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Computational Biology & Bioinformatics
Computational Biology and Bioinformatics pact with application of computers in solving problems of
molecular biology. Computational biology stretches several classical areas such as biology, physics, chemistry,
computer science, statistics and, and the activities in the area are numerous.
The area of computational biology is also indicated to as bioinformatics. These two names are used
interchangeably, but there seems to be a consensus forming where computational biology is used to indicate to
activities which mainly indicates on constructing algorithms that address problems with biological purpose,
while bioinformatics is used to refer to activities which mainly target on constructing and using computational
technique to analyze possible biological data.
The work of producing algorithms that solves problems with biological relevance, that is, the work of
constructing algorithms in computational biology, consists of two collaborating steps. The first step is to create
a biological fascinating question and to produce a prototype of the biological reality that makes it feasible to
formulate the created question as a computational problem. The second step is to produce an algorithm that
solves the constructed computational problem.
The first step involves knowledge of the biological actuality, while the second step involves knowledge of
algorithmic theory. The quality of the produced algorithm is commonly measured by standard algorithmic
approach in terms of the resources, most prominently space and time.
Constructing a good algorithm that solves a problem with biological purpose is therefore an interdisciplinary
activity that involves exchange between modeling the biological reality and producing the algorithm, until a
logical balance between the running time and space presumption of the algorithm, and the biological relevance
of the answers it creates, is accomplished. The degree of interchanging between modeling and producing of
course depends on how closely associated the problem addressed by the algorithm is to a particular biological
application, and therefore how relevant it is to examine the algorithm by the biological relevance of the
answers it creates.
Structure of Bioinformatics and Computational Biology Unit
The details of a particular model and algorithm of course depend on the questions being interrogate. Most
questions in computational biology are associated to molecular or evolutionary biology and focus on
evaluating and comparing the composition of the key bio molecules RNA, DNA and proteins, that together
constitute the major building blocks of organisms. The success of current efforts to produce and use tools for
getting data about the configuration of these bio molecules, most prominently DNA sequencing methods for
extracting the genetic material from DNA molecules, has resulted in a flood of available biological data to
analyze and compare.
Brainware University

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Computational Biology & Bioinformatics

  • 1. Computational Biology & Bioinformatics Computational Biology and Bioinformatics pact with application of computers in solving problems of molecular biology. Computational biology stretches several classical areas such as biology, physics, chemistry, computer science, statistics and, and the activities in the area are numerous. The area of computational biology is also indicated to as bioinformatics. These two names are used interchangeably, but there seems to be a consensus forming where computational biology is used to indicate to activities which mainly indicates on constructing algorithms that address problems with biological purpose, while bioinformatics is used to refer to activities which mainly target on constructing and using computational technique to analyze possible biological data. The work of producing algorithms that solves problems with biological relevance, that is, the work of constructing algorithms in computational biology, consists of two collaborating steps. The first step is to create a biological fascinating question and to produce a prototype of the biological reality that makes it feasible to formulate the created question as a computational problem. The second step is to produce an algorithm that solves the constructed computational problem. The first step involves knowledge of the biological actuality, while the second step involves knowledge of algorithmic theory. The quality of the produced algorithm is commonly measured by standard algorithmic approach in terms of the resources, most prominently space and time. Constructing a good algorithm that solves a problem with biological purpose is therefore an interdisciplinary activity that involves exchange between modeling the biological reality and producing the algorithm, until a logical balance between the running time and space presumption of the algorithm, and the biological relevance of the answers it creates, is accomplished. The degree of interchanging between modeling and producing of course depends on how closely associated the problem addressed by the algorithm is to a particular biological application, and therefore how relevant it is to examine the algorithm by the biological relevance of the answers it creates. Structure of Bioinformatics and Computational Biology Unit The details of a particular model and algorithm of course depend on the questions being interrogate. Most questions in computational biology are associated to molecular or evolutionary biology and focus on evaluating and comparing the composition of the key bio molecules RNA, DNA and proteins, that together constitute the major building blocks of organisms. The success of current efforts to produce and use tools for
  • 2. getting data about the configuration of these bio molecules, most prominently DNA sequencing methods for extracting the genetic material from DNA molecules, has resulted in a flood of available biological data to analyze and compare. Brainware University