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Computational biomodeling Main article: Modelling biological systems Computational biomodeling is a field concerned with building computer models of biological systems. Computational biomodeling aims to develop and use visual simulations in order to assess the complexity of biological systems. This is accomplished through the use of specialized algorithms, and visualization software. These models allow for prediction of how systems will react under different environments. This is useful for determining if a system is robust. A robust biological system is one that “maintain their state and functions against external and internal perturbations”,[6] which is essential for a biological system to survive. Computational biomodeling generates a large archive of such data, allowing for analysis from multiple users. While current techniques focus on small biological systems, researchers are working on approaches that will allow for larger networks to be analyzed and modeled. A majority of researchers believe that this will be essential in developing modern medical approaches to creating new drugs and gene therapy.[6] Computational genomics (Computational genetics) A partially sequenced genome. Main article: Computational genomics Computational genomics is a field within genomics which studies the genomes of cells and organisms. It is often referred to as Computational and Statistical Genetics. The Human Genome Project is one example of computational genomics. This project looks to sequence the entire human genome into a set of data. Once fully implemented, this could allow for doctors to analyze the genome of an individual patient.[7] This opens the possibility of personalized medicine, prescribing treatments based on an individual’s pre-existing genetic patterns. This project has created many similar programs. Researchers are looking to sequence the genomes of animals, plants, bacteria, and all other types of life.[8] One of the main tools used in comparing the genomes is homology. Homology is observing the same organ across species and seeing what different functions they have. Research suggests that between 80 to 90% of sequences genes can be identified this way. In order to detect potential cures from genomes, comparisons between genome sequences of related species and mRNA sequences are drawn. This method is not completely accurate however. It may be necessary to include the genome of a primate in order to improve current methods of unique gene therapy.[8] This field is still in development. An untouched project in the development in computational genomics is analyzing intergenic regions. Studies show that roughly 97% of the human genome consists of these regions. There are no current methods for determining possible implications of these sequences. Computational genomics will look to expand research in this area and develop new numerical and computational approaches to sequencing these regions.[8] Computational neuroscience Main article: Computational neuroscience Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is a subset of the field of neuroscience, and looks to analyze brain data to create practical applications.[9] It looks to model the brain in order to examine specific types aspects of the neurological system. Various types of models of the brain include: Realistic Brain Models: These models look to represent every aspect of the brain, including as much detail at the cellular level as possible. Realistic models provide the most information about the brain, but also have the largest margin for error. More variables in a brain model create the possibility for more error to occur. These models do not account for parts of the cellular structure that scientists do not know about. Realistic brain models are the most computationally heavy and the most expensive to implement.[10] Simplifying Brain Models: These models look to limit the scope of a model in order to assess a specific physical property of the neurological system. This allows for the intensive computational problems to be solved, and reduces the amount of potential error from a realistic brain model.[10] It is the work of computational neuroscientists to improve the algorithms and data structures currently used to increase the speed of such calculations. Computational pharmacology Computational pharmacology is “the study of the effects of genomic data to find links between specific genotypes and diseases and then screening drug data”.[11] The pharmaceutical industry requires a shift in methods to analyze drug data. Pharmacists were able to use Microsoft Excel to compare chemical and genomic data related to the effectiveness of drugs. However, the industry has reached what is referred to as the Excel barricade. This arises from the limited number of cells accessible on a spreadsheet. This development led to the need for computational pharmacology. Scientists and researcher develop computational methods to analyze these massive data sets. This allows for an efficient comparison between the notable data points and provide for a more accurate drugs to be developed.[12] Analysts project that if major medications fail due to patents, that computational biology will be necessary to replace current drugs on the market. Doctoral students in computational biology are being encouraged to pursue careers in industry rather than take Post-Doctoral positions. This is a direct result of major pharmaceutical companies needing more qualified analysts of the large data sets required for producing new drugs.[12] Computational evolutionary biology Computational biology has assisted the field of evolutionary biology in many capacities. This includes: Using DNA data to evaluate the evolutionary change of a species over time. Taking the results of computational genomics in order to evaluate the evolution of genetic disorders within a species. Build models of evolutionary systems in order predict what types of changes will occur in the future.[13] One method of representing this subfield of computational biology is through the use of trees. A tree is a data structure that splits nodes based on a predefined rule. This tree, developed by M.R. Hezinger, V. King, and T.Warnow implements traversal of evolutionary information in less than polynomial time. This is a particularly quick method, as opposed to some modern methods that take longer than O(n^2) time. These trees have multiple applications to questions in computational evolutionary biology.[14] Cancer computational biology Cancer computational biology is a field that aims to determine the future mutations in cancer through an algorithmic approach to analyzing data. Research in this field has led to the use of high-throughput measurement. High throughput measurement allows for the gathering of millions of data points using robotics and other sensing devices. This data is collected from DNA, RNA, and other biological structures. Areas of focus include determining the characteristics of tumors, analyzing molecules that are deterministic in causing cancer, and understanding how the human genome relates to the causation of tumors and cancer.[15] |
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