Computational Biology
During the past two decades, the disclosure of genome information has generated breakthroughs in the field of genetics and evolutionary biology to analyze the unexpected amount of data generated. There are many limitations associated with developing computational tools for analyzing genomic and proteomic data including genome duplication, rearrangement, and shrinkage. Computational biology is the amalgamation of math, statistics, and computer science applications to solve problems based on biology. It is a very broad discipline describing biological tasks carrying out by particular nucleic acid, gene expression in order to produce a particular phenotype, how a mutation in DNA leads to a particular disease, and how changes in cell organization influence cell behavior. Computational biology is the science to build models for diverse types of experimental data and biological systems by using methods from a broad range of mathematical and computational fields. Some of the areas responsible for biology problems are genetics, evolution, cell biology, and biochemistry. Computational biology uses the following algorithms-
- Global Matching
- Local Sequence Matching
- Hidden Markov Models
- Population genetics
- Evolutionary Trees
- Gene Regulation Networks
- Chemical Equations
Genomics, proteomics, and cancer detection are some of the recent applications of computational biology. Personalized or precision cancer therapy’s goal is to treat cancer efficiently with lessen side-effects. Next-generation sequencing plays a major role in precision antitumor treatment. Artificial intelligence has accelerated precision drug identification by using the genetic profile for each patient. Developing an artificial intelligence-based system can be used to identify biomarkers, develop better diagnoses, and identify novel drugs. Recently, computational approaches have been used for target identification, discovery, and optimization of drug candidate molecules. Their applications span almost all stages of the traditional drug development process. Drug discovery is a complex, prolonged, expensive, and challenging process. Computational biology and bioinformatics not only accelerate the drug discovery process by reducing the expenses but also change the way to design drugs. Computer-aided drug design (CADD) enables drug discovery based on the knowledge and functional properties of target structures. Structure-based and ligand-based drug design are two major CADD-based approaches. Systems biology is substantially proceeding with a better understanding of disease progression pathogenesis and the discovery of new therapeutic drugs. Computational and experimental systems biology methods approach in the pharmacy field provides a complete overview of molecular interactions between drugs and their respective targets. This approach assists in the establishment of novel drugs and more effective therapeutic strategies for patient treatment management. There are various clinically significant applications of systems biology in drug discovery including drug-target networks, predictions of drug-target interactions, investigations of the adverse effects of drugs, drug repositioning, and predictions of the drug combination.
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