Models are abstracts of reality. They also represent prototypes. The need to include modelling and simulation in the Computer Science and Biological Science curriculum of some Universities in different continents, especially in some developing countries, has been an object of debate. They keep asking about the significance of modelling to Computer Science and Biological Sciences.
Science always proceeds by models . The model is required to test ideas and theories . The more accurate it is , the more it allows you to have correct answers by means of model perturbation. The representation of the model may be different from discipline to discipline, but in the basic sciences (physics, biology, chemistry, etc..) It is essentially mathematical. The mathematical model has the advantage of summarizing, in a precise and concise symbolic language a theory. Obviously, improving the theory, we improve the model. In recent decades, due to the great development of information technology, we can view models through many different graphic visualizations, but there is always the underlying mathematical model. The computational biology (not bioinformatics, which classically is concerned with the storage of data and their analysis) relies heavily on 3D representations of biological models, allowing even their perturbation (and thus obtaining biological predictions), but there is always behind the mathematical model .That's why it is important to include them in the curricula of universities.
Prognosis, diagnosis and simulation are important aspects of any modelling. These are the driving processes that any research and operational activities are comprised of.
Representation in the form of a successful model is the yard stick to understand that the process and their inter-relations are clearly understood. A model may be as simple as an algebraic equation or as complex as atmospheric or cellular automata. They are representations of our cognitive skills that can drive many more related studies.
Bioinformatics is a huge area of research these days. My alma mater has a large research group, and I would point you there for more information on how the two (computer science and biological science) intersect. http://bioinformatics.cse.sc.edu/ and http://bioinformatics.cse.sc.edu/index_files/Page347.htm
Science always proceeds by models . The model is required to test ideas and theories . The more accurate it is , the more it allows you to have correct answers by means of model perturbation. The representation of the model may be different from discipline to discipline, but in the basic sciences (physics, biology, chemistry, etc..) It is essentially mathematical. The mathematical model has the advantage of summarizing, in a precise and concise symbolic language a theory. Obviously, improving the theory, we improve the model. In recent decades, due to the great development of information technology, we can view models through many different graphic visualizations, but there is always the underlying mathematical model. The computational biology (not bioinformatics, which classically is concerned with the storage of data and their analysis) relies heavily on 3D representations of biological models, allowing even their perturbation (and thus obtaining biological predictions), but there is always behind the mathematical model .That's why it is important to include them in the curricula of universities.
Multifunctionality and homeostasis. Regularirites of the organism functioning, important for maintenance of homeostasis
V. A. Kotolupov, V. F. Levchenko
Well, here is what I think: Modeling (in CS and Biological Research) plays the role of reducing complexity and testing hypothesis that would rather be too complex and time-consuming to test experimentally. Once a model is proposed (perhaps with a few computer simulations), the findings can then be validated in a lab. experiment. In some way one can look at modeling as a guide to some gray or rather complex unknown experiments in biology. Models can also be used for experimental design (if you know how to do it, that is!). Long story short, the benefits are endless and are only limited by the modeler and biologists imaginations and ambitions. Good luck.
Hi all, Model helps to understand the phenomena, analyzing them quantitatively and qualitatively, making experiment, testing hypothesis, generating ideas, quantify the functional relationship between factors and parameters, and organizing and formulating systematic way of study patterns and phenomena.
model helps to understand, analyse process better. Only you have to understand process mathematics and do the experiment mathematically which saves your time for doing qualitative research.
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