Direct computer mapping based modeling of a multiscale process involving p53/miR-34a signaling

Abstract

We studied a simplified multiscale biosystem with a new modeling and simulation methodology. The biosystem was a consciously, but arbitrarily selected multiscale part of the p53/miR-34a related signaling process that has an important role in tumor resistance, in cancer diagnostics, as well as in the therapy of various tumors. The multiscale model covered a vertical slice of the system from the change of a pathologic stage to the detailed dynamic molecular processes and vice versa. We employed the Direct Computer Mapping of process models for dynamic simulation of this typical multiscale, hybrid biosystem. The major advantage was the unified representation of the various quantitative and qualitative sub-models, as well as the easy combination of these various models within the unified simulating environment. Regardless to the limited number of components and interactions, the investigated fictitious illustrative example demonstrated many important and interesting features of the multiscale, hybrid biosystems. The model illustrates how the typical properties of the low level molecular events project onto the state properties of the higher scales. These properties (often called emergent properties) determine typical scenarios of lower scale states and actions. The simplified example, extracted from the independently developed, but coherent references, described some essential features about the modeled biological processes. We simulated the natural functioning of the p53/miR-34a signaling for the tumor suppression, as well as the various malfunctions of the system, resulting from tumor development. The simulation of addition of ectopic miR-34a demonstrates possible therapeutic intervention.Considering the uncertainties coming from the very limited set of modeled components and interactions, as well as from the roughly estimated numerical parameters, constraints and initial values, the simplified model worked feasibly. Probably, this came from the more or less correct consideration of the feedback loops in the compact low level quantitative model. The studied process provides a good example for combining conservational (material balance based) and informational (sign based) models. This helps to understand the relativistic notion of informational process, as a well defined subprocess of the conservational process. This sub-process consumes and produces less amount of conservational measures than its complement, but effects more on its complement than vice versa. Conscious consideration of conservation based informational processes is an important lesson from biosystem modeling for computational tools and from computer simulations for biosystem research.