Initial Plans

A Simplified Sample Simulation in Python

Assumptions of the Model

Lessons From Existing Models

Future Directions

Objective 1: Growth Curve

Parameters and Variables

Objective 2: Risk Assessment

Objective 3: Toxicity Testing

Objective 4: Bacterial Colonization Competition Model








Initial Plans

Transforming Vitamin B12 into E. coli Nissle 1917 for Optimal Gut Conditions


iGEM Guelph embarked on a project with the initial goal of enhancing gut health through the transformation of vitamin B12 (cobalamin) and gastric intrinsic factor (GIF) into the probiotic Escherichia coli Nissle 1917 (EcN). Furthermore, we contemplated expanding our scope to include the synthesis of vitamin B9 (folate), addressing the needs of individuals with deficiencies in both vitamin B12 and vitamin B9. This would further advance our mission to promote gastrointestinal well-being. Central to this endeavour was the primary objective of understanding the gut conditions required for EcN to efficiently synthesize and deliver these essential vitamins to the host's body. The goal of this exploration lay in pinpointing the exact conditions wherein the probiotic's impact on gut health attained its utmost significance. To tackle this challenge, our strategy revolved around the development of a comprehensive deterministic compartmental model, which encompassed pertinent compartments within the human body. This model was designed to include the stomach, small intestine, bloodstream, liver, and other compartments relevant to vitamin metabolism. To achieve this, a comprehensive compartmental mathematical model was proposed, driven by the following key steps:


Table 1: Key project steps in creating a compartmental model

Step 1: Identifying Relevant Compartments We identified all the relevant compartments within the human body where the probiotic and vitamin metabolism take place. This included not only the stomach and small intestine but also the bloodstream, liver, and any other compartments involved in the transport and metabolism of vitamins throughout the body.
Step 2: Defining Pharmacokinetic Processes The next challenge is to define the pharmacokinetic processes that describe the movement of the probiotic and vitamins between these compartments. This involved developing mathematical relationships, such as rate equations, that could explain the absorption, distribution, metabolism, and elimination of both EcN and the vitamins.
Step 3: Data Collection Gathering data was a critical aspect of this project. It involved collecting information on probiotic concentrations, vitamin production rates, vitamin metabolism, and various physiological parameters relevant to the compartments under investigation. These data served as the foundation for constructing the mathematical model.
Step 4: Creating the Mathematical Model The heart of this project lies in creating a comprehensive mathematical model. The goal was to develop a system of ordinary differential equations (ODEs) that described how the different parameters changed over time. These equations are needed to capture the dynamics of growth, decay, and vitamin production within EcN.
Step 5: Numerical Simulation With the mathematical model in place, numerical methods, such as ODE solvers like those in SciPy or deSolve, were utilized to simulate the model over time. This step would allow our team to predict how EcN and vitamins would interact within the gut over various scenarios.
Step 6: Comparison to Experimental Data The final and crucial step was to compare the mathematical model results with data collected in the lab, if available. This validation step would provide insights into the accuracy of the model and its ability to predict real-world outcomes.



A Simplified Sample Simulation in Python


To provide a glimpse of our simulation process, we outline a simplified Python script looking at the relationship between the introduction of E. coli Nissle 1917 into the small intestine and vitamin B12 production. According to Shaw et al. (1989), vitamin B12 is primarily absorbed in the ileum, and vitamin B12 produced by E. coli in the colon may not be absorbed by the human host. However, the bacterial load of certain E. coli strains that colonize the small intestine can be high (Pradhan et al., 2020). Therefore, we aimed to create a genetically engineered E. coli Nissle 1917 that can colonize the ileum. This script represents a generalized compartmental model with data found in the literature:


import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import solve_ivp

# Define the ODE system here:
def odes(t, y):
dEcoli_dt = growth_rate * y[0]
dB12_dt = production_rate_B12 * y[0]
return [dEcoli_dt, dB12_dt]

# Parameter values based on literature
growth_rate = 0.0615 # E. coli growth rate (hr^-1)
production_rate_B12 = 0.028 # B12 production rate (mg/l/hour)

# Initial conditions
initial_ecoli_population = 0.85 # Initial E. coli Nissle 1917 population (log(OD600))
initial_B12_concentration_untransformed = 120 # Initial B12 concentration (pg/mL)
initial_B12_concentration = 0.000126 # Initial B12 concentration (mg/l)

# Define parameters and initial conditions
initial_conditions = [initial_ecoli_population, initial_B12_concentration]
t_span = (0, 24)

# Code to solve the ODEs:
solution = solve_ivp(odes,
t_eval=np.linspace(0, 24, 100))

# Code to extract results:
t = solution.t
ecoli_population = solution.y[0]
B12_concentration = solution.y[1]
# Create a plot
fig, ax1 = plt.subplots(figsize=(10, 6))
ax1.set_xlabel('Time (hours)')
ax1.set_ylabel(r'$\mathrm color='tab:blue')
label=r'$\mathrm ax1.tick_params(axis='y', labelcolor='tab:blue')
ax2 = ax1.twinx()
ax2.set_ylabel('Concentration (mg/l)', color='tab:red')

# Plot B12 concentration on the right y-axis
ax2.plot(t, B12_concentration, color='tab:red', linestyle='--', label='Vitamin B12 Concentration (mg/l)')

ax2.tick_params(axis='y', labelcolor='tab:red')

# Legends
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

# Set the title and grid
plt.title('Simulation of $\\mathrm$ Nissle 1917 Population Growth and Synthesis of Vitamin B12')

# Show the plot


The growth rate of E. coli Nissle 1917 was estimated from the experimental data from the study on E. coli Nissle 1917 growth by Gill et al. (2019). The time and logOD600 values were entered into Python using the code below:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# Data (time in hours and corresponding logOD600 values)
time_hours = np.array([0, 3, 5, 9, 12, 14, 18, 19, 24])
logOD600_values = np.array([-0.85, -0.5, -0.21, -0.18, -0.1, 0, 0.1, 0.08, 0.18])

# Function for exponential growth
def exponential_growth(t, OD0, mu):
return OD0 * np.exp(mu * t)

# Convert logOD600 to OD600
OD600_values = 10**logOD600_values

# Fit the exponential growth curve to the data
popt, _ = curve_fit(exponential_growth, time_hours, OD600_values)

# Extract estimated parameters
OD0_estimate, mu_estimate = popt

# Plot the data and fitted curve
plt.figure(figsize=(8, 6))
plt.scatter(time_hours, OD600_values, label='Data', color='blue')
plt.plot(time_hours, exponential_growth(time_hours, OD0_estimate, mu_estimate), label='Fitted Curve', color='red')
plt.xlabel('Time (hours)')
plt.title('Exponential Growth Curve Fitting')

# Display the estimated growth rate (mu)
print(f"Estimated Growth Rate (mu): {mu_estimate:.4f} hr^-1")
mu = 0.0615 (hr^-1)


This Python simulation was conducted using the Replit Integrated Development Environment (IDE), which provides an interactive platform for coding and running Python scripts. Below, you can observe the plot generated as a result of the simulation.

Figure 1: Simulation of E. coli Nissle 1917 population growth and vitamin B12 concentration

Note: The simulation period was 24 hours.




Assumptions of the Model


1. According to Akbulut et al. (2022), an individual with vitamin B12 deficiency has a serum vitamin B12 concentration less than or lower than 126 pg/mL or 1.26^-4 mg/L. We assumed that the serum level of vitamin B12 for deficient individuals was the same as the concentration in the small intestine.

2. We assumed that the production rate of cobalamin remains constant under optimal conditions, using experimental values derived by Fang et al. (2018). The highest vitamin B12 production was achieved by an E. coli strain at a temperature of 32 degrees Celsius, with Isopropyl β-D-1-thiogalactopyranoside (IPTG) concentration of 1mM, and grown in an optimized CM medium. Therefore, we assumed the potential production rate of vitamin B12 to be calculated by: Production Rate (mg/l/h) = Vit B12 Yield (mg/l) / Fermentation Duration (hours) = (0.67 mg/l)/(24 h) = 0.028 mg/l/h Since the focus of this model is the production of vitamin B12 with respect to the EcN population, neither a clearance rate nor an absorption rate was included. The simulation plot reveals a noteworthy relationship between E. coli Nissle 1917 population growth and vitamin B12 concentration in the ileum. Specifically, the data demonstrated a linear correlation, where an increase in the EcN population corresponded to a proportional increase in the vitamin B12 concentration. This finding suggests that EcN plays a role in promoting the biosynthesis of vitamin B12 within the ileum.





Lessons from Existing Models


As part of the initial research process, it was crucial to study existing compartmental models and literature related to cobalamin, folate, and probiotics. We consulted two studies offering valuable insights:

Model 1: This compartmental model, conducted by Duncan et al. (2013) compared folate metabolism among 10,000 virtual individuals. It revealed a strong correlation between tissue and plasma vitamin B9 levels. For this reason, we have approximated serum levels of vitamin B12 to be representative of tissue levels of vitamin B12. The model by Duncan et al. (2013) was created using ordinary differential equations for each metabolite’s rate of change, as well as kinetic equations for metabolism and transport. For this reason, we chose to include a rate of change equation for the concentration of vitamin B12 and a rate of change equation for the concentration of GIF.

Model 2: Another notable study conducted by Fang et al. (2018), showcased how optimizing culture conditions could significantly impact the production of vitamin B12 in a variety of engineered E. coli strains. The researchers in this study used various substrates and environmental conditions to fine-tune the production process. Our future experimental work would include recommended methods for improving cobalamin production.





Future Directions


Looking ahead, we anticipate the integration of vitamins B9 and B12 into E. coli Nissle 1917 using CRISPR technology. Using our compartmental model as a guide, we aim to identify the optimal gut conditions for maximizing the probiotic's effectiveness. This represents a crucial step forward as we leverage CRISPR-Cas9 technology, known for its precision in genome editing. This transition marks a significant advancement in our research, opening the door to potential breakthroughs in probiotic studies and human health improvement.


Objective 1: Growth Curve

Utilizing the E. coli Nissle 1917 strain alongside our genetically transformed strain, we explored their growth dynamics by employing a growth curve assay. This approach provides valuable insights into the growth patterns of our modified strains within the gastrointestinal environment. To mimic the conditions of the human gut, we performed this growth curve analysis using a specially formulated chemostat media.

The formulation of this media draws from an industrialized diet model, originally developed by Dr. Allen-Vercoe at the University of Guelph. This diet model is meticulously composed and comprises a range of essential components, including:

  • Peptone water: 1.6 g
  • Yeast extract: 3.6 g
  • Pectin: 3.6 g
  • NaHCO3: 3.6 g
  • Xylo-oligosaccharide: 3.6 g
  • Arabinoglactan: 3.6 g
  • Starch (wheat): 9 g
  • Caesin: 5.4 g
  • Insulin: 1.8 g
  • NaCl: 0.18 g

Add to 1.8 L water.


  • Bile salts: 4 g
  • L-cysteine HCl: 4 g
  • K2HPO4: 0.32 g
  • KH2PO4: 0.32 g
  • MgSOK4: 0.08 g
  • CaCl4: 0.08 g
  • Hemin: 8 mL
  • Menadione: 8 mL

Add to 600 mL water.

The growth curve assay is conducted within an anaerobic chamber, maintaining a steady temperature of 30°C. This controlled environment is essential, given that E. coli Nissle 1917 functions as a facultative anaerobic bacterium. By incubating under anaerobic conditions, the risk of cell death or inhibition is mitigated, preserving the integrity of the microbial population. This strategic choice aligns with E. coli Nissle 1917's inherent anaerobic adaptability. Furthermore, this method guarantees precise analysis of growth kinetics, ensuring reliable and accurate results.

Figure 2: Growth Curves of E. coli Nissle 1917 and E. coli dH5a

Note: Cultured in chemostat media at 30°C with orbital shaking for 850s over a 48-hour period. The growth profiles of these strains were monitored by measuring the optical density at 600 nm (OD600). Notably, these strains exhibited distinct phases of exponential growth, with OD600 values ranging from 0.4 to 0.5 and from 0.58 to 0.65.


In the study by Gill et al. (2019), it was observed that E. coli Nissle 1917 exhibits higher growth compared to E. coli K12 strains like E. coli dH5a. As shown by the distinct trends observed in their growth curves. Specifically, when subjected to growth curve analysis at 30°C, EcN demonstrated two distinct phases of exponential growth, ultimately achieving a significantly higher final OD600 value in comparison to E. coli dH5a. Notably, both EcN and dH5a displayed two phases of exponential growth at 30°C, and their OD600 values converged to similar levels after 24 hours of incubation.


The observation that E. coli Nissle 1917 outperforms E. coli K12 strains like E. coli dH5a in growth curve experiments carries significant implications for its potential role as a probiotic within the gut. Firstly, EcN's capacity to outcompete other E. coli strains suggests it may possess a competitive advantage within the gut environment. This advantage could potentially facilitate more effective colonization and persistence in the gastrointestinal tract, enhancing its ability to deliver beneficial effects. Furthermore, the growth exhibited by EcN could imply it could remain viable in the gut for extended periods, a critical characteristic for probiotics. The establishment of a lasting presence in the gut microbiota is essential for probiotics to fulfill their functions, which include supporting digestion, modulating the immune system, and inhibiting the growth of harmful bacteria.


The analysis of growth curves not only provides insights into EcN's growth characteristics but also informs strategies for optimizing its potential as a probiotic. These findings inspire further investigations into the genetic factors and mechanisms underlying EcN's growth advantages. By deciphering the genetic basis of EcN's robust growth, researchers can identify specific genes and pathways that contribute to its superior performance. This knowledge can guide the development of genetically modified probiotics with enhanced growth traits, ultimately enhancing their efficacy and impact within the gut microbiome. Additionally, these insights into EcN's growth dynamics pave the way for targeted interventions to support and promote the growth of this probiotic strain within the gastrointestinal environment. Strategies such as tailored prebiotics or dietary interventions can be designed to create a nurturing ecosystem for EcN, further enhancing its probiotic effects.


Objective 2: Genetic Engineering

Our second objective revolves around leveraging genetic engineering techniques to enhance the capabilities of E. coli Nissle 1917. Specifically, we aim to engineer this probiotic strain to produce and deliver vitamins B9 and B12 effectively within the human gastrointestinal tract. Achieving this objective involves several key steps:


1. Gene Identification: The initial phase of genetic engineering entails identifying the specific genes involved in the biosynthesis of vitamins B9 (folate) and B12 (cobalamin). By pinpointing these genetic elements, we can establish the foundation for manipulating E. coli Nissle 1917's metabolic pathways to enable vitamin production.

2. Genetic Modification: Once the target genes are identified, we employ precise genetic modification techniques, such as CRISPR-Cas9, to introduce genetic alterations into E. coli Nissle 1917. These modifications are designed to activate or enhance the expression of genes responsible for vitamin synthesis. By engineering the probiotic's genetic makeup, we can equip it with the ability to synthesize vitamins B9 and B12.

3. Optimization: Genetic modification is followed by a meticulous optimization process. This phase focuses on fine-tuning the metabolic pathways of E. coli Nissle 1917 to ensure efficient vitamin production. Optimization parameters may include adjusting culture conditions, nutrient availability, and growth kinetics to maximize vitamin yield.

4. In Vitro Validation: Validating the engineered E. coli Nissle 1917 strains in vitro is a crucial step to confirm their vitamin-producing capabilities. This involves growing the modified strains in a controlled laboratory setting and measuring their vitamin production levels. In vitro validation serves as a preliminary assessment of the engineered probiotics' functionality.

5. In Vivo Testing: Transitioning to in vivo testing is the next milestone, where the engineered probiotics are administered to animal models or human participants. This phase assesses the probiotics' performance within a living organism's gastrointestinal environment. It involves monitoring vitamin absorption, host-microbe interactions, and overall probiotic effectiveness.

6. Safety Evaluation: Throughout the genetic engineering process and subsequent testing phases, safety remains a top priority. Comprehensive safety assessments are conducted to ensure that the engineered E. coli Nissle 1917 strains pose no harm to the host organism. Rigorous safety protocols and ethical considerations guide these evaluations.

7. Regulatory Compliance: As the research progresses toward potential clinical applications, adherence to regulatory guidelines and standards becomes essential. Researchers work closely with regulatory authorities to ensure that the engineered probiotics meet all safety and efficacy requirements for human use.

8. Clinical Trials: Advancing to clinical trials represents the final phase of development. In these trials, the engineered E. coli Nissle 1917 strains are tested in human subjects to evaluate their therapeutic benefits and safety profiles. Clinical trials provide valuable data for assessing the probiotics' impact on vitamin supplementation, gut health, and overall well-being.


The successful completion of these objectives represents a significant milestone in our research journey. By engineering E. coli Nissle 1917 to produce and deliver essential vitamins, we aim to enhance the health and well-being of individuals with vitamin deficiencies and contribute to advancements in probiotic therapy.





Parameters and Variables


For our compartmental model, several parameters and variables were identified based on the gathered information:


  • Production rate of vitamin B12
  • Rate of change of concentration of vitamin B12
  • Production rate of GIF
  • Rate of change of concentration of GIF


  • Populations of E. coli Nissle 1917 (EcN)
  • Permeability of the intestinal wall
  • Serum levels of vitamin B12 (cobalamin)
  • Concentration of GIF in the small intestine
  • Time (hours)

These parameters and variables have been determined through an extensive examination of the existing scientific literature. They serve as the foundational elements in the development of a more comprehensive compartmental in subsequent phases of our research.





Objective 2: Risk Assessment


Risk Assessment:

This comprehensive risk assessment explores the safety and effectiveness of employing E. coli Nissle 1917 as a probiotic to alleviate vitamin B12 deficiencies. A significant emphasis is placed on investigating the interaction between EcN and gastric intrinsic factor (GIF), as understanding this interaction is crucial for ensuring the probiotic's safety and effectiveness. The primary objective of this assessment is to evaluate the safety of EcN, particularly in its interplay with gastric intrinsic factor in the context of vitamin B9 and B12 supplementation. Additionally, this evaluation aims to shed light on potential risks, advantages, and the target population for our probiotic.


Purpose and Intended Use:

The probiotic EcN is intended for specific therapeutic purposes related to gut health. It has been primarily utilized in the treatment of intestinal disorders and inflammatory bowel diseases (IBD) such as ulcerative colitis (Schultz 2008). This particular strain of E. coli exhibits strong antagonistic activity against pathogenic bacteria, making it valuable for combatting foodborne illnesses and symptoms like bloody diarrhea caused by enteropathogenic E. coli strains (International Probiotics Association 2021; Bury et al, 2018). Furthermore, EcN has demonstrated its probiotic properties by regulating gut microbiota composition, inhibiting pathogenic bacterial colonization in the intestine, enhancing the host's intestinal mucosa protective layer, and boosting the immune system (Schultz 2008). These attributes make EcN a valuable tool in managing gastrointestinal conditions and maintaining intestinal health.


Risk Identification:

Hazard identification includes assessing potential hazards related to genetic modification, unintended metabolic byproducts, and unforeseen consequences on host cells or the gut microbiome. Recent research has raised concerns about EcN's genotoxic and mutagenic potential due to the production of colibactin, a genotoxin associated with colorectal cancer (Nougayrède et al., 2021). Chronic exposure to colibactin-producing bacteria, especially in inflamed intestines, may pose a risk (Nougayrède et al., 2021). The long-term effects of EcN supplementation must also be investigated to ensure the safety of the target population.


Risk Analysis:

Risk analysis entails a detailed examination of the frequency and consequences linked to EcN supplementation, with a particular emphasis on the role of gastric intrinsic factor. Dosage, frequency of consumption, and typical user habits are evaluated to estimate the extent and duration of exposure.

EcN is typically administered at a dosage of 10^8 CFU/g per capsule (Sonnenborn & Schulze, 2009). It has shown promise in preventing chronic constipation, normalizing intestinal functions, and increasing bowel movement frequency (Sonnenborn & Schulze, 2009). Some clinical trials reported adverse drug reactions, although serious issues were rare (Sonnenborn & Schulze, 2009). Severe adverse effects, including sepsis and acute pancreatitis, have been noted in isolated cases. Studies have indicated that even high doses of up to nine capsules daily (equivalent to 10^11 CFU/g) were well-tolerated by healthy adults (Sonnenborn & Schulze, 2009). However, exceeding this dosage may have unexplored consequences, and there is no established lethal dose.


Risk Evaluation of Gastric Intrinsic Factor:

This section delves into the specific risks and benefits associated with the introduction of gastric intrinsic factor (GIF) into the human host.

Pernicious anemia is an autoimmune disorder that leads to a decreased ability to absorb vitamin B12 (Vaqar et al., 2023). This lack of B12 absorption is caused by two types of intrinsic factor antibodies that inhibit the binding of vitamin B12 to intrinsic factor (IF) and prevent intestinal absorption of the vitamin B12/IF complex (Vaqar et al., 2023). Therefore, the supplementation of IF to individuals with pernicious anemia would be ineffective and is not recommended. Further, pernicious anemia is associated with the co-occurrence of other autoimmune disorders, and supplementation may result in harm to the individual due to an inappropriate immune response. (Vaqar et al., 2023).


Risk Evaluation of E. coli Nissle 1917:

There are studies outlining the benefits of EcN supplementation in individuals with pernicious anemia. A study conducted on 500 patients with intrinsic factor deficiency showed an average increase of 45% in serum B12 levels after 12 weeks of EcN treatment, indicating a substantial benefit (Devalia 2006). This research reveals that vitamin B12 absorption rates significantly increase in individuals with intrinsic factor deficiencies following EcN supplementation. A study by Kornerup et al. (2019), demonstrated that vitamin B12 deficient patients who were treated with a vitamin B12 supplement combined with IF, experienced elevated levels of holotranscobalamin or ‘active B12’. Therefore, research suggests that GIF supplementation may prove to be beneficial to individuals with vitamin B12 deficiencies due to malabsorption.



This risk assessment report serves as a pivotal tool for evaluating the safety and potential risks entailed in utilizing EcN as a probiotic for vitamin B12 supplementation, with a special emphasis on its interaction with gastric intrinsic factor (GIF). It underscores the significance of identifying hazards, benefits, and the need for continuous monitoring and research. Informed decision-making, supported by this assessment, is instrumental in enhancing EcN's safety profile. To ensure its effectiveness and safety, we encourage further studies and collaborations to investigate intrinsic factor interactions, refine risk assessment protocols, and advance the understanding of EcN as a probiotic for addressing vitamin deficiencies, especially in individuals with intrinsic factor deficiencies.

In our ongoing project, we're now looking ahead to the future and considering the inclusion of B9 to create a combined B9/B12/GIF probiotic. This forward-thinking approach is aimed at exploring the potential benefits of a dual B9 and B12 probiotic, which could offer enhanced health advantages to individuals. We are in the process of assessing B9 as part of our research into this exciting possibility, which could pave the way for innovative probiotic solutions in the future.



Objective 3: Toxicity Testing


Bile salts are synthesized in the liver, stored in the gallbladder, and released into the small intestine. The synthesis, storage, and release of bile salts are integral to the digestive process and the maintenance of a healthy gut environment. Bile salts are robust antimicrobial agents as they hold a pivotal role as integral components of the intestinal innate defense system (Sannasiddappa et al., 2017). They provide the intestine with formidable protection against invading organisms, orchestrating a crucial role in shaping the microbial landscape. Among these factors, the presence of bile salts holds particular significance, as it holds the potential to exert a profound impact on bacterial proliferation (Sannasiddappa et al., 2017). Imbalances in bile salt levels can pave the way for heightened colonization by pathogenic bacteria, highlighting their profound influence on intestinal microbial ecology (Sannasiddappa et al., 2017). Harnessing the attributes of bile salts may also provide a new avenue to explore in the development of therapeutic strategies to control drug-resistant bacteria.


Testing Method:

Within this context, our investigation employs time-course measurements of bacterial viability upon exposure to bile salts, as a method to assess the viability of E. coli Nissle 1917 and our mutant strain with GIF. This involves preparing bacterial cultures, exposing them to various concentrations of bile salts, and then quantifying bacterial colony counts to assess the effects of bile salts on bacterial growth. The assay aims to determine the minimum inhibitory concentration (MIC) of the bile salt, deoxy-cholic acid, and assess its impact on bacterial viability, providing valuable insights into how these substances affect bacterial survival in the gut.




Table 2: Relationship between Bile Salt Concentration and Counted Colonies

Bile salt concentration (mg/μl) Colonies
0.75 302
1.5 300
3 74
7.5 1
22.5 0


Figure 3: Impact of Bile Salts on the Viability of E. coli Nissle 1917

Note: Bacterial cultures were exposed to bile salts, incubated for 30 minutes, plated, and then incubated overnight at 37° C. The bile salts used in the experiments are composed of a conjugate of bile acids with either glycine or taurine and include four distinct bile acids: cholic, deoxycholic, chenodeoxycholic, and lithocholic acids. Bacterial cell density was maintained at 10^8 CFU/ml during the experiments.


When bile salt levels are elevated, as observed in our experiment with increasing concentrations, a marked inhibitory effect on the growth of E. coli Nissle 1917 becomes evident. This concentration-dependent inhibition suggests that higher levels of bile salts are associated with more effective suppression of bacterial proliferation. The results also underscore the importance of balanced bile salt levels in preventing imbalances that could lead to heightened colonization by pathogenic bacteria. Maintaining the appropriate concentration of bile salts within the gut is vital for shaping the microbial landscape in a way that favours a healthy and stable microbiota. Furthermore, our findings hint at the potential therapeutic applications of bile salts in controlling drug-resistant bacteria. Harnessing the antimicrobial attributes of bile salts may offer a promising avenue for the development of novel therapeutic strategies aimed at combating bacterial infections, particularly those resistant to conventional treatments.



Objective 4: Bacterial Colonization Competition Model

Bacterial competition for colonization is a fundamental aspect of microbial ecology within the human body. Within the context of the human gut microbiome, bacterial species compete for attachment sites along the intestinal epithelium and limited resources, ultimately shaping the composition of gut microbiota (Bull and Plumer, 2014). This, in turn, has significant implications for the host’s metabolism, physiology, immune function, and overall health (Bull and Plummer, 2014). Nonpathogenic bacteria, such as E. coli Nissle 1917, have been shown to compete for the same attachment sites as pathogenic bacteria (Bull and Plumer, 2014). Understanding E. coli Nissle 1917’s colonization effectiveness compared to pathogenic bacteria is crucial for assessing its long-term success in the gut environment.


Study and Model Description:

A study by Schierack et al (2011) compared the efficacy of three different E. coli strains in preventing the colonization of Salmonella Typhimurium bacteria of the intestinal epithelial cell line IPEC-J2. We have chosen to focus on the interaction between Salmonella Typhimurium and E. coli Nissle 1917 strain specifically, using values estimated from the experimental results of this study.

This model aims to explore how E. coli Nissle 1917 colonization of the intestinal epithelium can affect the adhesion efficacy of pathogenic Salmonella Typhimurium bacteria. Adhesion efficiency is defined as the adhesion percentage of Salmonella Typhimurium with pre-incubation versus without pre-incubation of E. coli Nissle 1917. The time points correspond to 6 hours pre-incubation of EcN and 2 hours pre-incubation of EcN.


Adhesion Efficacy Data:

Time points and adhesion efficacy values
time_points <- c(-6, -2) # Time points (in hours)
adhesion_efficacy <- c(100, 50) # Adhesion efficacy (%) of Salmonella Typhimurium

# Data frame
adhesion_data <- data.frame(Time = time_points, AdhesionEfficacy = adhesion_efficacy)

# Plot of adhesion efficacy over time
plot(adhesion_data$Time, adhesion_data$AdhesionEfficacy, type = "o", col = "blue",
xlab = "Time (hours)", ylab = "Adhesion Efficacy (%)",
xlim = c(-6, 0), ylim = c(0, 100))
points(adhesion_data$Time, adhesion_data$AdhesionEfficacy, pch = 16, col = "blue")
title(main = expression("Adhesion Efficacy of " ~ italic("Salmonella Typhimurium") ~ " at Different Pre-Incubation Times with EcN"))

Figure 4: Adhesion Efficacy of Salmonella Typhimurium

Note: Adhesion efficacy of Salmonella Typhimurium to IPEC-J2 cells after pre-incubation with EcN. Adhesion efficacy is shown at different pre-incubation times with EcN. 100% adhesion efficacy was measured at 6 hours pre-incubation and 50% adhesion efficacy at 2 hours pre-incubation, relative to adhesion without pre-incubation with EcN.


Key Findings:

Our key findings from this model are that E. coli Nissle 1917 (EcN) demonstrates a competitive advantage as it exhibits a competitive inhibition of colonization by Salmonella Typhimurium. These preliminary results suggest that the relative abundance of EcN may be greater than pathogenic bacteria abundance in individuals treated with our EcN probiotic. Further research is needed to assess the mechanisms of E. coli Nissle 1917’s inhibition of pathogenic bacterial colonization.





Our journey through the world of synthetic biology and probiotics has led us to explore the potential of E. coli Nissle 1917 as a vehicle for delivering essential vitamins B9 and B12 to individuals with deficiencies. By merging science, technology, and innovation, we have embarked on a transformative path that holds promise for improving human health and well-being.

This comprehensive report has offered a glimpse into our research, from the modeling of vitamin B12 production to the genetic engineering of E. coli Nissle 1917. We have shared our assumptions, methodologies, results, and future aspirations with the hope of inspiring further exploration in the field of probiotics and synthetic biology.

Our vision extends beyond the laboratory, reaching into the realm of clinical applications and meaningful contributions to public health. As we stand at the intersection of science and creativity, we invite you to join us on this remarkable journey. Together, we can unlock the full potential of probiotics and create a healthier future for all.

Thank you for accompanying us on this exciting voyage of discovery. The future of synthetic biology and probiotics is bright, and we are committed to making a lasting impact.

With gratitude and enthusiasm,

iGEM Guelph 2023






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