Cell modeling and simulation:

 

National Resource for Cell Analysis and Modeling,  University of Connecticut - “Virtual Cell Project”
 “NRCAM provides advanced research tools for the study of cell physiology.
The primary technology development project is the Virtual Cell, a general computational framework for modeling cell biological processes. Specifically, computational cell biology is coupled with high resolution light microscopy to facilitate the interplay between experimental manipulation and computational simulation of cellular events. This new technology associates biochemical and electrophysiological data describing individual reactions with experimental microscopic image data describing their subcellular locations. Cell physiological events can then be simulated within the empirically derived geometries, thus facilitating the direct comparison of model predictions with experiment. Access to the Virtual Cell modeling software is available via the internet using a Java based interface. Distinct biological and mathematical frameworks are encompassed within a single graphical interface.

Bio/Spice,

Bio/Spice is a biological data analysis and modeling workspace and database based loosely on SPICE tools used by Electrical Engineers for the design and analysis of their circuitry… The computational aspects of the laboratory are dedicated to creating methods for the numerical analysis of biological systems. The efforts cover roughly four areas: 1) Databasing, 2) Data mining/analysis, 3) Model building, 4) Numerical simulation and analysis. Database efforts entail the creation of complex biological databases containing more that just sequence and molecular structure information, but also containing information on developmental, signal transduction and metabolic pathways, models and parameters for various cellular processes, primary molecular profiling and image data. The datamining and analysis efforts involve developing informative relationships among the different datatypes to identify biological components and subsystems responsible for experimental observations. Model building tools are central to aiding in the rapid evaluation of theories of biochemical reaction network functioning and in explaining complex experimental data such a gene microarrays. Numerical simulation and analysis tools allow models, at various levels of abstraction and different types (stochastic or deterministic, differential or algebraic), to be evaluated for their dynamical behavior and the dependency of behavior on parameters and model structure.

Simulac is general purpose software for stochastic simulation of simple prokaryotic genetic and biochemical systems.

Deduce is software for deduction of chemical network structure from measured time-series.

 

M-cell

MCell, makes it possible to incorporate high resolution ultrastructure into models of ligand diffusion and signaling. MCell simulations are positioned at a biological scale above molecular dynamics but below whole cell and higher level studies. Diffusion of individual ligand molecules is simulated using a Brownian dynamics random walk algorithm, and bulk solution rate constants are converted into Monte Carlo probabilities so that the diffusing ligands can undergo stochastic chemical interactions with individual binding sites such as receptor proteins, enzymes, transporters, etc. MCell's use to date has been focused on one aspect of biological signal transduction, namely the microphysiology of synaptic transmission, but other areas of possible application include statistical chemistry, diffusion theory, single channel simulation and data analysis, noise analysis, and Markov processes. MCell simulations can provide unique insights into the behavior and variability of real systems comprising finite numbers of molecules interacting in spatially complex environments.

 

Neuron

NEURON is a simulation environment for developing and exercising models of neurons and networks of neurons. It is particularly well-suited to problems where cable properties of cells play an important role, possibly including extracellular potential close to the membrane), and where cell membrane properties are complex, involving many ion-specific channels, ion accumulation, and second messengers. It evolved from a long collaboration between Michael Hines and John W. Moore at the Department of Neurobiology, Duke University. Their express goal was to create a tool designed specifically for solving the equations that describe nerve cells. User-defined properties of membrane and cytoplasm are expressed in terms of kinetic schemes and sets of simultaneous equations. Membrane voltage and states are computed efficiently by compiling these model descriptions and using an implicit integration method optimized for branched structures. Variable-order variable-stepsize integration can be chosen to achieve increased accuracy and/or speed.

 

Genesis

GENESIS (short for GEneral NEural SImulation System) is a general purpose simulation platform which was developed to support the simulation of neural systems ranging from complex models of single neurons to simulations of large networks made up of more abstract neuronal components. Most current GENESIS applications involve realistic simulations of biological neural systems. Viewed most generally, GENESIS is intended to provide a framework for quantifying the physical description of the nervous system in a way that promotes common understanding of its physical structure. At the same time, this physical description also provides the base for simulations intent on understanding fundamental relationships between the structure of the brain and its measurable behavior. GENESIS was designed from the beginning to allow the development of simulations at any level of complexity, from sub-cellular components and biochemical reactions, to whole cells, networks of cells and systems-level models. The design of the GENESIS simulator and interface is based on a ``building block'' approach. Simulations are constructed from modules that receive inputs, perform calculations on them, and then generate outputs. Model neurons are constructed from these basic components, such as compartments (short sections of cellular membrane) and variable conductance ion channels. Compartments are linked to their channels and are then linked together to form multi-compartmental neurons of any desired level of complexity. Neurons may be linked together to form neural circuits. This object-oriented approach is central to the generality and flexibility of the system, as it allows modelers to easily exchange and reuse models or model components. In addition, it makes it possible to extend the functionality of GENESIS by adding new commands or simulation components to the simulator, without having to modify the GENESIS base code.

E-CELL
E-CELL Simulation Environment (E-CELL SE) is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells. Core part of the system, E-Cell Simulation Environment version 3, allows many components driven by multiple algorithms with different timescales to coexist. E-CELL attempts to provide a framework not only for analyzing metabolism, but also for higher-order cellular phenomena such as gene regulation networks, DNA replication, and other occurrences in the cell cycle… Genome sequencing projects and further systematic functional analyses of complete gene sets are producing an unprecedented mass of molecular information for a wide range of model organisms. This provides us with a detailed account of the cell with which we may begin to build models for simulating intracellular molecular processes to predict the dynamic behaviour of living cells. Previous work in biochemical and genetic simulation have isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established. We, therefore, were motivated to develop a software environment for building such integrative models based on gene sets, and running simulations to conduct experiments in silico.”

Cell Markup Language (CellML)
 “The CellMLTM language is an XML-based markup language being developed by Physiome Sciences Inc. in Princeton, New Jersey, in conjunction with the Bioengineering Research Group at the University of Auckland's Department of Engineering Science and affiliated research groups. The purpose of CellML is to store and exchange computer-based biological models. CellML allows scientists to share models even if they are using different model-building software. It also enables them to reuse components from one model in another, thus accelerating model building. CellML includes information about model structure (how the parts of a model are organizationally related to one another), mathematics (equations describing the underlying biological processes) and metadata (additional information about the model that allows scientists to search for specific models or model components in a database or other repository). CellML includes mathematics and metadata by leveraging existing languages, including MathML and RDF. In the future, CellML may also use other existing languages to specify data and define simulation and rendering information”.

Systems Biology Workbench and Systems Biology Markup Language

“Our Mission is to develop an integrated, easy-to-use environment, the workbench, which will enable biologists to create, manipulate, display and analyze biological models at molecular, cellular and multicellular levels.  We are focusing on biochemical networks including mass action kinetics, metabolic pathways, stochastic simulation, gene expression and regulation… One of the key aspects of out project is to facilitate collaboration among existing developers and users of system biology software. We aim to do this by providing an open-source software infrastructure which will enable collaborators to freely use and share each other's computational resources.”

The Systems Biology Workbench (SBW) is a modular, broker-based, message-passing framework for simplified application intercommunications. Features include:

  • Bindings for C, C++, Delphi, Java, Perl, Python
  • Support for Windows, FreeBSD and Linux (with MacOS X planned)
  • Distributed operation via SSH
  • Bidirectional CORBA-SBW gateway
  • Collection of modules provided with the base distribution:
    • Simple stochastic simulator
    • An SBML-to-MATLAB ODE and Simulink file translator
    • An SBML reader module
    • A "clipboard" module for exchanging SBML models
    • A Browser that outputs module interface definitions
    • A simple plotting module for time-series data
    • A generic simulation-control GUI interface

Many third-party, SBW-enabled modules exist (see the download page for links):

  • Jarnac Metabolic Simulation Package, a biochemical simulation package for Windows
  • JDesigner, a visual biochemical network layout tool
  • MetaToolSBW, a network analysis tool
  • Pasadena Twain, a simple interactive ODE solver
  • GillespieService, a module implementing a stochastic simulator
  • A bifurcation analysis module
  • An optimization module
  • An SBML Validator for checking the syntax of an SBML file
  • A simple Windows TrayTool utility
  • An Inspector that lists running modules and their services

Systems Biology Markup Language:

Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. We propose the Systems Biology Markup Language (SBML) as a common representation language for storing biochemical models. SBML is based on XML, and contains structures for representing compartments, species and reactions, as well as optional unit definitions, parameters and rules (constraints). We have kept the base definition of the language as simple as possible, so that simulator developers will not find it too difficult to implement support for SBML in their tools.

StochSim
The computer program StochSim was written by Carl Firth (formerly Carl Morton-Firth) as part of his PhD work at the University of Cambridge (Morton-Firth, 1998). It was developed as part of a study of bacterial chemotaxis as a more realistic way to represent the stochastic features of this signalling pathway and also as a means to handle the large numbers of individual reactions encountered (Morton-Firth & Bray, 1998; Morton-Firth et al., 1999). The program now provides a general purpose biochemical simulator in which individual molecules or molecular complexes are represented as individual software objects. Reactions between molecules occur stochastically, according to probabilities derived from known rate constants. An important feature of the program is its ability to represent multiple post-translational modifications and conformational states of protein molecules. StochSim consists of a platform-independent core simulation engine encapsulating the algorithm described above and a separate graphical user interface. The program is available for download from ftp://ftp.cds.caltech.edu/pub/dbray.

Gepasi 3 - Biochemical Kinetics Simulator
Gepasi is a software package for modeling biochemical systems. It simulates the kinetics of systems of biochemical reactions and provides a number of tools to fit models to data, optimize any function of the model, perform metabolic control analysis and linear stability analysis. Gepasi simplifies the task of model building by assisting the user in translating the language of chemistry (reactions) to mathematics (matrices and differential equations) in a transparent way. This is combined with a set of sophisticated numerical algorithms that assure the results are obtained fast and accurate. Gepasi is intended primarily for research purposes but because of its user-friendly interface it is equally good for education.

 

In Silico Cell
Physiome Sciences has created a unique integrated software platform for constructing and running computer-based biological models that can be effectively used to facilitate drug discovery. These models are: 
     Customizable — individualized models are constructed from a general application framework 
     Expandable — the models evolve as new data are generated; model components can be re-used to build more complex models 
     Transparent — researchers are allowed to access both the data and the mathematics used to create the model 
     Flexible — the models can interface with other applications and build on the user’s existing computational and IT infrastructure.
     Easy to use — automatically generates simulations from mathematical texts, and equations from cell and pathway maps                                       
                            

Introduction to Bioinformatics Bioinformatics Research Centre, Department of Computing Science, University of Glasgow

Enzyme function and Metabolic pathways databases: KEGG, EcoCyc, MPW-EMP, PathDB, AMAZE, BRENDA

PathDB is a metabolic database that combines storage of specific and detailed information about metabolism with powerful software for data query, navigation and visualization. This combination makes PathDB more than just a simple electronic encyclopedia, enabling discovery of facts that were already known but have been hidden in the complexity of metabolic data.