Humans are complex systems: from a biological cell made of thousands of different molecules that work together, to billions of cells that build our tissue, organ, and system to our society, 6 billion unique interacting individuals [2]. Such complex systems are not made of identical and undistinguishable components: rather each gene in a cell, each cell in the immune system and each individual have their own characteristic behavior and provide unique value contributions to the systems in which they are constituents. Therefore, understanding, quantifying and handling the endless signatures of order, disorder, self organization and self annihilation of biological systems is one of the biggest scientific challenges of our time [2]. However, techniques …show more content…
With this increasing rate of problems in fields of biomedicine all over the globe, the chances of diseases associated with it are also increasing. Such as diabetic retinopathy is one of the disabling micro vascular complications of diabetes mellitus that causes the loss of central vision. Similarly, complex fluid structure interaction problem of blood flow in cardiovascular system and many more like it, needs to be examined by modeling and simulation using computational techniques. These methods provide a practical platform for a better integration of the different biological and medical disciplines, both for practice and research. Moreover, the course work and research during PhD studies will enable to solve such kind of problem and also it will be a contribution to …show more content…
Numerical Approximation and Analysis
3. Develop and Implement of Algorithm
4. Computational Efficiency and Performance
5. Visualization for Simulation of Model
6. Validation and Verification of Proposed Model
7 Areas of Application
Application areas of Computational Modeling and Simulation in Biomedicine are as follow:
1. Medical information systems (electronic patient records)
2. Biostatistics (design and analysis of clinical studies and clinical trials)
3. Medical decision-support systems (diagnosis assistance and critiquing)
4. Biomedical image analysis (radiography, nuclear magnetic resonance)
5. Biomedical signal processing (electroencephalography, electrocardiography and also as an essential initial step for image analysis)
6. Biomedical systems and control (intelligent prostheses, intelligent drug delivery devices)
7. Statistical genetics and epidemiology (gene mapping, single nucleotide polymorphism analysis)
8. Computational structural biology (prediction of protein structure from sequence)
9. Biological databases and information technology (gene and protein databases)
10. Bioinformatics and computational biology (statistical data analysis strategies for molecular biology and in silico