Computational Modeling and Simulations in Life Sciences

Concepts and Trends in Healthcare Information Systems - Annals of Information Systems ◽

10.1007/978-3-319-06844-2_9 ◽

2014 ◽

pp. 135-141

Author(s):

Athina A. Lazakidou ◽

Maria Petridou ◽

Dimitra Iliopoulou

Keyword(s):

Computational Modeling ◽

Life Sciences ◽

Modeling And Simulations

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Computational Modeling and Simulations of Biomolecular Systems

10.1142/12173 ◽

2021 ◽

Author(s):

Benoit Roux

Keyword(s):

Computational Modeling ◽

Modeling And Simulations ◽

Biomolecular Systems

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A Review of Multi-Scale Computational Modeling Tools for Predicting Structures and Properties of Multi-Principal Element Alloys

Metals ◽

10.3390/met9020254 ◽

2019 ◽

Vol 9 (2) ◽

pp. 254 ◽

Cited By ~ 4

Author(s):

Mohsen Beyramali Kivi ◽

Yu Hong ◽

Mohsen Asle Zaeem

Keyword(s):

Computational Modeling ◽

Density Functional ◽

Density Functional Theory Calculations ◽

Atomistic Simulations ◽

Modeling And Simulations ◽

Principal Element ◽

Modeling Tools ◽

Functional Theory ◽

Multi Scale ◽

Structures And Properties

Multi-principal element (MPE) alloys can be designed to have outstanding properties for a variety of applications. However, because of the compositional and phase complexity of these alloys, the experimental efforts in this area have often utilized trial and error tests. Consequently, computational modeling and simulations have emerged as power tools to accelerate the study and design of MPE alloys while decreasing the experimental costs. In this article, various computational modeling tools (such as density functional theory calculations and atomistic simulations) used to study the nano/microstructures and properties (such as mechanical and magnetic properties) of MPE alloys are reviewed. The advantages and limitations of these computational tools are also discussed. This study aims to assist the researchers to identify the capabilities of the state-of-the-art computational modeling and simulations for MPE alloy research.

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Qualitative Findings from Study of Interdisciplinary Education in Computational Modeling for Life Sciences Student Researchers from Emerging Research Institutions

2018 IEEE Frontiers in Education Conference (FIE) ◽

10.1109/fie.2018.8659183 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aasakiran Madamanchi ◽

Shenika V. Poindexter ◽

Monica E. Cardella ◽

James A. Glazier ◽

David M. Umulis

Keyword(s):

Computational Modeling ◽

Life Sciences ◽

Interdisciplinary Education ◽

Research Institutions ◽

Student Researchers

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Computational Modeling and Simulations in Life Sciences

International Journal of Systems Biology and Biomedical Technologies ◽

10.4018/ijsbbt.2013040101 ◽

2013 ◽

Vol 2 (2) ◽

pp. 1-7

Author(s):

Athina Lazakidou ◽

Maria Petridou ◽

Dimitra Iliopoulou

Keyword(s):

Mathematical Modeling ◽

Protein Expression ◽

Medical History ◽

Human Life ◽

Life Sciences ◽

Life Time ◽

Experimental Methods ◽

Modeling And Simulations ◽

Molecular Fingerprints ◽

Tissue Samples

Billions of math operations per second may be performed by computers anymore. Obviously, a human life-time would be needed to do the same number of computations. When used in medication, the groundbreaking potential of the mathematical modeling approach is obvious. In Medicine, mathematical modeling is able to vastly improve both drug creation and clinic technology. Progress in technology and the development of new experimental methods has had a noteworthy effect on the study of disease. This has raised new researching opportunities, such as: gathering in-depth ‘molecular fingerprints’ from patients carrying information, for example, on genotype, gene or protein expression, or metabolism levels; studying intracellular processes in living and diseased tissue through the control of gene activity inside the cells; and creating understandable illness-specific databases that include both patients’ medical history with laboratory and clinical data in addition to storing useful tissue samples. In this article, the authors attempt to provide the readers with a view of current and future use of mathematical modeling in medicine.

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