A New Visualization and Analysis Method for a Convolved Representation of Mass Computational Experiments with Biological Models

Modern computational biology makes widespread use of mathematical models biological systems, in particular systems ordinary differential equations, as well dynamic described other formalisms, such agent-based models. Parameters are numerical values quantities reflecting certain properties a modeled system and affecting model solutions. At the same time, depending on parameter values, different regimes—stationary or oscillatory, established result transient modes various types—can be observed system. Predicting changes solution dynamics type parameters is an important scientific task. Nevertheless, this problem does not have analytical for all formalisms general case. The routinely used method performing series experiments, i.e., solving direct problems with sets followed by expert analysis plots labor-intensive large number decreasing step parametric grid. In regard, development methods allowing obtainment information set experiments aggregate form relevant. This work devoted to developing visualization classification regimes using composition time warping (DTW-algorithm) principal coordinates (PCoA) methods. enables qualitative results solutions performance correspondence between its has been tested Lotka–Volterra artificial dynamics.