ProBMoT is an implementation of the process-based modeling approach to modeling dynamical systems.
In the core of the process-based modeling approach is a formalism for representing models of dynamical systems as well as knowledge for modeling dynamical systems in a particular domain of interest. The process-based model formalism allows for representing models of dynamical systems at two levels of abstraction. At the higher level, the model is represented as sets of processes that govern the dynamics of the observed system and entities involved in the processes. At the lower level, each process includes a model of its dynamical influence on the variables of the observed system. The process-based modeling software can automatically combine the models of individual processes into a set of coupled differential equations used to simulate the behavior of the observed system. Thus, process-based models at the higher abstraction level reveal the structure of the observed systems in terms of entities and process interactions among them, providing explanation of the model behavior obtained by a lower-level declaration of the model equations.
To start establishing process-based models, we first have to formalize the modeling knowledge by establishing templates of generic entities that appear in the generic processes that govern the dynamics of systems in the particular domain. This modular knowledge representation allows for automated modeling of an observed system following a compositional approach. For a given modeling task, the generic templates are being instantiated into specific components (entities and processes) that can be used as building blocks for process-based models. Combinations of these building blocks represent candidate process-based models of the observed system. Automated modeling tool than searches for a process-based model with an optimal fit between the simulated and observed behavior of the system at hand.
ProBMoTs -> ProBMoTd BETA
- Reaction equations based formalism; Stochastic interpretation; Multi-objective optimization. Design of dynamical biological systems.
The source code is freely available upon request. Please contact us.
Sašo Džeroski, Ljupčo Todorovski 2007. Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data. Current Opinion in Biotechnology, 19: 360-368.
Darko Čerepnalkoski, Katerina Taškova, Ljupčo Todorovski, Nataša Atanasova, Sašo Džeroski, 2012. The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems . Ecological Modelling, 45:136-165
Mateja Škerjanec, Nataša Atanasova, Darko Čerepnalkoski, Sašo Džeroski, Boris Kompare, 2014. Development of a knowledge library for automated watershed modeling . Environmental Modelling & Software, 54:60-72
Jovan Tanevski, Ljupčo Todorovski, Yannis Kalaidzidis, Sašo Džeroski, 2015. Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis . BMC Systems Biology, 9:31
Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, 2015. Predicting long-term population dynamics with bagging and boosting of process-based models. Expert Systems with Applications 42(22):8484-8496