Interdisciplinary applications of mathematics in biological sciences
*Vasundhara V. GhorpadeCorresponding authordrvasundharaghorpade@gmail.comDepartment of Community MedicineKrishna Institute of Medical SciencesKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Suraj Bhansuraj.bhan@niu.edu.inSchool of Engineering & TechnologyNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , Shobhit Goyalshobhit.goyal@quantumeducation.inQuantum University Research CenterQuantum UniversityRoorkee, Uttarakhand, 247667, IndiaView full profile → , Namrata Vermanamrata.verma@rungta.orgDepartment of Computer Science and EngineeringRungta College of Engineering and TechnologyRungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Sachin Mittalsachin.mittal.orp@chitkara.edu.inCentre of Research Impact and Outcome (CRIO)Chitkara UniversityRajpura, Punjab, 140417, IndiaView full profile → , Gujjala SrinathGujjalaSrinath@gmail.comCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, IndiaView full profile → , Vikram V. Patelvikrampatel@gokuluniversity.ac.inFaculty of EngineeringGokul Global UniversitySiddhpur, Gujarat, 384151, IndiaView 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-2830
- Pages:
- 3195–3206
Abstract
Quantitative modeling, analysis, prediction and interpretation are valuable interdisciplinary tools for understanding complex biological systems that can be provided by mathematics. This study is dedicated to the application of mathematics in biology and is based on an interplay of differential equations, dynamical systems, graph theory, network mathematics and computational modeling. Both the temporal (time) changes of population sizes, interaction between cells, and physiological processes can be modeled as differential equations, and graph-based models can be used to capture the overall structure of complex biological networks and their connectivity. Biological processes which involve growth, regulation, competition and interaction are also represented using mathematical models. The behavior of gene expression is investigated with quantitative formulations, in order to understand the molecular dynamics and regulation. Estimation of disease transmission, development and the effects of interventions are also performed using disease progression models. The interdisciplinary approach shows the advantages of a mathematical literacy in enhancing biological interpretations, enabling prediction, and also the development of systematic mathematical tools for exploring and understanding complex biological mechanisms at several spatial and temporal scales.
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References
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