title: Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.
abstract: Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.
authors: Department of Computer Science, University of York, York, United Kingdom.|York Computational Immunology Laboratory, University of York, York, United Kingdom.
authors: Richard A
authors: http://orcid.org/0000-0001-6333-9448
authors: RA
authors: Williams
authors: York Computational Immunology Laboratory, University of York, York, United Kingdom.|Department of Electronics, University of York, York, United Kingdom.
authors: Jon
authors: J
authors: Timmis
authors: Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom.|Affiliated, Department of Pathology, School of Medicine, University of Washington, Seattle, Washington, United States of America.
authors: Eva E
authors: EE
authors: Qwarnstrom
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007375:Interleukin-1 / Q000494:pharmacology; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D016328:NF-kappa B / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D049490:Systems Biology
chemical_list: D007375:Interleukin-1; D016328:NF-kappa B