Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling Language

Abstract

The domain-specific modeling and simulation language ML-Rules is aimed at facilitating the description of cell biological systems at different levels of organization. Model states are chemical solutions that consist of dynamically nested, attributed entities. The model dynamics are described by rules that are constrained by arbitrary functions, which can operate on the entities’ attributes, (nested) solutions, and the reaction kinetics. Thus, ML-Rules supports an expressive hierarchical, variable structure modeling of cell biological systems. The formal syntax and semantics of ML-Rules show that it is firmly rooted in continuous-time Markov chains. In addition to a generic stochastic simulation algorithm for ML-Rules, we introduce several specialized algorithms that are able to handle subclasses of ML-Rules more efficiently. The algorithms are compared in a performance study, leading to conclusions on the relation between expressive power and computational complexity of rule-based modeling languages.

Replicated Computations Results (RCR) Report for “Semantics and Efficient Simulation Algorithms for an Expressive Multi-Level Modeling Language”

“…ese new algorithms are demonstrated on substantial case studies. is replicated computation report focusses on the ML-Rules modelling tool, and speci cally the new algorithms as demonstrated in the case studies in the paper [1]. e so ware was straightforward to install and use and the experimental results from the paper could all be reproduced.…”

“…For the purposes of this replication report the focus was on the version of the ML-Rules tool corresponding to the paper [1]. e corresponding entry in the repository [4] provides the models which are used in the three presented case studies, the R scripts needed to create the plots and results as shown in the paper, as well as all the necessary so ware.…”

“…Firstly the ML-Rules tool was downloaded and installed and its functionality was investigated in a general sense. In the second stage the focus was explicitly on the results presented in the paper [1], "Semantics and E cient Simulation Algorithms of an Expressive Multi-Level Modeling Language".…”

ROS Dependent Wnt/β-Catenin Pathway and Its Regulation on Defined Micro-Pillars—A Combined In Vitro and In Silico Study

“…ML-Rules allows the specification and simulation of complex systems biology models at multiple levels of organization. The semantics of ML-Rules is based on continuous-time Markov chains (CTMC) [34]. For all simulation experiments performed in this study, a stochastic simulator that is based on the Stochastic Simulation Algorithm (SSA) was used [34].…”

“…The semantics of ML-Rules is based on continuous-time Markov chains (CTMC) [34]. For all simulation experiments performed in this study, a stochastic simulator that is based on the Stochastic Simulation Algorithm (SSA) was used [34]. For the specification and execution of the model SESSL (The Simulation Experiment Specification on a Scala Layer) [35] was used.…”