The Methodology and Areas of Computational Sciences

John Fink^*
*Correspondence: John Fink, Department of Information Science, University of Oslo, Norway, Email:

Introduction

A mathematical model can describe complex systems. These models are used to create algorithms. To describe and solve the behavior of a physical system, these models and simulations are run in a computing environment. Essentially, the high processing capabilities of computing software and hardware allow for the simultaneous execution of many what-if statements to produce results. The massive data processing capabilities also enable analysis of the massive amounts of data generated by these simulations or analyses. Any natural phenomenon, process, or system can be studied using computational science. This means that it collaborates with other branches of science to solve scientific problems [1-3]. Computational physics is the intersection of computer science, applied mathematics, and physics, while computational chemistry is the intersection of computer science, applied mathematics, and theoretical chemistry. The application of computational science has resulted in the formation of several distinct disciplines.

Computational Biology is the study of biological systems such as anatomical, ecological, evolutionary, behavioral, and social systems. Here are a few of the many subcategories of computational biology. Computational methods for gene sequencing have been used, and computational science is partly responsible for our current understanding of genomes. Computational genomics applications include comparative genomics, gene expression analysis, gene evolution research, mining biosynthetic gene clusters, and so on.

Description

Computational Biology has several applications in medicine because it aids in the prediction of human system behavior. Computational oncology, for example, can aid in better understanding of the disease by analyzing patterns in tumour growth and progression. It can also be used to study the effects of drugs, such as predicting drug responses in cancer cells using computational methods. Computational immunology, which aids in the understanding of the immune system, can also aid in the understanding of diseases and their consequences. Computational biology can be used to model biological systems, including the human body, from the cellular to the organismal level.

Brain activity and protein structures can all be modeled and used for research. The structure and chemistry of biomolecules are becoming easier to study as technology advances, and there have been several advances in bio molecular modeling in recent years. Ecological models can also be created, providing insight into the relationships that exist within ecosystems. The study and analysis of flora and fauna behavioral patterns can help us better understand environmental changes. Changes in migratory patterns of birds, for example, can provide insight into the progress of global warming and how it affects them. Computational chemistry [4,5] is the study and prediction of chemical reactions, as well as the understanding of molecular structures and properties, using computers to solve chemical problems. Computational chemistry employs chemistry as well as quantum mechanics. The Schrodinger equation serves as the foundation for the majority of algorithms in computational chemistry.

Conclusion

Pharmaceuticals is one of the most important and well-known applications of computational chemistry for determining molecular interactions. Drug development can benefit from computational modeling. Simulations can be used to predict the effects of changing a drug molecule. This, combined with bio modeling of disease progression, makes drug design more efficient and faster. Computational physics studies and analyses physical problems using computation and modeling. The applications of computational models in physics are numerous, and they are used in almost all branches of physics. These are a few examples. CFD is a branch of fluid mechanics that uses computational models to analyze fluid flow.

CFD uses a variety of approaches, but the basic methodology remains the same. Before the simulation is solved iteratively, the system's CAD model is created, a mesh is created, and boundary conditions are defined. Computational finance has grown in popularity in recent years and now pervades all aspects of finance. Making the best investments is something that computational finance specialists frequently assist with. Computational science is well suited to solving optimization problems, which necessitate trial and error through numerous iterations. Portfolio optimization, asset pricing, and risk analysis, for example, can all be done quickly and efficiently using computing.

References

1. Kelton, K.F and Daan Frenkel. "Preface: special topic on nucleation: New concepts and discoveries." J Chem Phys 145 (2016): 211501.

Google Scholar, Crossref, Indexed at

2. Bai, Guoying, Dong Gao and Zhang Liu. "Probing the critical nucleus size for ice formation with graphene oxide nanosheets." Nat 576 (2019): 437-441.

Google Scholar, Crossref, Indexed at

3. Zhang, Tian Hui, and Xiang Yang Liu. "How does a transient amorphous precursor template crystallization." J Am Chem Soc 129 (2007): 13520-13526.

Google Scholar, Crossref, Indexed at

4. Haleblian, John and Walter McCrone. "Pharmaceutical applications of polymorphism." J Pharm Sci 58 (1969): 911-929.

Google Scholar, Crossref, Indexed at

5. Fours, Baptiste, Yohann Cartigny and Samuel Petit. "Formation of new polymorphs without any nucleation step. Desolvation of the rimonabant monohydrate: directional crystallisation concomitant to smooth dehydration." Faraday Discuss 179 (2015): 475-488.

Google Scholar, Crossref, Indexed at