Biosystem models, generated from a complex rule/reaction/influence network and from two functionality prototypes. (February 2017)

• Record Type: Journal Article
• Authors: Varga, M.; Prokop, A.; Csukas, B.
• Is Part Of: Bio systems. Volume 152(2017)
• Page Start: 24
• Page End: 43
• Publication Date: 2017-02
• Subjects: Reaction network -- Influence network -- Rule network -- State prototype -- Transition prototype -- Model generation -- Editable graphical model -- Quantitative model -- Qualitative model -- Multiscale model -- p53 signalling at cancer; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2016.12.005
• Languages: English
• ISSNs: 0303-2647

Abstract

Highlights: A transition based, unified representation of biosystem networks is suggested. Two general model prototypes for these biosystem networks are developed. A new generation method for a graphically editable model is developed. All above is implemented in an improved methodology of Direct Computer Mapping. It is tested for the improved implementation of a signalling biosystem model. Abstract: In this work we have further developed the Direct Computer Mapping (DCM) based modelling and simulation methodology. A unified, transition-based representation of complex rule, reaction and influence networks has been introduced and two prototypes (one general state- and another general transition-prototype) have been developed for the unified functional modelling of the state and transition nodes. Starting from the network and from the functional prototypes, an automatic generation method of the graphically editable and extensible GraphML description of biosystem models has been elaborated. The new developments have been implemented in the improved kernel of DCM models. The applied knowledge representation makes possible the unified generation and execution of the balance-based quantitative and influence- or rule-based qualitative, as well as optionally time-driven, multiscale biosystem models. Application of the developed methodology has been illustrated by the improved implementation of the formerly studied and upgraded example biosystem model for combining the detailed, quantitative p53/miR34a signalling system with the pathological model through an extended rule-based coupling model.