Mathematical modelling of epigenetic regulatory mechanisms in gene expression
Manish K. Assudanimkasudani@gmail.comDepartment of Computer Science & EngineeringAnjuman College of Engineering & TechnologyNagpur, Maharashtra, 440022, IndiaView full profile → , *Dheeraj ManeCorresponding authordhirajmane123@gmail.comDirectorate of ResearchKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Rahul Kumar Singhrahul.singh1@niu.edu.inSchool of Engineering and TechnologyNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , Jyoti Upadhyayjyoti.upadhyay@rungta.orgDepartment of Computer Science and EngineeringRungta College of Engineering and TechnologyRungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Vaishali Sunilsingh Bayasvaishali.bayas1@vit.eduDepartment of Electronics and Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Rakhimova Kholiskhonxolisxonraximova744@gmail.comDepartment of BiologyUrganch State UniversityUrgench, UzbekistanView full profile → , Inoyatillo Kholmurodovxolmurodov@tues.uzDepartment of Basic Medical SciencesTermez University of Economics and ServiceTermez, UzbekistanView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 27 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2805
- Pages:
- 2879–2889
Abstract
Epigenetic Regulation is the way that expression of genes is controlled by the reversible modification of DNA, modification of histones, chromatin remodeling and interaction with non-coding RNA so that cells respond to developmental and environmental cues without changing the DNA sequence. These complex networks and their dynamic functions can be understood quantitatively using math models. A comprehensive model framework, based on ordinary differential equations and stochastic techniques, is presented in this paper that models the epigenetic mechanisms that influence the regulation of the transcription. Using parameter estimation and numerical simulation of the model, the stability of regulation, interaction of feedback and the number of states for different biological conditions are explored. Dynamic analysis demonstrates the potential for epigenetic modification to activate/silence genes and how the phenotype can vary. The framework presented here provides useful insights into the regulatory forces, enhances predictive understanding of gene expression processes, and can assist in future applications such as systems biology, precision medicine, disease modelling and therapeutic strategy development.
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References
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