The BioMatLab at CNR IASI is active in the development, application and diffusion of mathematical modelling techniques for biomedicine. Our goal is to help provide reliable quantitative answers to physiological and pharmacological questions. The translation of biomedical problems into workable mathematical formulations and the estimation of relevant model parameters from available experimental data are our main concern.

History

In the year 2001 the Institute for Systems Analysis and Informatics (IASI) and the Center for the Study of the Pathophysiology of Shock (CFS), both organs of the Italian National Research Council (CNR), merged and the current IASI "A. Ruberti" was born. Pivotal in this merger was the long-standing collaboration of former IASI and CFS researchers on biomathematics research themes, specifically on the modelling of endocrine and metabolic systems.

BioMatLab, a laboratory specifically geared to research in biomathematics born in 1997 within CFS, is now part of IASI "A. Ruberti": it is located within the Catholic University School of Medicine, at the Policlinico Gemelli Hospital in Rome, next to the Institute for Clinical Surgery directed by Prof. Marco Castagneto (also Director of CFS).

Research Interests

The Laboratory is mainly dedicated to applied mathematical modelling on biomedical themes and to research in estimation techniques for nonlinear dynamical models and stochastic differential equations models. We have participated in several projects together with medical researchers (on the glucose-insulin system, on the metabolism of dicarboxylic and medium-chain fatty acids, on the distribution of resistances and compliances in the pulmonary vascular tree, on the production and elimination of Nitric Oxide in the airways, on MicroArrays, on noninvasive assisted ventilation, on antimicrobial efficacy, on indirect calorimetry, on the characterization of animal embrio development under sublethal toxicants), where we have provided the necessary mathematical and statistical expertise for experimental design, for data analysis and modelling, and for the conduction of multi-center clinical trials. We are very much interested, both from a practical and from a theoretical point of view, in statistical parameter estimation. We work on population parameter estimation, both frequentist and Bayesian, on parameter estimation for Stochastic Differential Equations models and on the relevance of measures of model nonlinearity for the computation of parameter confidence regions.