Wiener in a seminal book (Wiener, 1948) associated the ancient Greek word ‘κυβερνητικος’ to the control of physiological systems. “Thus, as far back as four years ago, the group of scientists about Dr. Rosenblueth and myself had already become aware of the essential unity of the set of problems centering about communication, control and statistical mechanics, whether in the machine or in the living tissue. [...] We have decided to call the entire field [...] by the name Cybernetics, which we form from the Greek κυβερνητης or steersman. In choosing this term, we wish to recognize that the first significant paper on feed-back mechanisms is an article on governors, which was published by Clerk Maxwell in 1868 and that governor is derived from a Latin corruption of κυβερνητης. We also wish to refer to the fact that the steering engines of a ship are indeed one of the earliest and best developed forms of feed-back mechanisms.” The increasing knowledge in each sector of science led to a huge diversification of scientific research, especially in a borderline sector like cybernetics applied to physiological systems. A first problem to solve was the following: let’s suppose that two groups, one with a control engineering experience and the other one with a medical background (e.g., physicians), decide to cooperate, because they strongly believe that a joined research could be useful for developing mathematical and statistical models. Usually physicians do not have enough time to study and apply advanced modelling. Wiener approached the communication between scientists belonging to different disciplines: “If a physiologist, who knows no mathematics, works together with a mathematician, who knows no physiology, the one will be unable to state his problem in terms that the other can manipulate, and the second will be unable to put the answers in any form that the first can understand. [...] The mathematician need not have the skill to conduct a physiological experiment, but he must have the skill to understand one, to criticize one, and to suggest one. The physiologist need not be able to prove a certain mathematical theorem, but he must be able to grasp its physiological significance and to tell the mathematician for what he should look.” A correct interaction in terms of a clear communication and reciprocal comprehension of the objectives of the research activity between groups with different competences is a crucial aspect in any interdisciplinary research. In 2003 at the University of Pisa it was decided to introduce a new course for undergraduate students in biomedical engineering, based on the Wiener ‘utopia’, in order to teach a novel discipline useful for helping biomedical students to communicate and cooperate effectively with physicians. We named this new course as Physiological Cybernetics, remembering the old Wiener definition. The organization of this course was a difficult task, and it required to gain experience in order to integrate so different disciplines and to produce a common language between students in biomedical engineer and physicians. At a first glance this attempt seemed to be too ambitious, because the different approaches of biomedical engineers with respect to physicians seemed incompatible and even the languages of the two groups were so different to remember the Babel tower… A great deal of effort and attention was required to produce appealing and stimulating lectures, but after many years we can affirm that this challenge is successful, especially for the enthusiastic answers of the students: their number was increasing year after year (about seventy students per year are now attending the course). A strict and trusted cooperation between different groups of physicians is growing up and several groups of physicians belonging to different medical fields are going to join us for new interactions. The aim of this chapter is to describe how the approach to physiological cybernetics has led to integrate academic lessons with research activities. To be more specific, the basic idea of Physiological Cybernetics was to search for models able to emulate physiological systems based on the feedback theory and/or the system theory. In fact, recently, the widespread use of friendly software packages for modelling, along with the development of powerful identification and control techniques has led to a renewed interest in control (Khoo, 2011; Hoppensteadt & Peskin, 2002; Cobelli & Carson, 2008) and identification (Westwick & Kearney, 2003) of physiological systems. Unfortunately physiological systems are intrinsically time variant and highly non linear. Moreover, an effective balance of the model complexity is a difficult task: low order models are usually too simple to be useful, on the other hand high order models are too complex for simulation purposes and they have too many unknown parameters to be identified. Each model selected for investigation was studied by a group of biomedical students supervised by physicians. Each model required several iterations and reformulations, due to the continuous adjustment of the research objectives, changing their final horizon, because of the gap between experimental data and theoretical models, so that the answers to the doubts and questions were continuously moving with the obtained partial results. A final goal of the research was to apply a mathematical framework for helping medical diagnostic techniques and for testing new therapeutic protocols. The procedure of model extraction followed two main pathways: the first one (pathway A) led to a formulation of a mathematical model usually based on differential equations and on an as deep as possible insight into physiological mechanisms (Marmarelis, 2004; Ottesen et al., 2004; Edelstein-Keshet, 2005; Jones et al., 2009) via a physical description of the system. The second one (pathway B) was founded on a model description based on a black-box and data-driven identification (Westwick & Kearney, 2003; Cobelli & Carson, 2008), usually leaving to stochastic models with a parametric or non-parametric structure (Ljung, 1987), depending on the a-priori knowledge of constitutive laws governing the observed system. In this paper we will describe two examples of research activity based on the Physiological Cybernetics, both of them addressed to produce a biomedical framework for predicting the effects of therapeutic actions, but following the two different pathways. The first example follows a statistical non parametric approach, the second one a mathematical model based on differential equations.

`http://hdl.handle.net/11568/146278`

Titolo: | Physiological Cybernetics: An Old-Novel Approach for Students in Biomedical Systems |

Autori: | Landi A; Laurino M; Piaggi P |

Autori: | |

Anno del prodotto: | 2011 |

Abstract: | Wiener in a seminal book (Wiener, 1948) associated the ancient Greek word ‘κυβερνητικος’ to the control of physiological systems. “Thus, as far back as four years ago, the group of scientists about Dr. Rosenblueth and myself had already become aware of the essential unity of the set of problems centering about communication, control and statistical mechanics, whether in the machine or in the living tissue. [...] We have decided to call the entire field [...] by the name Cybernetics, which we form from the Greek κυβερνητης or steersman. In choosing this term, we wish to recognize that the first significant paper on feed-back mechanisms is an article on governors, which was published by Clerk Maxwell in 1868 and that governor is derived from a Latin corruption of κυβερνητης. We also wish to refer to the fact that the steering engines of a ship are indeed one of the earliest and best developed forms of feed-back mechanisms.” The increasing knowledge in each sector of science led to a huge diversification of scientific research, especially in a borderline sector like cybernetics applied to physiological systems. A first problem to solve was the following: let’s suppose that two groups, one with a control engineering experience and the other one with a medical background (e.g., physicians), decide to cooperate, because they strongly believe that a joined research could be useful for developing mathematical and statistical models. Usually physicians do not have enough time to study and apply advanced modelling. Wiener approached the communication between scientists belonging to different disciplines: “If a physiologist, who knows no mathematics, works together with a mathematician, who knows no physiology, the one will be unable to state his problem in terms that the other can manipulate, and the second will be unable to put the answers in any form that the first can understand. [...] The mathematician need not have the skill to conduct a physiological experiment, but he must have the skill to understand one, to criticize one, and to suggest one. The physiologist need not be able to prove a certain mathematical theorem, but he must be able to grasp its physiological significance and to tell the mathematician for what he should look.” A correct interaction in terms of a clear communication and reciprocal comprehension of the objectives of the research activity between groups with different competences is a crucial aspect in any interdisciplinary research. In 2003 at the University of Pisa it was decided to introduce a new course for undergraduate students in biomedical engineering, based on the Wiener ‘utopia’, in order to teach a novel discipline useful for helping biomedical students to communicate and cooperate effectively with physicians. We named this new course as Physiological Cybernetics, remembering the old Wiener definition. The organization of this course was a difficult task, and it required to gain experience in order to integrate so different disciplines and to produce a common language between students in biomedical engineer and physicians. At a first glance this attempt seemed to be too ambitious, because the different approaches of biomedical engineers with respect to physicians seemed incompatible and even the languages of the two groups were so different to remember the Babel tower… A great deal of effort and attention was required to produce appealing and stimulating lectures, but after many years we can affirm that this challenge is successful, especially for the enthusiastic answers of the students: their number was increasing year after year (about seventy students per year are now attending the course). A strict and trusted cooperation between different groups of physicians is growing up and several groups of physicians belonging to different medical fields are going to join us for new interactions. The aim of this chapter is to describe how the approach to physiological cybernetics has led to integrate academic lessons with research activities. To be more specific, the basic idea of Physiological Cybernetics was to search for models able to emulate physiological systems based on the feedback theory and/or the system theory. In fact, recently, the widespread use of friendly software packages for modelling, along with the development of powerful identification and control techniques has led to a renewed interest in control (Khoo, 2011; Hoppensteadt & Peskin, 2002; Cobelli & Carson, 2008) and identification (Westwick & Kearney, 2003) of physiological systems. Unfortunately physiological systems are intrinsically time variant and highly non linear. Moreover, an effective balance of the model complexity is a difficult task: low order models are usually too simple to be useful, on the other hand high order models are too complex for simulation purposes and they have too many unknown parameters to be identified. Each model selected for investigation was studied by a group of biomedical students supervised by physicians. Each model required several iterations and reformulations, due to the continuous adjustment of the research objectives, changing their final horizon, because of the gap between experimental data and theoretical models, so that the answers to the doubts and questions were continuously moving with the obtained partial results. A final goal of the research was to apply a mathematical framework for helping medical diagnostic techniques and for testing new therapeutic protocols. The procedure of model extraction followed two main pathways: the first one (pathway A) led to a formulation of a mathematical model usually based on differential equations and on an as deep as possible insight into physiological mechanisms (Marmarelis, 2004; Ottesen et al., 2004; Edelstein-Keshet, 2005; Jones et al., 2009) via a physical description of the system. The second one (pathway B) was founded on a model description based on a black-box and data-driven identification (Westwick & Kearney, 2003; Cobelli & Carson, 2008), usually leaving to stochastic models with a parametric or non-parametric structure (Ljung, 1987), depending on the a-priori knowledge of constitutive laws governing the observed system. In this paper we will describe two examples of research activity based on the Physiological Cybernetics, both of them addressed to produce a biomedical framework for predicting the effects of therapeutic actions, but following the two different pathways. The first example follows a statistical non parametric approach, the second one a mathematical model based on differential equations. |

Digital Object Identifier (DOI): | 10.5772/2629 |

Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |