Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools

Abstract JCell is a Java-based application for reconstructing ‘gene regulatory networks’ from experimental data.

The framework provides several algorithms to identify ‘genetic and metabolic’ dependencies based on experimental data conjoined with mathematical models to describe and simulate regulatory systems.

Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers.

JCell is an application that can be used to address the problem of combining algorithms and models to ‘infer regulatory systems’ from time series data.

First, it aims to provide a ‘framework structure’ that enables researchers to use these methods without deeper knowledge of the underlying mathematics.

Second, one of the design goals is the independence from commercial software to ensure the spirit of free research.

Furthermore, owing to the modular design it can be easily extended with new algorithms and strategies. For this purpose, the manufacturer implemented non-mandatory interfaces to other packages such as MATLAB, Maple, and JavaEvA (see G6G Abstract Number 20579).

Gene Regulatory Networks --

Gene Regulatory Networks (GRNs) represent the dependencies of the different actors in a cell operating at the genetic level. They dynamically determine the level of ‘gene expression’ for each gene in the genome by controlling whether a gene will be transcribed into RNA.

A simple GRN consists of one or more input ‘signaling pathways’, several target genes, and the RNA and proteins produced from those target genes. In addition, such networks often include ‘dynamic feedback loops’ that provide for further regulation of ‘network regulation’ activities and output.

In order to understand the underlying structures of activities and interactions of ‘intra-cellular processes’ one has to understand the dependencies of gene products and their impact on the expression of other genes.

Therefore, finding a GRN for a specific biological process would explain this process from a logical point of view.

JCell is a framework for simulating GRNs --

JCell is a framework for simulating GRNs. It can be used for two (2) different applications:

1) Reverse-engineering and inferring regulatory mechanisms based on the evaluation of given biological and medical data coming from DNA ‘microarray experiments’, and

2) Simulating cell growth and mitosis by finding GRNs suitable for a given problem (e.g. limited growth).

JCell is aimed at supporting biological and medical researchers in generating hypotheses about topologies and kinetic parameters of ‘regulatory systems’.

This is done by searching for dependencies in time series data.

JCell was initially designed to handle ‘gene expression’ data from microarray experiments and was later extended to ‘metabolic data’ because they have similar properties with respect to the mathematical modeling of the underlying systems.

To reconstruct regulatory systems, JCell models ‘time series data’ by finding a topology of the network, together with a mathematical description and the corresponding ‘kinetic parameters’ of the given dynamic systems.

The actual computational search is performed mostly with ‘Evolutionary Algorithms’ such as ‘Genetic Algorithms’, Evolution Strategies, Differential Evolution, Particle Swarm, ‘Genetic Programming’, Memetic Algorithms and several multi-objective optimizers together with direct heuristics, if applicable.

Note: For additional info on 'Evolutionary Software' see the G6G Software Product Abstracts by Application (AI Section).

Overall, the framework comprises more than ten (10) different optimization strategies including their parallel and distributed versions that allow for processing the tool on high-performance clusters.

Furthermore, JCell provides several mathematical models for simulating ‘regulatory systems’, like Boolean Networks, linear and pseudo-linear Systems, S-Systems, H-Systems and arbitrary differential equations.

The manufacturer also implemented models to simulate ‘intra-cellular systems’ stochastically (e.g. ‘Bayesian Networks’), where a deterministic approach is unfavorable as it is in the case for immune relevant signaling cascades.

For the ‘inference process’, multiple novel strategies have been developed at the manufacturer's institute and implemented in JCell, including iterative knock-outs, topology identification and graph minimization problems.

In addition, JCell enables the user to edit the ‘network topology’, thus adding biological information of the network into the inference process, enforcing the algorithm to find networks with specified properties.

This can be done manually, using a built-in graph editor, or by importing topologies from external sources.

For example, pathways can be incorporated by querying public databases, such as, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and TransFAC (see G6G Abstract Number 20121) to import ‘topology graphs’ into JCell.

The imported pathways are used as templates for the inference process. They can either be fixed throughout the inference, so that the tool only fits the kinetic parameters, or they can be used as an initial guidance structure that can be altered by the framework.

Another feature of JCell is the ability to simulate regulatory networks to examine effects of particular components in silico.

This enables researchers to study regulatory networks and the impact of drug treatment on a computer to reduce expensive wet lab experiments. A publication on an application in ‘Artificial Embryology’ is listed on the manufacturer’s website.

One of the main goals was to develop a user-friendly framework that is able to run on most computer platforms without requirements of special hardware. To ensure this independence, JCell was written completely in Java.

Furthermore, it can be run using ‘Java WebStart’ in a standard Web Browser.

JCell comprises a simple and self-explanatory user interface (UI) that presents only the most important parameter values. For most of the remaining hidden settings, default values that showed good performance in preliminary experiments are used.

Nevertheless, all algorithm settings are accessible via ‘configuration files’. These files can be either loaded from the interface or committed in the command line for batch mode.

This enables experts to further tune the algorithms.

Beside the already implemented ability to import pathways in ASCII, XML and KEGG Markup Language (KGML), the manufacturer plans comprehensive support of the Systems Biology Markup Language (SBML) for the next release of JCell.

JCell documentation --

The corresponding publications for JCell and an extensive user guide/tutorial can be found on the JCell website.

System Requirements



Manufacturer Web Site University of Tübingen JCell

Price Contact manufacturer.

G6G Abstract Number 20578

G6G Manufacturer Number 104182