Cell modeling and simulation:
Resource for Cell Analysis and Modeling,
“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.
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.
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 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
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 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.”
Markup Language (CellML)
“The CellMLTM language is an XML-based markup language being developed by Physiome Sciences Inc. in
The Systems Biology Workbench (SBW) is a modular, broker-based, message-passing framework for simplified application intercommunications. Features include:
Many third-party, SBW-enabled modules exist (see the download page for links):
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.
The computer program StochSim was written by Carl Firth (formerly Carl Morton-Firth) as part of his PhD work at the
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.
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
to Bioinformatics Bioinformatics
Research Centre, Department of Computing Science, University of
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.