sismonr

sismonr simulates in silico gene regulatory networks involving protein-coding and non-coding genes to generate synthetic biological systems for studying transcriptional and post-transcriptional regulation, ploidy effects, genetic variation, and gene expression dynamics.

Key Features:

• Implementation: Implemented as an R package with additional implementation in Julia for simulation workflows.
• Comprehensive network simulation: Constructs multi-layer gene regulatory networks that incorporate transcriptional and post-transcriptional mechanisms.
• Molecular scope: Models interactions among protein-coding and non-coding genes within regulatory networks.
• Adjustable ploidy levels: Supports definition of higher ploidies beyond haploid and diploid for studying organisms with complex genomic structures.
• Stochastic simulation algorithms: Employs a range of stochastic simulation algorithms to model gene expression profiles and capture biological variability.
• Simulation of genetic variability: Simulates genetic mutations and genetically distinct individuals within the same system.
• Pathway-specific demonstration: Applied to simulate regulation of the anthocyanin biosynthesis pathway across three genetically distinct in silico plants.

Scientific Applications:

• Ploidy effect studies: Investigating the impact of different ploidy levels on gene expression and network dynamics.
• Post-transcriptional regulation analysis: Exploring post-transcriptional mechanisms within multi-omic regulatory frameworks.
• Genetic variation modeling: Modeling the effects of genetic mutations and individual genetic diversity on regulatory network behavior relevant to genetics, evolution, and disease research.
• Pathway regulation simulation: Simulating specific biochemical pathways such as anthocyanin biosynthesis to study pathway-level regulatory responses.

Methodology:

Generates synthetic biological systems and gene regulatory networks, incorporates adjustable parameters such as ploidy, employs stochastic simulation algorithms to model gene expression profiles, and simulates genetically distinct individuals and mutations.