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Data Mining Techniques in Imaging of Embryogenesis
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
Diptesh Mahajan, Gaurav K. Verma
Both these languages have similar goals, but digress with respect to their core abilities. CellML was primarily designed to create models, and hence can illustrate the mathematics of the cellular models in a more powerful and general way than SBML. SBML, on the other hand, was primarily designed to exchange datasets associated with pathways and reaction models, and hence can represent the models of biochemical reaction networks more profoundly than CellML. Although differing in core competencies, the development teams of both languages have a close working relationship, which ensures a synchronous development between languages.
Modelling practices from local to global
Published in Raimund Bleischwitz, Holger Hoff, Catalina Spataru, Ester van der Voet, Stacy D. VanDeveer, Routledge Handbook of the Resource Nexus, 2017
Enrique Kremers, Andreas Koch, Jochen Wendel
The following lessons learned can be drawn from today’s outcomes of multi-scale cancer modelling: Parameter estimation: Whereas more and more data is currently available, not always the correct parameters can be estimated or quantified at the needed scales. Two techniques are mainly used for parameter estimation: local optimisation (e.g. direct-search and gradient-based methods) and global optimisation (e.g. simulated annealing, branch and bound, and evolutionary algorithms)Computational power: Discrete or discrete-based hybrid models are typically computationally more intensive as continuous ones, because they can be too detailed in some time periods of the simulation. Adaptive hybrid modelling (with a dynamically changing granularity of the model to avoid these too-detailed periods) are currently in an early stage of development.Data sharing and model reusability: Data sharing of public-funded works has to be made better available to the research community. To avoid duplication and waste of resources a standardisation of both data and models is needed. In biochemistry, several XML-based standards (e.g. SBML, CellML or FieldML) are already defined and recognised for different purposes. However they do not cover yet all fields necessary for a more fluent exchange, so work is still ongoing.These lessons extracted from a distant, but currently very active and relevant field, are of interest to anticipate the modelling challenges arising for the nexus. Parameter estimation in a microscopic system has been identified as an important question. Laboratory experiments, such as the ones carried out for cancer research, can provide some reliable quantitative data. This data has still to be fitted to be conform to the parameters that the designed models use. A similar topic arises when dealing with nexus models, however, in macroscopic systems laboratory experiments that would, for example, represent a nexus or parts of it, are not at all realisable within a realistic scope. Real data and analysis has to be acquired from the field and from estimations. Furthermore, softer data is typically needed, when dealing with non-fully quantifiable systems, including especially social subsystems (e.g. socio-technical, socio-economical). This is an important difference from more quantitative sciences, where hard data is measurable and can be more easily transferred to models. Virtual simulation environments in which the system is represented in-silico might help to overcome these questions.
Construction of lumped-parameter cardiovascular models using the CellML language
Published in Journal of Medical Engineering & Technology, 2018
Yubing Shi, Patricia Lawford, D. Rodney Hose
The above example clearly demonstrates the potential of CellML as a handy tool to support the cardiovascular modelling. By using CellML, researchers can focus on the description of the model structure, without worrying about the model solution procedures. Also, as long as the model structure is clearly defined, the model can be run by different researchers and produce the same results, thus avoid the problems associated with the different solution algorithms and the human errors involved in the traditional modelling procedures. Besides, CellML and the accompanying modelling tools OpenCell/OpenCOR are all open to the research community for free downloading. We have coded the models introduced in this paper as well as some further application models (about the heart failure condition with different types of artificial heart support) using the CellML language and uploaded them to the CellML model repository for public access [19]. We have also applied these CellML models in the teaching of cardiovascular physiology to undergraduate medical students, which served as a powerful e-learning tool and significantly improved the student performance in a quiz on cardiovascular medicine, with a 33% and 22% increase in the number of questions answered correctly compared to the control group [20]. The CellML models developed can also be used by the clinicians to simulate the cardiovascular response in different disease conditions, thus to assist them in the diagnosis and treatment planning of cardiovascular lesions. For example, by changing the parameter values in the heart valves the models can be applied to predict the pressure/flow changes in the patient body in the valve incompetence and valve stenosis conditions; by reducing the ventricular contractility the model can simulate different levels of heart failure; by varying the vascular resistance and compliance values the model can mimic the circulatory response in the hypertension and hypotension conditions. The CellML models can also be used by medical device manufacturers in the design optimisation of cardiovascular prosthetics. In an example study, we have extended the CellML model to study the cardiovascular changes in the heart failure condition under the support of the Berlin Heart InCor ventricular assist device [21].