2. Overview

Whole-cell (WC) models are comprehensive computational models of all of the biochemical activity inside individaul cells [Karr et al., 2015; Macklin et al., 2014]. WC models have great potential to enable bioengineers to rationally design microorganisms for industrial and medicine applications and to enable physicians to personalize medical therapy [Carrera and Covert, 2015]. Please see WholeCell.org for more information about WC modeling including perspectives, reviews, and tutorials.

This package contains an example WC model based on the reduced gram-positive bacterium Mycoplasma pneumoniae. The model is clearly described using wc_lang, a high-level, rule-based language for describing WC models and the model can simulated using WC-Sim, a reusable multi-algorithmic WC simulator. Portions of the model are also available in BioNetGen [Faeder et al., 2009] and SBML formats [Hucka et al., 2003].

2.1. Features of the example model

The example model is representative of full-scale WC models, but is simpler and less computationally expensive. In particular, the model has the following features of full-scale WC models:

  • Single-cell: The model represents a single cell
  • Temporally-complete: The model represnts the entire cell cycle
  • Rule-based: The model is described using high-level patterns for generating species and reactions from genomic and biochemical data
  • Modular: The model is organized into several pathway submodels which can be separately simulated and tested
  • Multi-algorithmic: The pathway submodels are represented using multiple simulation algorithms including the stochastic simulation algorithm (SSA) and flux balance analysis (FBA).

To provide a simple and computationally cheap example, the model does not have following features of full-scale WC models:

  • Molecularly-complete: The model does not represent every molecule inside a cell
  • Structurally-complete: The model does not represent the detailed three-dimensional organization of cells
  • Functionally-complete: The model does not represent every gene and cell function

2.2. Biological summary of the example model

The model represents the Central Dogma including the following pathways (second level of outline). All of the pathway submodels are represented using stochastic simulation, except for metabolism which is represented using dynamic FBA (dFBA).

  • Small molecule synthesis
    • Metabolism
  • DNA synthesis
    • DNA replication
  • RNA synthesis (and degradation)
    • Transcription
      • Initiation
      • Elongation
      • Termination
    • RNA processing: cleavage of operons into individual rRNA and tRNA molecules
    • RNA degradation
  • Protein synthesis (and degradation)
    • tRNA aminoacylation
    • Translation
      • Initiation
      • Elongation
      • Termination
    • Translocation
    • Macromolecular complexation
    • Protein degradation
  • Cell division
    • Random assortment of cytoplasm

To provide a simple and computationally cheap example, the model does not represent the following pathways:

  • DNA
    • Chromosome condensation
    • Chromosome segregation
    • Chromosome separation
    • DNA damage
    • DNA repair
    • DNA replication initiation
    • DNA supercoiling
  • RNA
    • RNA modification
  • Protein
    • Protein deformylation
    • Protein folding
    • Protein modification
    • Protein N-terminal methionine cleavage
    • Ribosome assembly
  • Regulation
    • Allosteric regulation
    • Product feedback inhibition
    • Transcriptional regulation
    • Translational regulation
  • Other
    • Membrane potential maintainence
    • Motility
    • Septation including the formation of FtsZ rings
    • Terminal organelle assembly

2.3. Purpose and motivation of the example model

We developed the example model for three purposes:

  • To provide an example model to drive the development of a software platform for systematically building, simulating, and analyzing WC models which ultimately will include the following tools:
    • WC-Aggregator: Software for aggregating the data needed to build WC models from manuscripts and databases
    • WC-KB: Pathway/genome database (PGDB) for organizing the experimental data used to train WC models
    • WC-Designer: Software for building WC models from PGDBs
    • wc_lang: High-level, data-driven rule-based language for describing WC models
    • WC-Estimator: Software for analyzing the identifiability of WC parameters and estimating their values
    • WC-Verifier: Formal verification system for WC models
    • wc_sim: Rule-based, multi-algorithmic WC simulator
    • WC-SimDB: Database for storing, browsing, and searching WC simulation results
    • WC-Viz: Software for visual analysis of high-dimensional WC simulation results
  • To provide an example model for establishing community consensus on WC modeling best practices, including on building, describing, annotating, and simulating models.
  • To provide a simple example model for teaching WC modeling. Please see our WC tutorials which are developing based on this example.

2.4. Community contributions and collaboration

We welcome community contributions to the example model and/or the WC methods and tools outlined above. Please contact the Karr Lab to discuss potential collaborations.

2.5. References

  • Carrera J & Covert MW. (2015). Why Build Whole-Cell Models? Trends in Cell Biology, 25(12), 719-722. DOI: 10.1016/j.tcb.2015.09.004
  • Faeder JR, Blinov ML & Hlavacek WS. (2009). Rule-based modeling of biochemical systems with BioNetGen. Systems Biology, 113-167. DOI: 10.1007/978-1-59745-525-1_5
  • Hucka M et al. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524-531. DOI: 10.1093/bioinformatics/btg015
  • Karr JR, Takahashi K & Funahashi A. (2015). The principles of whole-cell modeling. Current Opinion in Microbiology, 27, 18-24. DOI: 10.1016/j.mib.2015.06.004
  • Macklin DN, Ruggero NA & Covert MW. (2014). The future of whole-cell modeling. Current Opinion in Biotechnology, 28, 111-115. DOI: 10.1016/j.copbio.2014.01.012