Biomedical subjects

L Ironi

Publications and source records attributed to L Ironi.

5 recordsLinked to original sources

PubMed2001

A hybrid input-output approach to model metabolic systems: an application to intracellular thiamine kinetics.

Models of the dynamics of complex metabolic systems offer potential benefits to the deep comprehension of the system under study as well as for the performance of certain tasks. Unfortunately, dynamic modeling of a great deal of metabolic systems may be problematic due to the incompleteness of the available knowledge about the underlying mechanisms and to the lack of an adequate observational data set. In theory, a valid alternative to classical structural modeling through ordinary differential equations could be represented by input-output approaches. But, in practice, such methods, which learn the nonlinear dynamics of the system from input-output data, fail when the experimental data set is poor either in size or in quality. Such a situation is not rare in the case of metabolic systems. This paper deals with a hybrid approach which aims at overcoming the problems addressed above. More specifically, it allows us to solve the identification problems of the intracellular thiamine kinetics in the intestine tissue. The method, which is half way between the structural and input-output approach, uses the outcomes of the simulation of a qualitative structural model to build a good initialization of a fuzzy system identifier. Such an initialization allows us to efficiently cope with both the incompleteness of knowledge and the inadequacy of the available data set, and to derive an input-output model of the intracellular thiamine kinetics in the intestine tissue. The comparison of the predictions of the intracellular thiamine kinetics obtained by the application of such a model with those obtained by traditional approaches, namely compartmental models, neural networks, and fuzzy systems, highlighted a better performance of our model. As the structural assumptions are relaxed, we obtained a model slightly less informative than a purely structural one but robust enough to be used as a simulator. The paper also discusses the interpretative potential offered by such a model, as tested on diabetic subjects.

Animals↗

PubMed1999

Model-based interpretation of creep profiles for the assessment of polymer-mucin interaction.

PURPOSE: This paper presents a new rheological approach, based on a stationary viscoelastic test (creep test), to describe the interaction between a mucoadhesive polymer and mucin. METHODS: An automated model builder tool is used to identify accurate compliance models from experimental data. This approach is applied to study the interaction of gastric porcine mucin with three viscosity grades of a mucoadhesive polymer, sodium carboxymethylcellulose. RESULTS: By comparing the compliance models of polymer solutions and their mixtures with mucin, prepared at different concentrations and concentration ratios, we observed an increase in the order of the model of the mixtures at the lowest polymer concentration for all the three viscosity grades; this effect is more pronounced for the low viscosity grade. On the other hand, an increase in mucin concentration does not result in a further increase in model order, but rather in a decrease in retarded compliance and an increase in newtonian viscosity. CONCLUSIONS: We interpret the model order as the number of different interactions between polymer and mucin, and the parameter values as a measure of their strength. The results indicate the suitability of the approach for a deep characterization of the interactions involved in mucoadhesion.

Adhesiveness↗

PubMed1994

A framework for building and simulating qualitative models of compartmental systems.

This paper describes a framework, called QCMF (Qualitative Compartmental Modeling Framework), which assists the user in formulating models of a pathophysiological system and in analyzing their behaviors through the simulation of the effects of a variety of pathogenetic mechanisms and therapeutical treatments. QCMF has adopted the compartmental theory as modeling ontology: a system is represented as a finite set of interacting compartments. The user enters, through an iconic language and menus, the compartmental structure of a pathophysiological system and the definition of the kinds of functional relationships describing the interactions between compartments. Then, QCMF automatically generates a behavior model of the system. Such a model consists of a set of ordinary differential equations, which are qualitatively expressed, and is directly coded into the language which is interpreted by the simulation algorithm. The system behavior can be obtained by simulating the model starting from an initial state which describes the perturbations acting on the system. The code defining the initial state is automatically built by QCMF as well. Finally, explanations of the predicted behavior are also automatically generated.

Algorithms↗

PubMedDate not supplied

Learning from biomedical time series through the integration of qualitative models and fuzzy systems.

Our work deals with a method for the identification of the dynamics of nonlinear (patho-)physiological systems by learning from data. The key idea which underlies our approach consists in the integration of qualitative modeling methods with fuzzy logic systems. The major advantage which derives from such an integrated framework lies in its capability both to represent the structural knowledge of the system at study and to determine, by exploiting the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. We have successfully applied our method in the identification of the intracellular kinetics of thiamine from data collected in the intestine cells.

Artificial Intelligence↗

PubMedDate not supplied

Qualitative models and fuzzy systems: an integrated approach for learning from data.

This paper presents a method for the identification of the dynamics of non-linear systems by learning from data. The key idea which underlies our approach consists of the integration of qualitative modeling techniques with fuzzy logic systems. The resulting hybrid method exploits the a priori structural knowledge on the system to initialize a fuzzy inference procedure which determines, from the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. The major advantage which results from such an integrated framework lies in a significant improvement of both efficiency and robustness of identification methods based on fuzzy models which learn an input output relation from data. As a benchmark of our method, we have considered the problem of identifying the response to the insulin therapy from insulin-dependent diabetic patients: the results obtained are presented and discussed in the paper.

Algorithms↗