Computational Modeling and Simulations in Life Sciences

Computational Modeling and Simulations in Life Sciences

Athina Lazakidou (Department of Nursing, Faculty of Human Movement and Quality of Life Sciences, University of Peloponnese, Sparta, Greece), Maria Petridou (School of Computer Science & IT, The University of Nottingham, Nottingham, UK) and Dimitra Iliopoulou (Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece)
DOI: 10.4018/ijsbbt.2013040101


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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Computer modeling is the process via which a computer is a tool to create a mathematical model of a complicated system or process. Computer modeling is satisfactory for taking into consideration all the separate parameters and for simplifying and organizing realistic processes. Computational analysis and modeling can aid in understanding the data gathered. Molecular fingerprints can also be utilized for the recognition of biomarkers warning for a higher risk of acquiring a disease or corroborating clinical results. Healthcare simulation may also be used for purposes other than comparing scenarios or showing problems in medical processes. A simulation model may be implemented as a part of continuous efforts to record and improve the performance and heighten the efficiency. For such a reason, a simulation model is developed not only for experimenting, but also to employ running the information systems of the organization. A great advantage of simulation is seen when the simulation models become a complete part in the daily business of healthcare delivery, i.e., the current information system applications on which the daily operation of the healthcare provider is based on. Basically, the project's goal is not to use simulation as an instrument for conducting test-experiments once when an important change is due to happen, but making the simulation models run along the other procedures as a routine part of the everyday work environment. Simulation has wide field-potential in healthcare, which can be divided in specific major directions, based on certain disciplines and sub-disciplines. Each direction may include sub-directions of its own. Below we are referred to a more universal classification of healthcare simulation that includes four types of simulation:

  • Clinical Simulation: Simulation is mostly incorporated for researching, decomposing and replicating the symptoms of specific diseases along with biological processes in the human body.

  • Operational Simulation: Simulation is mainly used for recording, analyzing, and researching healthcare operations, healthcare operations, scheduling, as well as the healthcare business processes, and the patient flow.

  • Managerial Simulation: Simulation is mostly used to handle management, to make decisions, to implement policies, and to plan strategically.

  • Educational Simulation: Simulation can also be utilized for training and educating, where virtual environments and virtual and actual objects are confoundedly utilized to enhance and improve the simulation experiment.

Computational Models For Medical Image Analysis

Medical Image Analysis (MIA) is essential for the diagnosis, organizing, management and after-treatment of therapy. MIA can be supported by developing specific computational models of the human body operating at a variety of levels so as to improve its efficiency when combined with the medical robotics. Medical Imaging is utilized for planning and regulating the motion of medical robots. For instance, utilizing pre-operative images and geometric reasoning for path planning, combining the utilization of pre-operative and intra-operative images to guide a surgical procedure with enhanced reality visualization, or the simulation of surgical interferences on virtual organs created from pre-operative images and atlases with realistic visual and dynamic feedback. Such procedures require the use of advanced medical image analysis methods and the development of a hierarchy of computational models of the human body (Ayache, 2004). These computational models seek to recreate the geometrical, physical and physiological properties of the human organs and systems on various levels. They may be used in combination with medical images and robotics to realistically increase the possibilities of image analysis and robot control. In this chapter the potential use of computational models for a variety of advanced medical applications, such as image guided, robot-assisted and simulated medical interventions is presented.

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