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Computational methods and scientific programming

The computational modeling history was first introduced several decades ago. Nowadays, a great number of methods for the construction, verification, and analysis of computational models have been developed in physics, geology, chemistry, and life sciences. Nevertheless, it is not always possible to directly translate the existing methods for pharmacology and bio-industry. Standard methods are often limited by the specific features of objects of interest and the research field.

Biological and biomedical objects can be characterized by:

  • High nonlinearity of the model in response to parameter changes;
  • The multicomponent nature of the system, with a number of negative and positive feedbacks;
  • Differentially scaled processes presented in one system: short- vs. long-term, small vs. large sizes;
  • Heterogeneous experimental data for model verification;
  • High cost and complexity of biological experiment techniques and, as a result, the lack of experimental data;
  • High statistical error of experimental data because of limitations of instruments and high variability of living systems.

The Institute for Systems Biology pays much attention to studying and testing of known and promising mathematical methods of modeling and data analysis. New approaches are used for our projects and are included in our new software development program.