Review of Mathematical Modelling Techniques with Applications in Biosciences

Abstract

Modelling can provide intellectual frameworks that are necessary to translate data into knowledge. Mathematical modelling has played an important role in many applications, such as ecology, genetics, engineering, psychology, sociology, physics and computer science, in recent years. This study focused on reviewing mathematical modelling and its applications to biological systems by tracing many metabolic activities of cellular interactions on the one hand and between the spread of epidemics and population growth on the other hand. Various mathematical equations have played fundamental roles in the formation of these systems for model development procedures by describing them mathematically and establishing relationships that characterise the dynamics of a biological phenomenon. Consequently, the creation of new mathematical representations and simulation algorithms is important to the success of biological modelling initiatives. Finally, the optimisation approach performs its primary role in directing and controlling interactions by adjusting the parameters that provide the best possible result for the system

Modelling the Early Outbreak of Covid-19 Disease in Malaysia Using SIRS Model with 4-Step Adams-Bashforth-Moulton Predictor-Corrector Method

“…Infectious disease transmission dynamics are better understood and more apparent when represented using mathematical models. The saying that things become clearer when seen in the light of mathematics has promoted the acceptability of mathematical models [3]. These models are crucial in quantifying potential infectious disease control, management and prevention techniques [4;5].…”

The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison

“…Moreover, the medical sector is one of the sectors in which image processing techniques are utilised, such as chest x-rays, magnetic brain imaging, etc [7][8][9][10][11]. These techniques are utilised to eliminate the risk of misdiagnosis, which may lead to the death of the patient [12][13][14][15][16][17]. Thanks to artificial intelligence and the growth of algorithms and devices, the error rate of diagnosis and human intervention has been reduced in the modern era [18][19][20][21][22][23][24][25].…”

A Mathematical Model for Bioremediation of Hydrocarbon-Contaminated Soils

“…In addition, the selection and implementation of bioremediation approaches, as well as the prediction of the process outcomes, is highly complex. In this framework, mathematical modelling emerges as a promising tool, providing support for decision makers to effectively deal with the complexity of cleaning and restoring contaminated sites [12,13]. Over the years, mathematical modelling of hydrocarbon bioremediation has been performed, focused mainly on contaminated aquifers considering that relatively soluble hydrocarbon fractions will be transported from the contaminated soils to the water level, where further transport and biodegradation will occur [14][15][16].…”