Systems Biology for Metabolic Disorder and Disease

The simplistic approach frequently used in biological sciences is no longer sufficient if we want to research complicated multi-factorial illnesses like Metabolic Syndrome (MetS) by utilizing cutting-edge ‘omics’ techniques. A thorough understanding of the system is required to comprehend the adaptive changes in molecular mechanisms at various phases of pathogenicity since activating various pathways may still result in the same functionality but at multiple metabolic costs. Systems biology is an interdisciplinary branch of research that employs a much more thorough overview to tackle biomedical and biologic research. It focuses on complex interactions within and across biological systems. Whereas the significance of the systems biology approach has long being understood, experimental and simulation methods have progressed to the point where thorough molecular characterization of biological systems is now attainable. The term metabolic syndrome describes a collection of illnesses that include dyslipidaemia, fatty liver, diabetes, obesity, resistance to insulin and other cardiovascular issues. Metabolic syndrome is significant due to its frequency, possible severity and expense. The control of the metabolic pathways that control the overall balance of the body’s systems depends heavily on the liver. An intricate web of hormones, transcription factors, and signalling pathways regulates how much glucose and lipids are produced in the liver. A prevalent understanding nowadays is that the metabolic syndrome’s hepatic manifestation is fatty liver that is associated with dysfunctional lipoprotein, fatty acid and glucose metabolism. In order to replicate the recognized metabolic activities in a cell, metabolic systems biology provides significant abstracted techniques, which results in a picture which is near to the observed phenotype. This will enable identifying the patient’s illness pattern and giving accurate medical solutions, leading to prophylactic medicine, less treatment and in silico clinical studies when combined with cutting-edge machine learning techniques.