Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools and Cross-Omics>Agent-Based Modeling/Simulation/Tools

Abstract CycSim (pathway genome simulator) is a web-based application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc (see G6G Abstract Number 20230) and KEGG (Kyoto Encyclopedia of Genes and Genomes).

Specifically, CycSim supports the design of knockout experiments: simulation of ‘growth phenotypes’ of single or multiple ‘gene deletion(s)’ mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on ‘metabolic maps’.

The web interface is designed for simplicity, putting constraint-based modeling techniques within easier reach of biologists.

CycSim also functions as an online repository of genome-scale metabolic models.

Constraint-based modeling --

Constraint-based modeling is a framework, simple and abstract enough to allow ‘tractable modeling’ of metabolism at genome-scale, providing direct insights into the genotype-phenotype relationship.

Constraint-based models (CBM) consist of a stoichiometric representation of the whole-cell metabolism together with a set of constraints on reaction fluxes.

A wide variety of ‘computational methods’ have been developed for this framework to characterize metabolic capabilities, help to discover new reactions, simulate scenarios of ‘metabolic evolution’ or design experimental strategies to investigate metabolic behaviors.

CycSim Functionalities --

1) Predictions - CycSim supports in silico experiments with metabolic models. Each experiment consists of selecting a wild-type strain, choosing one or several ‘genetic perturbations’ (e.g. knockout), and picking a set of growth media.

Growth phenotype predictions are then generated for all (mutant, medium) pairs.

These predictions can be compared against experimental growth phenotypes when available. Two (2) prediction methods are implemented: a) flux balance analysis and b) metabolites producibility check.

For any given (mutant, medium) pair, CycSim can also compute a ‘flux distribution’ that is compatible with the model constraints and the objective function.

2) Visualization - Reactions, pathways and genes can be visualized in their context through a tight coupling of the CycSim core with the pathway display layers of BioCyc and KEGG.

For instance, clicking on a reaction in the simulation panel will show the corresponding BioCyc reaction page augmented with information from the active model (i.e. balanced reaction equations or the Boolean gene- reaction correspondence).

Conversely, a gene can be deleted from the current model by selecting it from a ‘pathway map’. Predictions and experimental results can be directly visualized and compared on pathways.

3) Model and data repository - The online CycSim repository stores information relative to three (3) organisms: Escherichia coli, Saccharomyces cerevisae and Acinetobacter baylyi ADP1.

For each organism, CycSim includes:

1) A genome-scale metabolic model;

2) A detailed correspondence between that model and relevant data of that organism;

3) A set of media definitions; and

4) Experimental growth phenotype datasets.

Altogether, CycSim includes over 2,800 genes, 3,700 reactions, 1,400 metabolites, 190 media, 20,000 experimental phenotypes and 550 pathways.

Any of the four (4) data types can be submitted online, using the Systems Biology Markup Language (SBML) format for models, enhanced with Minimal Information Requested In the Annotation of biochemical Models (MIRIAM) annotations.

CycSim Architecture and Technologies --

In order to facilitate operations from any computer, CycSim was developed as a web application using the AndroMDA framework deployed on a Java application server (JBoss) with a MySQL (database) backend.

CycSim uses the AJAX technology (GWT). In order to ensure the availability of sufficient computational resources, computations are performed on the server.

A simple mechanism ensures some persistence of user sessions: the settings of each analysis are saved on the server and can be retrieved through a unique identifier.

In order to foster extensions by its developers or by the bioinformatics community, CycSim is based on a comprehensive Unified Modeling Language (UML) model, which covers biochemical information (reactions and phenotype experiments) and information specific to Constraint-based models (CBM) - (fluxes and perturbations).

Furthermore, ‘web services’ are provided to programmatically access the models contained in CycSim.

System Requirements



Manufacturer Web Site CycSim

Price Contact manufacturer.

G6G Abstract Number 20562

G6G Manufacturer Number 104169