Effect of delay in transportation of extracellular glucose into cardiomyocytes under diabetic condition: a study through mathematical model

Numerical simulation of calcium dynamics dependent ATP degradation, IP3 and NADH production due to obesity in a hepatocyte cell

Calcium (Ca[Formula: see text]) signals have a crucial role in regulating various processes of almost every cell to maintain its structure and function. Calcium dynamics has been studied in various cells including hepatocytes by many researchers, but the mechanisms of calcium signals involved in regulation and dysregulation of various processes like ATP degradation rate, IP[Formula: see text] and NADH production rate respectively in normal and obese cells are still poorly understood. In this paper, a reaction diffusion equation of calcium is employed to propose a model of calcium dynamics by coupling ATP degradation rate, IP[Formula: see text] and NADH production rate in hepatocyte cells under normal and obese conditions. The processes like source influx, buffer, endoplasmic reticulum (ER), mitochondrial calcium uniporters (MCU) and Na[Formula: see text]/Ca[Formula: see text] exchanger (NCX) have been incorporated in the model. Linear finite element method is used along spatial dimension, and Crank-Nicolson method is used along temporal dimension for numerical simulation. The results have been obtained for the normal hepatocyte cells and for cells due to obesity. The comparative study of these results reveal significant difference caused due to obesity in Ca[Formula: see text] dynamics as well as in ATP degradation rate, IP[Formula: see text] and NADH production rate.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.