An introduction to whole-cell modeling
0.0.1
  • 1. Introduction
    • 1.1. Motivation for WC modeling
      • 1.1.1. Biological science: understand how genotype influences phenotype
      • 1.1.2. Medicine: personalize medicine for individual genomes
      • 1.1.3. Synthetic biology: rationally design microbial genomes
    • 1.2. The biology that WC models should aim to represent and predict
      • 1.2.1. Phenotypes that WC models should aim to predict
      • 1.2.2. Physics and chemistry that WC models should aim to represent
    • 1.3. Fundamental challenges to WC modeling
      • 1.3.1. Integrating molecular behavior to the cell level over several spatiotemporal scales
        • 1.3.1.1. Sensitivity of phenotypic predictions to molecular parameter values
        • 1.3.1.2. High computational cost of simulating large fine-grained models
      • 1.3.2. Assembling a unified molecular understanding of cells from imperfect data
        • 1.3.2.1. Incomplete data
        • 1.3.2.2. Imprecise and noisy data
        • 1.3.2.3. Heterogeneous experimental methods
        • 1.3.2.4. Heterogeneous organisms and environmental conditions
        • 1.3.2.5. Siloed data
        • 1.3.2.6. Insufficient annotation
      • 1.3.3. Selecting, calibrating and validating high-dimensional models
    • 1.4. Feasibility of WC models
      • 1.4.1. Experimental methods, data, and repositories
        • 1.4.1.1. Measurement methods
        • 1.4.1.2. Data repositories
        • 1.4.1.3. Prediction tools
      • 1.4.2. Modeling and simulation tools
        • 1.4.2.1. Data aggregation and organization tools
        • 1.4.2.2. Model design tools
        • 1.4.2.3. Model selection tools
        • 1.4.2.4. Model refinement tools
        • 1.4.2.5. Model formats
        • 1.4.2.6. Simulation algorithms
        • 1.4.2.7. Simulation experiment formats
        • 1.4.2.8. Simulation tools
        • 1.4.2.9. Calibration tools
        • 1.4.2.10. Verification tools
        • 1.4.2.11. Simulation results formats
        • 1.4.2.12. Simulation results databases
        • 1.4.2.13. Simulation results analysis
      • 1.4.3. Models of individual pathways and model repositories
        • 1.4.3.1. Models of individual pathways
      • 1.4.4. Models of multiple pathways
        • 1.4.4.1. Model repositories
    • 1.5. Emerging principles and methods for WC modeling
      • 1.5.1. Principles of WC modeling
      • 1.5.2. Methods for WC modeling
        • 1.5.2.1. Data aggregation, standardization, and integration
        • 1.5.2.2. Model design
        • 1.5.2.3. Model calibration
        • 1.5.2.4. Model verification and validation
        • 1.5.2.5. Network-free multi-algorithmic simulation
        • 1.5.2.6. Visualization and analysis of simulation results
    • 1.6. Latest WC models and their limitations
      • 1.6.1. Coarse-grained models
      • 1.6.2. Genomically-centric bottom-up fine-grained models
      • 1.6.3. Physiologically-centric top-down fine-grained models
      • 1.6.4. Spatially-centric bottom-up fine-grained models
      • 1.6.5. Hybrid models
    • 1.7. Bottlenecks to more comprehensive and predictive WC models
      • 1.7.1. Inadequate experimental methods and data repositories
      • 1.7.2. Incomplete, inconsistent, scattered, and poorly annotated pathway models
        • 1.7.2.1. Incomplete models
        • 1.7.2.2. Poorly validated and unreliable models
        • 1.7.2.3. Inconsistent models
        • 1.7.2.4. Unpublished and scattered models
        • 1.7.2.5. Incompletely annotated models
      • 1.7.3. Inadequate software tools for WC modeling
      • 1.7.4. Inadequate model formats
      • 1.7.5. Lack of coordination among the cell modeling community
    • 1.8. Technologies needed to advance WC modeling
      • 1.8.1. Experimental methods for characterizing cells
      • 1.8.2. Tools for aggregating, standardizing, and integrating heterogeneous data
      • 1.8.3. Tools for scalably designing models from large datasets
      • 1.8.4. Rule-based format for representing models
      • 1.8.5. Scalable network-free, multi-algorithmic simulator
      • 1.8.6. Scalable tools for calibrating models
      • 1.8.7. Scalable tools for verifying models
      • 1.8.8. Additional tools that would help accelerate WC modeling
    • 1.9. A plan for achieving comprehensive WC models as a community
      • 1.9.1. Phase I: Piloting the core technologies and concepts of WC modeling
      • 1.9.2. Phase II: Piloting collaborative WC modeling
      • 1.9.3. Phase III: Community modeling and model validation
    • 1.10. Ongoing efforts to advance WC modeling
      • 1.10.1. Genomically-centric models
        • 1.10.1.1. Mycoplasma pneumoniae
        • 1.10.1.2. Escherichia coli
        • 1.10.1.3. H1 human embryonic stem cells (hESCs)
      • 1.10.2. Physiologically-centric, spatially-centric, and hybrid models
      • 1.10.3. Technology development
        • 1.10.3.1. Data aggregation
        • 1.10.3.2. Model representation
        • 1.10.3.3. Simulation of genomically-centric models
    • 1.11. Resources for learning about WC modeling
      • 1.11.1. Summer schools
      • 1.11.2. Online forum
    • 1.12. Outlook
  • 2. Foundational concepts and skills for computational biology
    • 2.1. Typing
    • 2.2. Software engineering
      • 2.2.1. An introduction to Python
        • 2.2.1.1. Key concepts
        • 2.2.1.2. Installing Python and Python development tools
        • 2.2.1.3. Data types
        • 2.2.1.4. Variables
        • 2.2.1.5. Boolean statements
        • 2.2.1.6. If statements
        • 2.2.1.7. Loops
        • 2.2.1.8. Functions
        • 2.2.1.9. Classes
        • 2.2.1.10. Modules
        • 2.2.1.11. String formatting
        • 2.2.1.12. Printing to the command line
        • 2.2.1.13. Reading and writing to/from files with csv and pyexcel
        • 2.2.1.14. Warnings and exceptions
        • 2.2.1.15. Other Python languages features
        • 2.2.1.16. Exercises
      • 2.2.2. Numerical computing with NumPy
        • 2.2.2.1. Array construction
        • 2.2.2.2. Concatenation
        • 2.2.2.3. Query the shape of an array
        • 2.2.2.4. Reshaping
        • 2.2.2.5. Selection and slicing
        • 2.2.2.6. Transposition
        • 2.2.2.7. Algebra
        • 2.2.2.8. Trigonometry
        • 2.2.2.9. Other mathematical functions
        • 2.2.2.10. Data reduction
        • 2.2.2.11. Random number generation
        • 2.2.2.12. NaN and infinity
        • 2.2.2.13. Exercises
        • 2.2.2.14. NumPy introduction for MATLAB users
      • 2.2.3. Plotting data with matplotlib
        • 2.2.3.1. Plot types
      • 2.2.4. Developing database, command line, and web-based programs with Python
        • 2.2.4.1. Using databases with SQLAlchemy and SQLite
        • 2.2.4.2. Building command line programs with Cement
        • 2.2.4.3. Building web-based programs with Flask
      • 2.2.5. Writing code for Python 2 and 3
      • 2.2.6. Organizing Python code into functions, classes, and modules
      • 2.2.7. Structuring Python projects
      • 2.2.8. Revisioning code with Git, GitHub, and Meld
        • 2.2.8.1. Installing and configuring the required software
        • 2.2.8.2. Instructions
      • 2.2.9. Testing Python code with unittest, pytest, and Coverage
        • 2.2.9.1. Required packages
        • 2.2.9.2. File naming and organization
        • 2.2.9.3. Writing tests
        • 2.2.9.4. Testing stochastic algorithms
        • 2.2.9.5. Testing standard output
        • 2.2.9.6. Testing cement command line programs
        • 2.2.9.7. Testing for multiple version of Python
        • 2.2.9.8. Running your tests
        • 2.2.9.9. Analyzing the coverage of your tests
        • 2.2.9.10. Additional tutorials
      • 2.2.10. Debugging Python code using the PyCharm debugger
      • 2.2.11. Documenting Python code with Sphinx
        • 2.2.11.1. Required packages
        • 2.2.11.2. File naming and organization
        • 2.2.11.3. Generating a Sphinx configuration file
        • 2.2.11.4. Writing documentation
        • 2.2.11.5. Compiling the documentation
      • 2.2.12. Continuously testing Python code with CircleCI, Coveralls, Code Climate, and the Karr Lab’s dashboards
        • 2.2.12.1. Required packages
        • 2.2.12.2. Using the CircleCI cloud-based continuous integration system
        • 2.2.12.3. Code Climate
        • 2.2.12.4. Coveralls
        • 2.2.12.5. Karr Lab test results dashboard (tests.karrlab.org)
        • 2.2.12.6. Karr Lab software development dashboard (code.karrlab.org)
      • 2.2.13. Distributing Python software with GitHub, PyPI, Docker Hub, and Read The Docs
        • 2.2.13.1. Required packages
        • 2.2.13.2. Prepare your package for distribution
        • 2.2.13.3. Distributing source code with GitHub
        • 2.2.13.4. Distributing Python packages with PyPI
        • 2.2.13.5. Distributing containers with Docker Hub
        • 2.2.13.6. Distributing documentation with Read The Docs
      • 2.2.14. Recommended Python development tools
        • 2.2.14.1. Installation
      • 2.2.15. Comparison between Python and other languages
        • 2.2.15.1. Python vs MATLAB
    • 2.3. Linux
      • 2.3.1. How to build a Linux Mint virtual machine with Virtual Box
        • 2.3.1.1. Instructions
        • 2.3.1.2. Additional tutorials
      • 2.3.2. How to build a Ubuntu Linux image with Docker
        • 2.3.2.1. Required packages
        • 2.3.2.2. Configuring a image
        • 2.3.2.3. Building a image
        • 2.3.2.4. Uploading images to Docker Hub
        • 2.3.2.5. Listing existing images
        • 2.3.2.6. Removing images
        • 2.3.2.7. Running an image
      • 2.3.3. An introduction to Linux Mint
        • 2.3.3.1. Running command line programs
        • 2.3.3.2. Getting help for command line programs
        • 2.3.3.3. Installing, upgrading, and uninstalling software
        • 2.3.3.4. Additional tutorials
    • 2.4. Version and sharing data with Quilt
      • 2.4.1. Overview
      • 2.4.2. Using Quilt
    • 2.5. Scientific communication: papers, presentations, graphics
      • 2.5.1. Writing manuscripts
        • 2.5.1.1. The publication process
        • 2.5.1.2. How to write a manuscript
        • 2.5.1.3. How to write an abstract
        • 2.5.1.4. How to format a manuscript for submission
        • 2.5.1.5. How to write a response to reviewer critiques
      • 2.5.2. Reviewing manuscripts
        • 2.5.2.1. Becoming a reviewer
        • 2.5.2.2. Timeline
        • 2.5.2.3. Format
        • 2.5.2.4. More information
      • 2.5.3. Making posters
        • 2.5.3.1. Abstracts
        • 2.5.3.2. Content
        • 2.5.3.3. Layout
        • 2.5.3.4. Formatting
        • 2.5.3.5. Software tools
      • 2.5.4. Making presentations
        • 2.5.4.1. Presentation structure
        • 2.5.4.2. Slide design
        • 2.5.4.3. Software tools
        • 2.5.4.4. Further information
      • 2.5.5. Visualizing data
        • 2.5.5.1. Interactive exploratory data visualization
        • 2.5.5.2. Software tools
        • 2.5.5.3. Exercises
        • 2.5.5.4. Further information
      • 2.5.6. Formatting textual documents with LaTeX
        • 2.5.6.1. Required software
        • 2.5.6.2. Tutorial
        • 2.5.6.3. Online, collaborative LaTeX editing
      • 2.5.7. Drawing vector graphics with Adobe Illustrator and Inkscape
        • 2.5.7.1. Key concepts
        • 2.5.7.2. Fundamental vector graphic objects
        • 2.5.7.3. Color models
        • 2.5.7.4. Vector graphics drawing tools: Illustrator vs Inkscape
        • 2.5.7.5. Vector graphics file formats
        • 2.5.7.6. Required software
        • 2.5.7.7. Illustrator exercise
        • 2.5.7.8. Inkscape exercise
        • 2.5.7.9. Additional tutorials
      • 2.5.8. Editing raster graphics with Gimp
        • 2.5.8.1. Concepts
        • 2.5.8.2. Required software
        • 2.5.8.3. Exercise
        • 2.5.8.4. Additional tutorials
  • 3. Fundamentals of cell modeling
    • 3.1. Data aggregation
      • 3.1.1. Common data types and data sources
      • 3.1.2. Finding data sources
      • 3.1.3. Finding relevant data for models
      • 3.1.4. Data aggregation tools
      • 3.1.5. Determining the consensus of multiple observations
      • 3.1.6. Exercise
    • 3.2. Input data organization
      • 3.2.1. Schema
      • 3.2.2. Software tools
      • 3.2.3. Exercises
        • 3.2.3.1. EcoCyc and Pathway Tools
        • 3.2.3.2. WholeCellKB
    • 3.3. Model design
      • 3.3.1. Software tools
      • 3.3.2. Exercises
        • 3.3.2.1. Required software
        • 3.3.2.2. Expert-driven model design with COPASI
        • 3.3.2.3. PGDB-driven model design with MetaFlux
        • 3.3.2.4. Formal model selection
        • 3.3.2.5. Bayesian network structure learning
    • 3.4. Model calibration
      • 3.4.1. Key concepts
      • 3.4.2. Calibration data
      • 3.4.3. Approximate, multi-stage parameter estimation
        • 3.4.3.1. Univariate parameter estimates
        • 3.4.3.2. Pathway joint parameter estimates
        • 3.4.3.3. Global joint parameter estimates
      • 3.4.4. Exercise
    • 3.5. Model representation
      • 3.5.1. Custom numerical simulation code
        • 3.5.1.1. Exercise
      • 3.5.2. Standard numerical simulation packages
        • 3.5.2.1. Exercise
      • 3.5.3. Enumerated modeling languages
        • 3.5.3.1. Exercise
      • 3.5.4. Ruled-based modeling languages
        • 3.5.4.1. Exercise
      • 3.5.5. Rule-based modeling API
        • 3.5.5.1. Exercise
      • 3.5.6. High-level rule-based modeling language
    • 3.6. Model annotation
      • 3.6.1. Component-level semantic annotations of species, reactions, and parameters
      • 3.6.2. Model-level semantic annotations
      • 3.6.3. Model provenance annotations
      • 3.6.4. Simulation algorithm annotations
      • 3.6.5. Exercises
    • 3.7. Model composition
      • 3.7.1. Model composition procedure
      • 3.7.2. Software tools
      • 3.7.3. Exercises
        • 3.7.3.1. Merging metabolic models
        • 3.7.3.2. Merging electrophysiological models
    • 3.8. Mathematical representations and simulation algorithms
      • 3.8.1. Boolean/logical models
      • 3.8.2. Ordinary differential equations (ODEs)
      • 3.8.3. Stochastic simulation
        • 3.8.3.1. Gillespie Algorithm / Stochastic Simulation Algorithm / Direct Method
        • 3.8.3.2. Gillespie first reaction method
        • 3.8.3.3. Gibson-Bruck Next Reaction Method
        • 3.8.3.4. Tau-leaping
      • 3.8.4. Network-free simulation
      • 3.8.5. Flux balance analysis (FBA)
      • 3.8.6. Hybrid/multi-algorithmic simulation
        • 3.8.6.1. Approaches to constructing hybrid simulations
        • 3.8.6.2. Numerical simulation
        • 3.8.6.3. Synchronizing discrete and continuous variables
        • 3.8.6.4. Simulating individual submodels
      • 3.8.7. Reproducing stochastic simulations
      • 3.8.8. Simulation descriptions
      • 3.8.9. Software tools
      • 3.8.10. Exercises
        • 3.8.10.1. Required software
        • 3.8.10.2. Boolean simulation
        • 3.8.10.3. ODE simulation
        • 3.8.10.4. Stochastic simulation
        • 3.8.10.5. Deterministic, probalistic, and stochastic simulation of mRNA and protein synthesis and degradation
        • 3.8.10.6. Network-free simulation of rule-based models
        • 3.8.10.7. Dynamic FBA (dFBA) simulation
        • 3.8.10.8. Hybrid simulation
    • 3.9. Model testing
      • 3.9.1. Testing composite models
      • 3.9.2. Exercise
    • 3.10. Logging simulation results
    • 3.11. Organizing simulation results
      • 3.11.1. Exercise
    • 3.12. Quickly analyzing large simulation results
    • 3.13. Rule-based Modeling with BioNetGen, BNGL, and RuleBender
      • 3.13.1. Description of Rule-based Modeling, BioNetGen, BNGL, and RuleBender
      • 3.13.2. BNGL and RuleBender Basic Functionality
      • 3.13.3. Exercises
      • 3.13.4. Molecular sites, their states, and bonds
  • 4. Principles and methods of WC modeling
    • 4.1. Units of WC models
    • 4.2. Using the wc_lang package to define whole-cell models
      • 4.2.1. Semantics of a wc_lang biochemical Model
      • 4.2.2. wc_lang Classes Used to Define biochemical Models
        • 4.2.2.1. Static Enumerations
        • 4.2.2.2. wc_lang Model Components
        • 4.2.2.3. wc_lang Model Data Sources
      • 4.2.3. Using wc_lang
    • 4.3. Using wc_env_manager build, version, and sharing computing environments for WC modeling
      • 4.3.1. How wc_env_manager works
      • 4.3.2. Installing wc_env_manager
      • 4.3.3. Using wc_env_manager to build and share images for WC modeling
        • 4.3.3.1. Creating contexts for building the wc_env and wc_env_dependencies images
        • 4.3.3.2. Creating Dockerfile templates for wc_env and wc_env_dependencies
        • 4.3.3.3. Setting the configuration for wc_env_manager
        • 4.3.3.4. Building the wc_env and wc_env_dependencies Docker images
        • 4.3.3.5. Pushing the wc_env and wc_env_dependencies Docker images to DockerHub
      • 4.3.4. Using wc_env_manager to create and run Docker containers for WC modeling
        • 4.3.4.1. Pulling existing Docker images
        • 4.3.4.2. Building containers for WC modeling
        • 4.3.4.3. Using containers to run WC models and WC modeling tools
      • 4.3.5. Using WC modeling computing environments with an external IDE such as PyCharm
      • 4.3.6. Caveats and troubleshooting
  • 5. Appendix: Funding WC modeling research
    • 5.1. Graduate fellowships
    • 5.2. Postdoctoral fellowships
    • 5.3. Postdoc/faculty transition awards
    • 5.4. Grants
      • 5.4.1. Funding streams for your lab
      • 5.4.2. Taxonomy of funding opportunities
      • 5.4.3. Funding programs for early career investigators
      • 5.4.4. Finding funding opportunities
      • 5.4.5. Eligibility
      • 5.4.6. Deadlines
      • 5.4.7. Proposal process
      • 5.4.8. Writing proposals
      • 5.4.9. Typical costs for budgets
      • 5.4.10. Submitting proposals
      • 5.4.11. Peer review
      • 5.4.12. Statistics (NIGMS)
      • 5.4.13. Grant award process
      • 5.4.14. Annual grant renewals
      • 5.4.15. Advice for winning grants
      • 5.4.16. Advice for resubmissions
  • 6. Appendix: Installing the code in this primer
    • 6.1. Requirements
    • 6.2. How to install these tutorials
    • 6.3. Detailed instructions to install the tutorials and all of the requirements
  • 7. Appendix: Documentation for the code in this primer
    • 7.1. Subpackages
      • 7.1.1. intro_to_wc_modeling.cell_modeling package
        • 7.1.1.1. Subpackages
        • 7.1.1.2. Submodules
        • 7.1.1.3. intro_to_wc_modeling.cell_modeling.model_composition module
        • 7.1.1.4. Module contents
      • 7.1.2. intro_to_wc_modeling.concepts_skills package
        • 7.1.2.1. Subpackages
        • 7.1.2.2. Module contents
      • 7.1.3. intro_to_wc_modeling.wc_modeling package
        • 7.1.3.1. Subpackages
        • 7.1.3.2. Module contents
    • 7.2. Submodules
    • 7.3. intro_to_wc_modeling._version module
    • 7.4. Module contents
  • 8. Appendix: Acronyms
  • 9. Appendix: Glossary
  • 10. Appendix: References
  • 11. Appendix: About this primer
    • 11.1. License
    • 11.2. Authors
    • 11.3. Acknowledgements
    • 11.4. Questions and comments
An introduction to whole-cell modeling
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  • 4. Principles and methods of WC modeling

4. Principles and methods of WC modeling¶

Table of contents

  • 4.1. Units of WC models
  • 4.2. Using the wc_lang package to define whole-cell models
    • 4.2.1. Semantics of a wc_lang biochemical Model
    • 4.2.2. wc_lang Classes Used to Define biochemical Models
    • 4.2.3. Using wc_lang
  • 4.3. Using wc_env_manager build, version, and sharing computing environments for WC modeling
    • 4.3.1. How wc_env_manager works
    • 4.3.2. Installing wc_env_manager
    • 4.3.3. Using wc_env_manager to build and share images for WC modeling
    • 4.3.4. Using wc_env_manager to create and run Docker containers for WC modeling
    • 4.3.5. Using WC modeling computing environments with an external IDE such as PyCharm
    • 4.3.6. Caveats and troubleshooting
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