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Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

https://onlinelibrary.wiley.com/doi/10.1111/dgd.12897

A Whole-Cell Computational Model Predicts Phenotype from Genotype

https://www.sciencedirect.com/science/article/pii/S0092867412007763?via%3Dihub#bib43

Comprehensive whole-cell computational models of individual cells

Experimental Procedures

Reconstruction

The whole-cell model was based on a detailed reconstruction of M. genitalium that was developed from over 900 primary sources, reviews, books, and databases.

First, we reconstructed the organization of the chromosome, including the locations of each gene, transcription unit, promoter, and protein-binding site.

Second, we functionally annotated each gene, beginning with the Comprehensive Microbial Resource (CMR) annotation. Functional annotation was primarily based on homologs identified by bidirectional best BLAST. To fill gaps in the reconstructed organism and to maximize the scope of the model, we expanded and refined each gene's annotation using primary research articles and reviews (see Data S1 and Table S3).

Third, we curated the structure of each gene product, including the posttranscriptional and posttranslational processing and modification of each RNA and protein and the subunit composition of each protein and ribonucleoprotein complex.

After annotating each gene, we categorized the genes into 28 cellular processes. We curated the chemical reactions of each cellular process.

The reconstruction was stored in an MySQL relational database. See Data S1 and Table S3 for further discussion of the reconstruction.

Cellular Process Submodels

Because biological systems are modular, cells can be modeled by the following: (1) dividing cells into functional processes; (2) independently modeling each process on a short timescale; and (3) integrating process submodels at longer timescales. We divided M. genitalium into the 28 functional processes illustrated in Figure 1 and modeled each process independently on a 1 s timescale using different mathematics and different experimental data. The submodels spanned six areas of cell biology: (1) transport and metabolism; (2) DNA replication and maintenance; (3) RNA synthesis and maturation; (4) protein synthesis and maturation; (5) cytokinesis; and (6) host interaction. Submodels were implemented as separate classes. See Data S1 for further discussion of each submodel.

Single-Gene Disruptions

Single-gene disruptions were modeled by (1) initializing the cell variables, (2) deleting the in silico gene, and (3) calculating the temporal evolution of the cell state for the first generation postdisruption. We also calculated the mean growth rate of each single-gene disruption strain at successive generations postdisruption. See Data S1 for further discussion of the implementation of disruption strains and their computational analysis.

Computational Simulation and Analysis

We used the whole-cell model to simulate 192 wild-type cells and 3,011 single-gene deletants. All simulations were performed with MATLAB R2010b on a 128 core Linux cluster. The predicted dynamics of each cell were logged at each time point and subsequently analyzed using MATLAB. See Data S1 for further discussion.

Source Code

The model source code, training data, and results are freely available at SimTK (SimTK: Whole-Cell Computational Model of Mycoplasma genitalium: Project Home

).

Extended Experimental Procedures

The whole-cell model was constructed by integrating submodels of 28 essential cellular processes. The model includes over 1,900 quantitative parameters and is based on over 900 primary research articles, reviews, books, and databases.

Data S1 describes the mathematical, computational, and experimental methods of this study. Chapter 1 of Data S1 outlines the whole-cell model methods. Chapters 2 and 3 of Data S1 discuss the mathematics of each cellular variable and process submodel. Appendix A of Data S1 discusses the computational implementation of the whole-cell model and the in silico reconstruction. Chapter 4 of Data S1 outlines the experimental methods of this study. Table S3 details the M. genitalium reconstruction on which the whole-cell model is based. The whole-cell model source code and detailed results, as well as a guide to installing and running the model and knowledge base, are freely available at SimTK: SimTK: Whole-Cell Computational Model of Mycoplasma genitalium: Project Home

.

Simultaneous cross-evaluation of heterogeneous E. Coli datasets via mechanistic simulation

https://www.science.org/doi/10.1126/science.aav3751

WholeCellEcoliRelease/docs/README.md at master · CovertLab/WholeCellEcoliRelease · GitHub
Linux安装pyenv（python多版本）教程_pyvenv.cfg怎么安装-CSDN博客
WholeCellEcoliRelease/README.md at master · CovertLab/WholeCellEcoliRelease · GitHub

Setup

See docs/README.md for docs on how to set up and run the model.

In short, there are two alternative ways to set up to run the model: in a Docker container or in a pyenv Python virtual environment. Docker containers are easier to build and isolated from your development computer, but they run slower. (PyCharm should support debugging into a Docker container but we haven't tested that.) pyenv virtual environments take more steps to build and depend on your computer's OS, but are lighter weight and easier for debugging. With Docker, you can start running a simulation with these steps:
Create a github personal access token with at least the read:packages permission selected.
Place the token in github_personal_access_token.txt.
Log in to docker.pkg.github.com:
cat github_personal_access_token.txt | docker login https://docker.pkg.github.com -u USERNAME --password-stdin
You should see an output message like Login Succeeded

# Pull the Docker image
docker pull docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full:latest
# Run the Docker container，--name=wcm：为容器指定名称为 wcm可进行修改，-it：这两个参数结合起来让 Docker 提供一个交互式终端,-i 保证容器的标准输入（STDIN）保持打开，-t 分配一个伪终端, docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full：这是要运行的 Docker 镜像。--rm 标志用于在容器退出时自动删除容器，移除该标志将使容器在停止后依然保持存在，从而保留容器中的文件和更改。
## 示例
docker run --name=wcm -it --rm docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full
## 实际操作，--name=wcm_test
docker run --name=wcm_test1 -it docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full
# Inside the container, run the model
mkdir -p /wcEcoli/out/
python runscripts/manual/runSim.py
## 后台运行
mkdir -p /wcEcoli/out/simulations
nohup python runscripts/manual/runParca.py /wcEcoli/out/simulations > /wcEcoli/out/runParca.log 2>&1 &

数据持久化：虽然去除 --rm 可以让容器在关闭后保留，但容器内的数据在容器被删除后依然会丢失。为了持久化数据，可以使用 Docker 卷或绑定挂载来持久化数据：
docker run --name=wcm -it -v /宿主机/路径:/容器内/路径 docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full
# 这里的 -v 参数将宿主机的目录挂载到容器内，确保数据的持久化。
启动已停止的容器：如果您的容器已经运行过并被停止，您可以使用以下命令重新启动该容器：
docker start -i -a wcm
这将以交互模式重新连接到容器的 STDIN。

查看容器日志：如果容器正在运行，您可以通过以下命令查看容器的输出：
docker logs wcm
如果容器已经停止，这个命令也可以用来检查容器的日志文件。

通过这些步骤和设置，您可以确保 Docker 容器在运行后不会删除内部文件，并且可以重新进入和访问这些文件

Quick start

# cd to the top level of your wcEcoli directory.
# Set the $PYTHONPATH
export PYTHONPATH="$PWD"
# In the wcEcoli directory, compile the Cython code
make clean compile

Ways to run the model

Use the manual runscripts.

They run each step directly in-process, which is particularly handy to use with a debugger. But you're responsible for properly sequencing all the steps: parameter calculation, cell simulation generations, and analyses. The manual runscripts work with a Docker container and also with a pyenv virtual environment.

Queue up a Fireworks workflow, then run it.

You configure it for the desired variants, number of generations, and other options, then Fireworks will automatically run all the steps including parameter calculation, simulations, and all the analysis plots.

The workflow tasks can be distributed over multiple processes or even multiple computers, but they must all access a shared file system such as NFS and the (or copies of the) pyenv virtual environment. We have not tested Fireworks with Docker containers.

Run on the Google Cloud Platform using Docker containers and our custom workflow software.

Use the multi-scale agent-based framework.

This can run several cells interactively on a simulated microscope slide.

Using the manual runscripts

# To run the parameter calculator (ParCa), which is needed to prepare data for the simulation:
python runscripts/manual/runParca.py [-h] [--cpus CPUS] [sim_outdir]
## 实际运行（默认参数）
mkdir -p /wcEcoli/out/simulations
nohup python runscripts/manual/runParca.py /wcEcoli/out/simulations > /wcEcoli/out/runParca.log 2>&1 &# To simulate one or more cell generations with optional variants:
python runscripts/manual/runSim.py [-h] [--variant VARIANT_TYPE FIRST_INDEX LAST_INDEX] [--generations GENERATIONS] [--init-sims INIT_SIMS] [--seed SEED] [sim_dir]
## 实际运行（默认参数）
mkdir -p /wcEcoli/out/sim_dir
cp -r /wcEcoli/out/simulations/* /wcEcoli/out/sim_dir/
nohup python runscripts/manual/runSim.py sim_dir > /wcEcoli/out/sim_dir/runSim.log 2>&1 &# To interactively select from the data that is saved during a simulation for visualization:
python runscripts/manual/analysis_interactive.py [-h] [sim_dir]
## 实际运行（默认参数）
nohup python /wcEcoli/runscripts/manual/analysis_interactive.py /wcEcoli/out/sim_dir > /wcEcoli/out/sim_dir/analysis_interactive.log 2>&1 &

Running the command without any arguments will populate drop down menus for each set of simulations in out/ where you can select the desired variant/seed/generation/daughter and view the available values that are saved during simulations. Some simple data processing options are available. This interface mainly lets you select time traces or create scatter plots that can be used to compare different variants, generations, etc.

To run predefined analysis plots on the simulation output in a given sim_dir (use the -h parameter to get complete help on the command line options):

# analysisVariant.py
python runscripts/manual/analysisVariant.py [-h] [--plot PLOT [PLOT ...]] [--cpus CPUS] [sim_dir]
# 实际运行
nohup python runscripts/manual/analysisVariant.py /wcEcoli/out/sim_dir > /wcEcoli/out/sim_dir/analysisVariant.log 2>&1 &# analysisCohort.py
python runscripts/manual/analysisCohort.py [-h] [--plot PLOT [PLOT ...]] [--cpus CPUS] [--variant-index VARIANT_INDEX] [--variant-range START_VARIANT END_VARIANT] [sim_dir]
# 实际运行
nohup python runscripts/manual/analysisCohort.py /wcEcoli/out/sim_dir > /wcEcoli/out/sim_dir/analysisCohort.log 2>&1 &# analysisMultigen.py
python runscripts/manual/analysisMultigen.py [-h] [--plot PLOT [PLOT ...]] [--cpus CPUS] [--variant-index VARIANT_INDEX] [--seed SEED] [--variant-range START_VARIANT END_VARIANT] [--seed-range START_SEED END_SEED] [sim_dir]
# 实际运行
nohup python runscripts/manual/analysisMultigen.py /wcEcoli/out/sim_dir > /wcEcoli/out/sim_dir/analysisMultigen.log 2>&1 &# analysisSingle.py
python runscripts/manual/analysisSingle.py [-h] [--plot PLOT [PLOT ...]] [--cpus CPUS] [--variant-index VARIANT_INDEX] [--seed SEED] [--generation GENERATION] [--daughter DAUGHTER] [--variant-range START_VARIANT END_VARIANT] [--seed-range START_SEED END_SEED] [--generation-range START_GENERATION END_GENERATION] [sim_dir]
# 实际运行
nohup python runscripts/manual/analysisSingle.py /wcEcoli/out/sim_dir > /wcEcoli/out/sim_dir/analysisSingle.log 2>&1 &

If you default the analysis parameters, these scripts will pick the latest simulation directory, the first variant, the first generation, and so on. To get full analyses across all variants, generations, etc., run:

analysisVariant.py
analysisCohort.py for each --variant_index you simulated
analysisMultigen.py for each combination of --variant_index and --seed you simulated
analysisSingle.py for each combination of --variant_index, --seed, and --generation you simulated

重新进入之前运行的名为 wcm_new 的 Docker 容器
docker exec -it wcm_test1 /bin/bash
# 这里使用的 -it 参数是让 Docker 提供一个交互式文本界面，wcm_new 是您的容器名称，而 /bin/bash 是大多数 Linux 系统中的 shell 程序。
如果容器已经停止运行，您需要先启动容器，然后才能进入。要检查容器的状态，可以使用以下命令：
docker ps -a
这将列出所有 Docker 容器，包括正在运行的和已停止的。如果 wcm_new 显示为已停止，您可以使用以下命令启动它：
docker start wcm_test1
启动后，您可以使用 docker exec 命令进入容器。

请注意，--rm 标记通常用于在容器退出时自动清理容器文件系统，这意味着当容器停止时，所有的更改和数据都会丢失。如果您的容器确实使用了 --rm 标记并且已经停止了，那么您将无法再次进入它，因为它已经被删除了。

如果容器是用 --rm 标记启动的，并且您希望保留容器的数据，您可以考虑在启动时不使用 --rm 标记，或者将重要的数据持久化到 Docker 卷中。

The consensus genome-scale metabolic model of Saccharomyces cerevisiae

The consensus genome-scale metabolic model of Saccharomyces cerevisiae | yeast-GEM

https://github.com/SysBioChalmers/yeast-GEM/releases/

https://github.com/SysBioChalmers/yeast-GEM/blob/main/.github/CONTRIBUTING.md

https://www.embopress.org/doi/full/10.1038/s44320-024-00060-7

Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network | BMC Systems Biology | Full Text

Here is how to set up yeast-GEM for local development to contribute smaller features or changes that you can implement yourself:

1. First of all, make sure that you have all requirements for contributing to yeast-GEM.
MATLAB-based
If you want to contribute to the development of yeast-GEM, or otherwise want to run any of the provided MATLAB functions, then the following software is required:

RAVEN Toolbox version 2.8.3 or later GitHub - SysBioChalmers/RAVEN: The RAVEN Toolbox for genome scale model reconstruction, curation and analysis.
Python-based
Contribution via python (cobrapy) is not yet functional. In essence, if you can retain the same format of the model files, you can still contribute to the development of yeast-GEM. However, you cannot use the MATLAB functions.

If you want to use any of the provided Python functions, you may create an environment with all requirements:
# install all dependencies
pip install -r code/requirements/requirements.txt  
# create a .env file for locating the root
touch .env 
If you want to locally run memote run or memote report history, you should also install git lfs, as results.db (the database that stores all memote results) is tracked with git lfs.
2. Fork the yeast-GEM repository on GitHub (go to GitHub - SysBioChalmers/yeast-GEM: The consensus GEM for Saccharomyces cerevisiae & click on the upper right corner).

WholeCellKB

WholeCellKB: model organism databases for comprehensive whole-cell models | Nucleic Acids Research | Oxford Academic
GitHub - CovertLab/WholeCellKB: WholeCellKB is a collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms.

WholeCellKB is a collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms. WholeCellKB provides comprehensive, quantitative descriptions of individual species including:

Cellular chemical composition,
Growth medium composition,
Gene locations, lengths, and directions,
Transcription unit organization and transcriptional regulation,
Macromolecule composition,
Reaction stoichiometry, kinetics, and catalysis, and
Extensive links and cross-links to all references used to construct each model organism database.

See the tutorial and the about page for more information about WholeCellKB how to use WholeCellKB as well as how it was design, implemented, and curated.

AI赋能生命科学：打造虚拟细胞模型，开启生物学研究新纪元

https://arxiv.org/pdf/2409.11654

Simularium

Simularium

The Simularium Viewer: an interactive online tool for sharing spatiotemporal biological models | Nature Methods
https://github.com/simularium
SpringSaLaD – VCell- Modeling & Analysis Software

https://github.com/achattaraj/Ksp_phase_separation

https://www.youtube.com/watch?v=usPhGggnIkU&list=PLHUVSEH0UcxgbXJL5UHgvgCDGSJCEr6Nr&index=6https://emonet.biology.yale.edu/sites/default/files/files/NFsim_manual_v1_11.pdfhttps://www.youtube.com/watch?v=usPhGggnIkU&list=PLHUVSEH0UcxgbXJL5UHgvgCDGSJCEr6Nr&index=6

https://simularium.github.io/subcell-website/

The Simularium Viewer makes it easy to share and interrogate interactive 3D visualizations of biological simulation trajectories and related plots directly in a web browser. Its primary goal is to facilitate collaborations among experimental and computational biologists by removing major challenges to sharing, accessing, and comparing simulation results.

cellPack

https://github.com/mesoscope/cellpack/blob/main/README.md

Welcome to cellPack’s documentation! — cellPack 1.0.8 documentation

Allen Cell Explorer

https://www.allencell.org/

https://www.allencell.org/publications.html

https://www.allencell.org/what-we-do.html

Overview of the Allen Institute for Cell Science workflow

The mission of the Institute is to understand and predict cell behaviors. The current project aims to:

create a stem cell state space by conjoining genomic, imaging, and phenotypic data from cells that are differentiating, that are at different points in the cell cycle, or that have been perturbed
determine the mechanisms by which cells transition from state to state
determine how the expression of a subset of genes produces key cellular states.

Our approaches include single cell gene expression and live cell imaging of the major molecular machines (organelles) and activities to establish a physical platform for developing predictive and mechanistic models. To do this, we study human induced pluripotent stem cells that are genome edited to express proteins tagged with fluorescence reporters using high replicate quantitative microscopic observations, image analysis, and computational models that can both predict and reveal mechanistic information. In addition, we have developed tools for observing and interrogating the images from both these cells and the outputs from the computational models.

We have implemented this ambitious project through an integrated workflow employing interdisciplinary teams all working toward a common goal.

Cell Feature Explorer

The Cell Feature Explorer (CFE) is an online tool to access segmented and processed images of over 200,000 cells as curated datasets. Each dataset has specific measured features, such as cellular volume and mitosis stage that are described in the user interface and detailed in publications associated with each dataset. The CFE is composed of a plot, where each cell is graphed by these measured features, and a 3D viewer for in-depth visual analysis.

Longevity.Omics

The launch of groundbreaking Longevity.Omics Platform: Pioneering a new era in AI-enabled precision health care
Longevity Omics

Human BioMolecular Atlas Program (HuBMAP)

HuBMAP Data Portal

Human BioMolecular Atlas Program

Inaugurated in 2018, the Human BioMolecular Atlas Program (HuBMAP) endeavours to construct comprehensive spatial maps that feature a range of biomolecules such as RNA, proteins, and metabolites in human organs at single-cell resolution. This collection features the research, datasets, methods and tools generated by this project, accompanied by a Perspective, a News and Views, and links to other resources.

3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging | Communications Biology
Companion Website for “3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging” | ㅤ