Modeling in synthetic biology is a bridge liaising the past and the future of the experiments. Bringing the elements from physics and chemistry and using the language of math to depict the picture of the design, modeling is indeed a process starting before the beginning of the experiments and finishing after the end of them.
Fig 1. Modeling for systems biology in silico(Carbonell, Pablo. 2019. Metabolic Pathway Design: A Practical Guide.)
In wet experiment, modeling can provide data support for its conduction and assist in the design of experiments. For the difficult goal of wet experiment, the process can be simulated by means of modeling to provide support for the feasibility of the project. The modeling part of SCU-China 2022 this year is divided into two parts: protein structure modeling and metabolic modeling, The former predicts the properties of the proteins in the project and assists in the design of experiments, while the latter is based on GEMs, and describes key pathways with enzyme kinetic models and simulates metabolism flow in a more precisely way at the metabolite level. This modeling part not only plays a role of data support and guidance in the selection of medium for wet experiment, but also simulates the growth of each intestinal microbials, which has a promoting effect on the development of the subject. On this basis, we exploited an algorithm framework for multi-microbials co-culture with wider application value.
We wanted EcN to complete the metabolism of erythritol, then to explore whether EcN can utilize erythritol and its subsequent reactions and metabolism under optimal conditions. We need to evaluate the ability of erythritol transport mechanism to transport erythritol and how SLDH and L-RI can perform a multi-enzyme cascade to achieve the reaction and achieve better results. This will allow us to investigate whether EcN can transport erythritol properly and its subsequent reactions and metabolism.
1.Simulation of erythritol transport mechanism based on molecular docking (here is an eryEFG
in
addition to sugar transport protein).
2. Select linker order based on protein structure prediction and structure comparison to
determine multi-enzyme cascade order.
3. Protein function validation based on CB-Dock2 and SWISS-Dock
1.The erythritol transport mechanism can work normally.
2. ABC transporter protein can also transport erythritol normally (this part of the
experiment has
not been done yet).
3. the correct order of the multi-enzyme cascade
Fig 2. Schematics of the predicted flux distributions in modified EcN(Kim, D et al., 2021. Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917)
This project aims to introduce the pathway of erythritol metabolism into EcN to improve
its
colonization rate in the intestine. After colonization, EcN can secrete
pyrroloquinoline
quineone(PQQ) and other metabolites beneficial to human body.
To verify the feasibility of utilizing independent carbon sources, we conducted modeling.
1. Introducing the pathway of metabolizing erythritol and secreting PQQ into the existing
GEM
model of EcN.
2. Describing the above pathway with the enzyme kinetic model.
3. Carrying out the dimensional refinement for GSM and enzyme kinetic model.
4. Sensitivity analysis.
1. EcN can take advantage of the independent carbon sources to achieve a higher growth
rate.
2. In the metabolic erythritol pathway, the rate-determining step are erythritol absorption
from the extracellular and the isomerization from D-3-tetrulose-4-P to D-erythrose-4-P.
There are distribution of E. coli in the terminal ileum, therefore, if we want EcN to reach
a considerable population size in the intestine, we need to verify that EcN have some
competitive advantage against strains of the same genus at the very least.
At
the same
time,
exploring the interspecific relationship between the bacteria of the same genus is also
conducive to the subsequent analysis
1. Based on the GSM model of EcN constructed in the previous step, we simulated glucose
competition co-culture between EcN and other five E.coli species under anaerobic
conditions.
2. Applying MICOM to explore the interspecific relationship between EcN and five other
E.coli.
1. In the anaerobic environment, when the modified EcN uses erythritol as an independent
carbon source, it has a high competitiveness in the population.
2. Differing from other Escherichia coli, the growth of EcN is favorable to the growth of
other Escherichia coli.
We hope that the microbiota density of the modified EcN in ileum as large as possible, which may help it colonizes. Hence, we need to probe into whether EcN occupies a certain niche among many intestinal resident bacteria in the intestinal environment through modeling.
1. Collecting GEMs of 25 intestinal resident bacteria from AGORA.
2. Constructing the intestinal environment of ordinary Chinese people according to the 2022
dietary recipes for Chinese Residents.
3. Building a co-culture algorithm model for the intestinal microbials of the small
intestine
based on dFBA algorithm.
1. EcN is competitive among the microbials in small intestine.
2. The period of high competitiveness of EcN occurs when the main carbon source is depleted
while erythritol is not.
3. Our self-developed algorithm is effective.
You can try answering the following questions after reading this part.
1. What is GEM?
2. How do GEMs predict the growth rate of microbials or the bounds of other reactions?
3. What is dFBA?