Virtual Cell

Virtual Cell Modeling and Simulation Framework at The National Resource for Cell Analysis and Modeling (NRCAM)

The National Resource for Cell Analysis and Modeling (NRCAM), developer of the Virtual Cell, is a national resource center supported by the National Center for Research Resources (NCRR), at the National Institutes of Health (NIH).
 

Microbial Cell

Developed by the U.S. Department of Energy to complement its Genomes to Life project.

MCP takes a whole-genome approach to understanding the function and regulation of genes for a single living system  and the pathways in which the protein products interact.

In the MCP, scientists will begin to write a comprehensive "owner's manual" for a microbial cell.Microbial cells have internal organization and complex control systems that allow them to respond to their environment. They can work as miniature chemistry laboratories, making unique products and carrying out specialized functions. Ultimately, understanding the complex functioning of a single microbial cell will enable science to go far beyond just exploiting the beneficial capabilities of microbes to meet DOE's missions. The knowledge gained will apply to cells in all living things. Thus the MCP represents a first step in moving from cataloguing molecular parts to constructing an integrative view of life at the level of a whole organism -- microbe, plant, or animal (Human Genome News vol 11, Nos 3-4, 2001).

Project Cybercell

The goal of Project CyberCell is to create an accurate dynamic model of a simple living organism within 5 years
using a molecular-level population dynamics approach. The overarching premise of Project CyberCell is
the integration of experiment and theory, where directed, high-resolution biological and biomolecular measurements
are used to drive and validate combinatorial numerical analysis and systems modeling.

E-cell

A computer software environment for modelling and simulation of a hypothetical cell.

Simulations can be run to conduct experiments in silico

Users can define:

Protein functions

Protein - protein interactions

Protein -DNA interactions

Regulation of Gene Expression

Cellular Metabolism Pathways

Designed to help predict consequences of changes in cell or environment, e.g.

    - gene knockouts

    - alteration of available metabolites

Consequences of intervention include:

    - cell death

    - changes in growth rate

    - increase or decrease in expression of specific genes

In silico cell models complement experimental efforts to modify & engineer whole genomes

Enable modelling & simulation of the complex interactions among the gene products of whole genomes

E-cell model cosists of 3 lists:

    - substance list: defines all objects making the cell and culture medium (substrates, products, catalysts)

    - rule list:  defines all the reactions that can take place within the cell

    - system list:  defines spatial spatial and/or functional structure of the cell and its environment

State of cell at each time frame expressed as:

    - concentrations of all substances in cell

    - cell volume, pH & temperature

In a single time interval each rule in the rule list is called upon to compute the change in concentration of each substance (proteins, DNA, RNA & small molecules) to generate next state of the cell.

Applications:

1.  Assessment of the metabolic requirements of the cell

M. Genitalium currrently grown on complex material containing fetal bovine serum, yeast extracts

E-cell may help formulate chemically-defined synthetic medium

Comparison of experimental and computed results could result in identification of new enzmes or transporters

2.  Decifering gene regulatory networks

Specific mechanisms for control of transcript levels  may be identified by comparison of parallel in vitro and in silico experiments.

3.  Definition of the minimum set of genes required under specific set of conditions

Non-essential genes can be modified by gene disruption

Features not modelled:

Proliferation, cell structure proteins

Reference: Tomita M et al. Bioinformatics (1999) 15, 72-84
 

DIGITAL SIMULATION OF CELLULAR NETWORKS

Diagrammatic Cell Language^TM

Complete diagrammatic language for large-scale network visualization and simulation

Colon cancer and other examples at Gene Network Sciences website

Physiolab and In Silico R&D (Entelos Corporation):

Provides a framework for integrating data (including genomic, proteomic, physiologic, and environmental) in the
context of a disease, with a focus on understanding anddetermining clinical responses to potential treatment.
 

DIGITAL SIMULATION OF EXPERIMENTS

Virtual experiments such as mutations and gene knock-outs.

Example: Digital Disease Models^TM
 
 

MATHEMATICAL MODELLING OF METABOLIC NETWORKS
 

Application of Flux Balance Analysis

1.  Analysis of variation of the H. Influenzae Rd metabolic genotype in silico:

Schilling CH and Palsson BO (2000) J. Theor. Biol. 203, 249-283

Edwards JS and Palsson BO (1999)  J. Biol.Chem. 274, 17410 -17416

Defined extreme pathways: systemically-independent biochemical pathways

Six different optimal metabolic phenotypes sould be defined

Redundant functions under defined conditions could be identified

2.  Definition of system capabilities and capabilities of metabolism in E.Coli MG1655

Edwards JD and Palsson BO PNAS (2000) 97, 5528 - 5533

Applications:

1.  Reconcile and curate sequence annotation by identifying unsupported reactions

2.  Decifering gene regulatory networks

Specific mechanisms for control of transcript levels  may be identified by comparison of parallel in vitro and in silico experiments. Coregulation and co-expression may be discovered

3.  Definition of the minimum set of genes required under specific set of conditions

4.  Identification of genes that can be deleted under critical conditions in specific human environments

Review:

Papin JA et al. (2003) Metabolic Pathways in the Post-genome era. Trends. Biochem. Sci. 28: 250-8

Lectures in Harvard Biophysics 101 Lecture Course:

    Metabolic network flux models: Introduction to the basic concepts and linear optimization

    Metabolic network flux models: Scientific and practical use

THE BIOME

[Course Schedule]  [Metabolic Pathways]
 
Bioinformatics Programming

The Scriptome :  a Minimal learning toolbox for Manipulating Biological Data