings. Read more about them on our [contributions page](https://2025.igem.wiki/virginia/contribution).

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/calibrationcurve79.avif)

**Figure 1**
depicts calibration curve results prior to optimization testing. 5 readings were taken at each concentration with negligible error and variance.

Figure 1 depicts calibration curve results prior to optimization testing. 5 readings were taken at each concentration with negligible error and variance.

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/calibration716.avif)

**Figure 2**
depicts calibration curve results with post-optimization modifications. 5 readings taken at each concentration with neglible error and variance.

The ninhydrin calibration curve performed before the modifications showed no evidently discernible relationship between L-cysteine concentration and OD560 (see Figure 1). The ninhydrin calibration curve performed after the post-optimization modifications were able to model the expected logarithmic relationship between L-cysteine concentration and OD560 (see Figure 2). These results further validated that our modifications were justified.

#### Plasmid Assembly

After addressing our assembly challenges, we were able to successfully assemble our L-cysteine overexpression plasmid construct through traditional restriction cloning into the pUC19 vector. Blue/White colony screening yielded positive qualitative and sequence results (see Figure 3).

Our immediate next steps are:

* transforming the L-cysteine overexpression plasmid into E. coli JM109 cells and beginning our assay scheme to determine the amount of L-cysteine export and the speed of cell growth.
* performing a similar blue/white screen of OneShot CcdB Survival Cell colonies transformed by the L-cysteine-induced kill-switch plasmid.

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/img-7791.avif)

**Figure 3**
NEB turbo competent cells transformed with pUC19 (left) and L-cysteine overexpression plasmid (right). X-Gal and IPTG spread on plates 1 hour prior to plating of cells. Plates incubated ~18 hours at 30 °C.

### Effects of Modifications on L-cysteine production

The first aim of the flux-balance analysis (FBA) analysis was to understand how the modifications we made in the L-cysteine overexpression plasmid affect overall L-cysteine production/export and biomass growth. A lexicographic optimization was done on the modified and unmodified systems, and those results are detailed in Table 1. Table 1 shows that our circuitry **increases L-cysteine export by nearly twofold.**

|  |  |  |
| --- | --- | --- |
| Simulation | Biomass/Growth Rate (h^-1) | L-cysteine Export Flux (mmol/gDW\*h) |
| Unmodified | 0.117 | 7.263 |
| Modified | 0.201 | 14.03 |
| Fold Change | 1.71 | 1.93 |

**Table 1.**
shows the effects of modifications to the GEM on overall growth rate and L-cysteine export

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/cysteinator/top-15-flux.avif)

**Figure 4**
shows the Top 15 reactions by flux as predicted by the optimal solution from FBA

While performing optimization, the model predicts how fluxes are carried through metabolic networks/pathways. The top 15 reactions carrying the highest flux are illustrated in Figure 3. It is noted that most of these reactions involve using glucose and acetate for biomass growth. Upon further analysis of the fluxes through all pathways, the L-cysteine production pathways depicted in Figure 2 all carried flux.
Thus, we can expect the modifications made in genetic circuits used in wet lab to similarly increase L-cysteine production experimentally.

### Media Conditions

Another mechanism for adding constraints before performing FBA is through media conditions and limiting the metabolites available for uptake. The initial simulations were run using SM1, LB, and thiosulfate.
Because adding thiosulfate is an additional cost to our device design and function, the model was used to understand how thiosulfate and carbon sources like glucose affect biomass growth and L-cysteine production.

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/cysteinator/og-heatmaps.avif)

**Figure 5 A-B.**
shows the heatmap on the effects of glucose and thiosulfate uptake on biomass growth and L-cysteine export

From Figure 5B, it is apparent that after thiosulfate uptake reaches around 8 mmol/gDW\*h, L-cysteine export does not increase significantly and plateaus. These results are critical for understanding how much thiosulfate to add to the medium to optimize L-cysteine production without added costs, especially for making the device cost-effective for industrial application. Biomass growth similarly does not continue to increase linearly as glucose uptake increases. It is important to note that because FBA predicts fluxes assuming steady state conditions, specific optimal concentrations cannot be determined from this model.
However, the results still provide strong insights to consider when preparing the device for industrial application.

### Predicting Flux Under Varying Conditions

Furthermore, these conditions are likely to change if our device were to be implemented in a bioreactor. Dark fermentation bioreactors contain wastewater and sludge for biohydrogen production. Through modifying and testing various media conditions, we can predict how our device will behave under these different conditions and use the results further down the line for industrial use. Wastewater used in bioreactors commonly comes in three forms—real wastewater (i.e. from factories), synthetic wastewater, and solid waste. Sugar factory wastewater (i.e. from beet sugar) and synthetic wastewater contain several of the same components like sucrose and acetate [16]. Solid waste is typically composed of fruit/vegetable waste and office paper waste, which contain glucose, fructose, and xylose [17]. The original SM1 conditions had glucose as the main carbon source. To test different bioreactor conditions, two representative medium conditions were developed to represent real/synthetic wastewater and solid waste. The upper bounds for uptake rates were calculated similar to the SM1 medium based on the concentration of each medium component found in literature and the component’s molecular weight. Although we expect to add our device with the SM1 medium to wastewater, the media conditions tested excluded SM1 medium components to understand the effects of the wastewater without other contributing factors. The real/synthetic wastewater conditions incorporated sucrose, acetate, butyrate, lactate, and ammonium in addition to amino acids and trace metals. The solid waste conditions incorporated fructose and xylose in addition to amino acids and trace metals.
The uptake bounds for amino acids and trace metals were kept the same from initial simulations.

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/cysteinator/wastewater-heatmap.avif)

**Figure 6 A-D.**
show heatmaps plotting biomass growth and L-cysteine export based on the uptake of various metabolites under varying conditions. A-B: Real/synthetic wastewater conditions. C-D: Solid waste conditions

While testing the real/synthetic wastewater conditions, the main carbohydrate added to the medium was sucrose. Thus, sucrose was initially expected to be used for growth in place of glucose. However, after running optimization and evaluating Figure 5A, it was apparent the model did not follow the initial conjecture. Biomass growth was independent of sucrose uptake. With further evaluation of the iML1515 model, it was found that the model did not contain downstream reactions for hydrolyzing sucrose 6-phosphate to use it as a carbon source. This is because the E.coli K-12 strain and its derivatives lack the csc operon, which is responsible for sucrose catabolism [18]. This operon is found in other strains like E.coli EC3132 and E.coli W. Although the specific strains tested in wet lab are E.coli K-12 derivatives, future directions for the project include testing the genetic circuits in various chassis.
Thus, the same type of testing and analysis can be used to evaluate the effect of sucrose on the functionality of the device in different chassis.

Furthermore, biomass growth and L-cysteine export were still possible without sucrose or glucose present. From analyzing the reactions carrying flux in the model, acetate and threonine uptake reactions were found to carry high fluxes. These reactions were tested to see if they had an effect on biomass growth. From the analysis, it was seen that the model was able to utilize threonine and amino acids for growth.
These results highlight the flexibility of our device to function under various and nonideal conditions, necessary for a device to be implemented in variable environments.

In addition to real/synthetic wastewater, solid waste was also tested. To determine the effects of different carbon sources like fructose and xylose, glucose was removed from the medium conditions although it is commonly present in solid waste. These simulations yielded slightly higher biomass growth and L-cysteine export flux of 0.222 h^-1 and 14.17 mmol/gDW\*h, respectively, compared to initial simulations. With further analysis, it was observed that these results were mainly attributed to the addition of fructose to the medium. E.coli K-12 was able to naturally metabolize this carbon source. These results are shown in Figure 5C-D. Thus, we expect higher yields with the addition of glucose as well. Thiosulfate uptake similarly plateaus and does not contribute to increased L-cysteine export after reaching a certain uptake rate.
This signifies that once an optimal concentration of thiosulfate is determined, adding more thiosulfate does not make the system more effective.

## Summary

The FBA model, incorporating the iML1515 GEM and enzyme constraints, provided insights into the effects of genetic modifications on the metabolic network of E.coli K-12 BW23115 and its ability to produce and export L-cysteine. The results validated the effect of the genetic modifications made experimentally and predicted a 1.71-fold increase in biomass growth and a 1.93-fold increase in L-cysteine export. After testing media conditions, the model simulations showed that thiosulfate and glucose uptake plateaued after reaching certain rates, revealing that the cost-effective aspect of the device design can be optimized by identifying the ideal concentrations of these medium components. Testing various media conditions representative of dark fermentation bioreactor environments demonstrated the metabolic flexibility of the design to use various carbon sources like fructose and threonine for growth, while identifying limitations such as the K-12 strain’s inability to metabolize sucrose.
These findings offer actionable insights for scaling the device and selecting the final industrial chassis.

## Molecular Docking Results

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/cysteinator/oligomerization-1.avif)

**Figure 7 A-D.**
A. Monomer structure of CcdR protein developed in SWISS-Model after structural minimization in ChimeraX. B-D. Dimer, tetramer, and octamer structure determined by AlphaFold. The structures are colored by confidence scores. PlDDT scores higher than 90, 70-90, and less than 70 are indicated by dark blue, cyan, and yellow, respectively.

Figure 7 illustrates the 3D structure of each phase of oligomerization of CcdR. The binding patterns follow those of the FFRP transcription factor family of proteins. Specifically, in the AsnC family of proteins, the homodimer is held together by interactions between the anti-parallel beta sheets of the C-terminal domain [11]. The same binding pattern is observed in the dimer structure in Figure 7B. The dimer-dimer interface mainly consists of hydrophobic contacts, and each dimer pair forms a side of the binding pocket for the ligand. AsnC proteins also form an octamer with P4 symmetry, which is observed in the predicted octamer structure of CcdR as well.

Overall, all of the predicted structures in AlphaFold have confidence scores consistently higher than 90, indicating high accuracy. Furthermore, the interface predicted template modeling (ipTM) score for the dimer and octamer structures were higher than 0.8 as shown in Table 2. Values higher than 0.8 represent confident high-quality predictions.

|  |  |  |
| --- | --- | --- |
| Oligomerization Phase | Predicted Kd Value (M) | ipTM Score from AlphaFold |
| Dimerization | 8.4e-18 | 0.89 |
| Tetramerization | 8.9e-09 | 0.6 |
| Octamerization | 7.9e-16 | 0.88 |
| CcdR Octamer + L-cysteine | 7.2e-15 | N/A |

**Table 2.**
show Kd values predicted for each phase of oligomerization by PRODIGY

The Kd values were directly inputted into the mechanistic model to represent the binding affinity and the likelihood of the formation of the final octamer.

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/binding-channe-s.avif)

**Figure 8.**
show four binding channels into the L-cysteine binding pocket adapted from Zhou et. Al, 2024 [10]. Two L-cysteine ligands bind to each dimerization interface

![Embedded Image](https://static.igem.wiki/teams/5664/cysteinator/screen-shot-2025-10-04-at-1-09-18-pm.avif)

**Figure 9.**
sho *In silico* docking of L-cysteine bound to the dimerization interface. Residues involved in binding pocket formation and binding to L-cysteine are labeled with residue name and number. (2nd CcdR monomer was removed for visibility).

Given the hydrophobic nature of L-cysteine, it is reasonable that the ligand preferentially binds within the hydrophobic dimer–dimer interface.

## Mechanistic Model Results

For this section, I would like to recollect the guiding questions that we started with

* How long on average does the killswitch take to activate?
* What is the average internal concentration of cysteine at that time?
* What is the desired promoter of ccdA such that we ultimately reach our target concentration at cell death?

and to answer these questions with the data we have collected. Starting with question 1, which is the main output from the mechanistic model. Due to the stochastic expression of CcdB, output graphs can vary drastically; to remedy this, Figure 10 overlays 1000 trials of our system. The black bars represent the average first passing time and its standard deviation with \(\bar{t}\_f=19469.38\pm8937.72\) s. Firstly, the standard deviation is notably quite large, though this is expected as the system is known to and expected to have a certain variance. By definition, we have induced variability into this system, and therefore, this cannot be used as an indicator of model accuracy. Though we can clearly see a clustering of values within that range that begins to thin out significantly beyond. To that end, we find that \(64.5\%\) of our \(t\_f\) values fall within the domain \(\{0,\bar{t}\_f+\sigma\}\). Meaning we estimate that approximately 2/3 of the population's kill-switch will activate within the first 0.5 hours of a significant increase in cysteine concentration.

![final_graphs_multi_graph (1).png](https://gitlab.igem.org/2025/virginia/-/raw/fb4e550fe3da9a14032d67bff3e0d1aac2284efa/images/final_graphs_multi_graph__1_.png)

**Figure 10**  is a graph showing 1000 runs of our toxin-antitoxin system

![distribution.png](https://gitlab.igem.org/2025/virginia/-/raw/fb4e550fe3da9a14032d67bff3e0d1aac2284efa/images/distribution.png)

**Figure 11**  shows the distribution of first passage times of 1000 runs of the toxin antitoxin system

Figure 11 displays this information in a slightly more cohesive manner and displays a very interesting result. We see that, due to the central limit theorem, we actually recover the Poissonian statistical distribution that we originally put in. This fits quite well, showing us that the decision to use Poissonian statistics was justifiable. An \(R^2\) of 0.8885 is fairly consistent with a good fit; typically, fits closer to 1 are considered better. We still must ask the question of how accurate our average is to answer our second question. To do so, we take the standard error of the mean for our results, which is given by Eq. 35

\[
SEM\equiv\frac{\sigma}{\sqrt{N}}
\] Where \(\sigma\) is the standard deviation and N is the number of outcomes in our sample space, for our system, we find that \(SEM=\pm282.64\), meaning our average is accurate within \(2.9\%\) of the actual value. With that degree of accuracy, we can take our average to be a fairly good approximation of the average of our system, and it can therefore be used to answer question 2. By looking at our dFBA outputs at our average time point, we see that the average internal cysteine concentration at the time of kill-switch activation is 2.144 mM. This is of a reasonable magnitude for intracellular cysteine under an over-expression pathway as found in "High Levels of Intracellular Cysteine Promote Oxidative DNA Damage by Driving the Fenton Reaction" (Park and Imlay, 2003). In this paper, it was found that an intracellular concentration of 1.5 mM is typically reached by a cysteine-overexpressing device. This is indicative that our model may overestimate concentrations in general, which is not unexpected, but is certainly a shortcoming of our model. To find what promoter strength we needed for CcdA we first looked at the available Anderson promoters that we had on hand and their relative strength according to the base promoter. We then used a tool from the Salis lab, specifically from "Automated design of thousands of nonrepetitive parts for engineering stable genetic systems"(Houssain et al., 2020), to calculate the rate constant for the base promoter in units of \(\frac{1}{M\*s}\), the equation for which is detailed below \[
k\_j=\frac{1}{2}\sum\_{i=1}^{2}\frac{\frac{RNA\_{i,j'}}{\sum RNA\_i}}{\frac{DNA\_{i,j'}}{\sum DNA\_i}}
\] Where i represents the replicate trial, j' represents the j'th concentration amongst all of the listed promoters for both DNA and RNA. These measurements were made for 2 replicates and are then averaged over those 2 replicates to increase the accuracy of the final rate constant. From this Eq. 36, we were able to determine the strength of the base level Anderson promoter and, therefore, the strength of the others that we had on hand.

|  |  |  |
| --- | --- | --- |
| Promoter | Relative strength to BBa\\_J23100 | Rate (\(\frac{1}{M \cdot s}\)) |
| BBa\\_J23100 | 1 | 0.1834 |
| BBa\\_J23102 | 0.86 | 0.157724 |
| BBa\\_J23105 | 0.24 | 0.044016 |
| BBa\\_J23109 | 0.04 | 0.007336 |

**Table 3** shows the relative promoter strengths and transcription rates.

For each of our 3 available promoters, we tested the above values for the expression rate constant of CcdA, \(k\_{AMRNA}\), to see how it affects our dynamical system. The results of this are in Figures 12 and 13:

![sim_0_graph (2).png](https://gitlab.igem.org/2025/virginia/-/raw/f93464e55099f5a8d72dbdcb4c62d82b7d072e14/images/sim_0_graph__2_.png)

**Figure 12** are graphs showing 1 run of the toxin antitoxin system with the BBa\\_J23102 promoter on CcdA

![sim_0_graph (3).png](https://gitlab.igem.org/2025/virginia/-/raw/f93464e55099f5a8d72dbdcb4c62d82b7d072e14/images/sim_0_graph__3_.png)

**Figure 13** are graphs showing 1 run of the toxin antitoxin system with the BBa\\_J23109 promoter on CcdA

As can be seen in the case of the BBa\\_J23102 Figure 12, the level of expression was such that it would always outweigh the expression of CcdB. This would therefore make activation of our kill switch almost impossible, meaning we would have no control over cell death. Additionally, looking at the case of BBa\\_J23109, Figure 13, we see that expression of CcdA cannot outweigh that of CcdB. This does not allow sufficient time for cysteine to overexpress and is therefore not viable. With BBa\\_J23105, Figure 10, we are in the goldilocks zone, and expression of the CcdA/CcdB systems perfectly outweigh each other, allowing for fine-tuned control of the kill-switch. Ideally, we would prefer a slightly less powerful promoter, as currently, we would reach an external cysteine concentration of about 433 mg/L with the BBa\\_J23105 promoter. To reach our desired external concentration of 300 mg/L, we would need an average first passage time of 15945 s. An additional constraint on this is the availability of discretized promoter strengths, necessitating an approximation based on the closest available strength. Based on this analysis, we have found that the best Anderson promoter to optimize our device is BBa\\_J23105.

## dFBA Results

The dFBA model without integration with the mechanistic model predicted extracellular metabolites, biomass concentrations, and intracellular/extracellular L-cystiene concentrations over time. However, upon integration with the mechanistic model, the transcription factor concentrations were too low to activate the kill switch. We are currently in the process of troubleshooting the error. The predicted intracellular L-cysteine concentrations from the dFBA reach the target concentration levels of around 300 mg/L, which should be sufficient to activate the kill-switch. The low levels of the transcription factor are likely attributed to the assumption that 8 L-cysteine molecules are required to form the complex and the formulation of the Hill-like approximations used in the mechanistic model. We expect to identify a solution and accurately predict the kill-switch activation time.

{% endblock %}