Background

During the Conference of China iGEMer Community (CCiC), we observed that numerous teams necessitate intricate calculations about the anticipated content of products, while the metabolic processes associated with various projects are notably complicated, disorderly, and exceedingly challenging. Our project focuses on the computation and prediction of the production of DHA (docosahexaenoic acid). To streamline the project workflow and make it convenient,we developed software POMPC（Prediction Of Metabolism Pathway Changes） that allows direct access to the content of the expected product by inputting the relevant substrate concentration. This innovative tool significantly alleviates the complexity of work, liberating valuable time and energy for individuals involved.

Highlights

• Non-professional users friendly: inputting substrate can retrieve pertinent pathways directly, making it easy for individuals.
• Better performance: by modifying the initial value of substrate concentration, one can readily acquire the anticipated concentration curve of the product, rendering a substantial enhancement in performance compared to the original approach.
• Wide range of applications: POMPC is suitable for all members who need to view and analyze various pathways.
• No networking required: the application solely necessitates an internet connection during its initial use, afterwards it can be used without network connections.

Functions

• Retrieve the relevant pathways the substrate involved in.
• Show how the concentration of each substance changes over time during routine metabolic processes.
• Show the change of the curve of substance concentration after modifying the initial concentration value of the substrate.

Methods

The main use flow of the software is as follows:

The whole process of the usage of POMPC:

How To Build:

1. Data Storage

We collect the models’ data of bacterial metabolism on the Biomodels^[1] website, a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats, and provides the systems modelling community with reproducible, high-quality, freely-accessible models published in the scientific literature. Many forms of files of models are automatically generated with the System Biology Format Converter from SBML files^[2]. We use Excel as a database, collect and sort the substrates corresponding to the model, forming a data table. All the files have been uploaded on our GitLab account.

2. Data Connection

We use SSH keys to connect to the data uploaded to GitLab. SSH is a network protocol used for encrypted logins between computers.

An SSH key pair makes it easy to log in to an SSH server without having to enter a password. SSH key pairs always come in pairs, one public key and one private key. The public key can be placed freely on the SSH server that you want to connect to, while the private key needs to be kept safe.

We provide a detailed online documentation for you setting SSH connection.

What to watch for:

The first time you use it, you need to equip the SSH keys on the computer to clone the files, if it doesn’t work, you can also download file CAU_database directly on GitLab and unzip it to your home folder (the whole filepath must be C:\Users\userHomeDir\CAU_database\Soft_ware), it will still work normally.

3. Data Retrieval

After cloning the files to our home folder, We run the MATLAB code we wrote to let the POMPC read the substrate name, search it in Excel database, and find the substrate name. Finally, POMPC pops up all metabolic pathways associated with the substrate.

4. Data Modification

After choosing the metabolic pathway we want, we use the function to read all the contents of the specified file, store it in the variable, find the substrate name we input, and change the initial value with the value we assign.

5. Data Presentation

Finally, we can run the modified file to show the changes over time of the various substances in the selected pathway, including the substrates we have selected. After the file run, the previously modified value of the substrate is restored again so that it will be in the normal standard state for the next call.

Results

Software Interface Design

We use MATLAB App designer to design POMPC. The design interface is as follows:

On the initial page, enter the substrate you want to look for in the search box and click “Search”. Searching the entered substrate name in the database, the interface will jump to another interface, showing the relevant metabolic pathways. The following substrate Extracellular Glucose is used as an example:

Click “Search”:

After that, the interface jumps to the metabolic models that are related to the substrate retrieved from the database. Then select the metabolic model you need, and click “Confirm”. The system will retrieve the metabolic pathway file, in order for subsequent modification of the initial concentration of the substrate. There is a back button at the top right, you can click it to go back to the previous page.

Enter the expected substrate concentration, take 1 mmol/L as an example, then click “Confirm”.

Finally, the change of the concentration of each substance over time is presented.

When the concentration of substrate is changed, the concentration of each product will change accordingly. For example, when the substrate concentration is changed from 1 mmol/L to 3 mmol/L, the curve change correspondingly.

Focusing on area full of application value, such as pharmacokinetics, we found this model of statin transport in human hepatocytes when we searched models. Statins, β-hydroxymethylglutaryl-monoyl-coenzyme A (HMGCOA) reductase inhibitors, are widely used in clinical practice as highly effective lipid-lowering drugs. Its lipid-lowering effect is to inhibit the synthesis of cholesterol by inhibiting HMGCOA reductase (the rate-limiting enzyme in the cholesterol synthesis pathway), resulting in the reduction of total cholesterol and low-density lipoprotein cholesterol (LDL-C) in the blood. Atorvastatin (AS), a model drug, is one of the most commonly used 3-hydroxymethylglutaryl-coa reductase inhibitors (statins), which can be chemically or enzymatically hydrolyzed by putative PON to the corresponding acid. The acid and lactone further nonspecifically bind to macromolecules in human hepatocytes, thereby reducing blood lipids. Therefore, statins reduce cholesterol synthesis and also stimulate the absorption of low-density lipoprotein cholesterol in the blood. Although they are relatively safe agents, the lipid-lowering effects of statins are insufficient in some patients, and unpredictable drug-drug interactions, as well as hepatic and extrahepatic adverse effects, including hepatotoxicity, myopathy, and rare but serious rhabdomyolysis, may occur. Hence, we tried to analyze the factors affecting the action of statins by debugging the parameters of Statin pharmacokinetics model.

By changing the parameters in the Statin pharmacokinetics model, we found that the change in the value of substrate ASLoOH-m had a great influence on the results of the product. When the initial concentration of ASLoOH-m was raised, the content of AS was very low, while the concentration of hydroxylated variants of AS increased significantly halfway through. Then, a suppose came into our mind: ASLoOH-m promoted the hydroxylation process of AS, resulting in a certain reduction of drug efficacy or some adverse reactions.

To verify the result, we obtained relevant conclusions by searching the literature, and found that ASLoOH-m was indeed an important variant in the process of drug metabolism^[3]. When the concentration of AsLOOH-M increased, the hydroxylation degree of AS substance increased, which led to the decrease of drug efficacy. This was consistent with our conclusion from the curve, indicating that the product prediction software POMPC showed a great prospect. If there are teams concerned about such statins drug action diseases in the future, we hope that POMPC can let them analyze the interaction of substances in metabolism like we do, and have an initial assessment, so as to provide them with timely and convenient help.

When the input substrate is changed, there are corresponding different pathways. For example, when the substrate is changed to theta, there will be a circuit of G1 phase regulation, which is another pathway.

We also added some feedback services to make the users understand the situation more clearly.

For example:

When the input substrate is incorrect, it cannot be found in the database with "Found no substrate" tip.

A prompt will pop up when some errors occurred during the clone of repository.

By selecting different substrates and inputting different concentration values, the curves of different concentrations of products over time can be obtained, so as to know the expected product content without intricate metabolic deduction and time-consuming literature search work.

Download Method

We offer three ways depending on various conditions to download our App:

1. MATLAB App

For users who have installed MATLAB on their computers. After installation, open the APP list in the MATLAB main interface and click to use it.

2. Web App

For users who do not equip MATLAB, the MATLAB Runtime program needs to be installed on the user's computer. For details about the download of the program, please refer to Matlab Installer Documentation.

3. Standalone desktop App

This installation does not require any new programs or software to be installed on the user's computer, which is suitable for any conditions.

Contributions

The construction of POMPC will greatly facilitate our project to predict the final DHA output and how much DHA output will be obtained with high probability by adding different concentrations of substrates, providing a lot of insights for the early stage of the experiment, as well as helping many teams who need to acquire the output of the product of their projects to provide predictions and insights, which have a fairly wide range of applications and prospects. We hope our software will make it easier and smoother to predict the content of a product.

Discussion

In order to create a more powerful and useful software, we have went through four or five iterations of POMPC. Initially, it could only run files in existing folders on the computer. Subsequently, the SSH link was applied, making it easier to copy data remotely. Moreover, considering that users tend to investigate different pathways on one substrate, we constructed a function capable of showing related pathways with input substrate. Then we came into deeper thinking: it would be quite limited to conduct quantitative analyses when the concentration of substrate was unable to modify. So, we worked hard to achieve the goal of modifying the substrate concentration to obtain the curves of concentrations of different products. Finally, we beautified the entire interface to make it look more beautiful and comfortable, and added legends to each graph that was not present in the original file. Now, we are constantly expanding the database to add immune regulation, gene expression and cell differentiation pathways on the basis of metabolic pathways, making the databases more comprehensive and powerful.

For specific iteration, please read our engineering.

Although we have built a very convenient system, unfortunately, the amount of data we have collected is far from enough to allow users to retrieve almost all pathways.

In the future, we will try to accumulate and add more models to make POMPC more comprehensive and durable.

The detailed codes of POMPC are all stored in GitLab.