GEne Network Evolution SImulation Software (GeNESiS)

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

Abstract GEne Network Evolution SImulation Software (GeNESiS) is for modeling and simulating the evolution of arbitrary ‘Gene Regulatory Networks’ (GRNs).

GeNESiS models the process of gene regulation through a combination of finite-state and stochastic models. The evolution of GRNs is then simulated by means of a ‘genetic algorithm’ with the network connections represented as binary strings.

The software allows users to simulate the evolution under varying selective pressures and starting conditions.

The manufacturers believe that the software can provide a way for researchers to understand the evolutionary behavior of populations of GRNs.

In an earlier study (Evolution of gene regulatory networks: Robustness as an emergent property of evolution; Physica A: Statistical Mechanics and its Applications; Volume 387, Issues 8-9, 15 March 2008, Pages 2170-2186), the manufacturer developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.

The manufacturer observed that robustness evolves along with the networks as an emergent property even in the absence of specific selective pressure.

During this optimization process towards a more robust system, multiple genotypes evolve which give rise to the same phenotype; this is in accordance with the theoretical view that natural selection operates on phenotypes, thereby accommodating variation in the genotype by fixing those changes that are phenotype-neutral.

The manufacturer created a parallel software package (GeNESiS), that implements the framework developed in the earlier study (see above...), for studying the evolution of GRNs.

GeNESiS Software --

The software GeNESiS is composed of two (2) parts:

The front-end graphical user interface (GUI) written in Java and the backend Algorithm written in C.

The Algorithm itself is parallel in nature and has been built using the GNU Scientific Library (GSL) - [The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least- squares fitting.

GSL provides over 1,000 functions in total including an extensive test suite] and the Parallel Genetic Algorithm Package (PGAPack) - [PGAPack is a parallel genetic algorithm library that is intended to provide most capabilities desired in a ‘genetic algorithm’ package, in an integrated, seamless, and portable manner].

GeNESiS Algorithm --

At the basic level, the model consists of a finite-state aspect since the state of the network depends on the binding/unbinding of proteins to the different binding sites in the promoter regions of the different genes. Each protein has binding domains for none or more genes.

The effect of a protein binding to the promoter region of a gene can be activation or repression. In addition, a protein can also undergo an activating or a repressing post translational modification (PTM).

The gene activity in this model is governed by the number of molecules of the “active” gene (that is one with promoter proteins bound to their promoter regions) as a result of which, the model stays closer to reality where a basal level of gene activity is present and genes are seldom seen to exhibit purely binary state behavior. Additionally, the manufacturer also models the effect of reversible PTMs.

GeNESiS Model --

The manufacturer's model attempts to describe the process of ‘gene regulation’ from the binding of the transcription factor-RNA Polymerase complex to the DNA molecule to the translation of mRNA into the protein product. Every gene is represented by a DNA molecule that is assumed to have one or more sites for the binding of ‘transcription factors’ and other cofactors.

The RNA Polymerase molecule can then bind to the transcription factor- cofactor complex which then breaks down on completion of the reading to form the mRNA molecule, which is in turn translated to form of the associated protein molecule.

The protein molecule can cause both positive and negative regulation of its target gene. However, negative regulation is Not independent of the binding order which implies that the molecule can only bind to the promoter region of its target gene in the absence of any other transcription factors.

The protein molecule can also undergo post-translational modifications (PTMs) which can be both enabling and disabling modifications. Enabling modifications turn the activity of a protein molecule “on” while an inactivating modification turns the activity of the molecule “off”.

Network Evolution --

The evolution of the networks is studied by means of a genetic algorithm (GA). A bit-string representation of the different RNAP-cofactor complexes and the protein molecules is concatenated together to form a representation of the entire network. Each such representation of a network is used as an individual chromosome in the genetic algorithm.

At the start, a population of solutions is initialized using networks with random connectivities or ones in which all proteins have broad specificities. These correspond to two scenarios: random connectivities corresponding to specificity of DNA-protein interactions, while those in which all proteins are connected to each other correspond to the situation whereby any protein can activate any other protein leading to very broad specificities.

Once the initial population has been seeded, evolution is allowed to proceed.

In each generation, two individuals in the population are chosen at random to mate in order to produce offspring. Individual networks are also subject to mutations while unfit individuals die out, only to be replaced by newer networks.

Evolution proceeds under certain pre-defined selective pressures such as maximizing the biomass or through minimizing the number of interactions between proteins or a combination of both selective pressures. Evolution stops when stable networks are obtained.

Graphical User Interface (GUI) --

The GUI for GeNESiS contains two (2) main canvases. The “Evolve” tab is used for the evolution of a given network while the “Simulate” tab is used to simulate a particular GRN with desired parameters.

System Requirements

Contact manufacturer.


Manufacturer Web Site GeNESiS

Price Contact manufacturer.

G6G Abstract Number 20565

G6G Manufacturer Number 104172