Mathematical modeling of physiological systems: An essential tool for discovery

Optimization is an essential tool in the field of decision support. In this chapter, the authors study an inverse problem applied in the telecommunication networks. Indeed, in the telecommunication networks, service providers have subscription offers to customers. Since competition is strong in this sector, most of these advertising offerings, totally or partially ambiguous, are prepared to attract the attention of consumers. For this reason, customers face problems in making decisions about the choice of the operators that gives them a better report price/QoS. Mathematical modeling of this decision support problem led to the resolution of an inverse problem. More precisely, the inverse problem is to find the function of the QoS real knowing the QoS theoretical or advertising. This model will help customers who seek to know the degree of sincerity of their operators, and it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers.

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Mathematical Modeling of Physiological Systems

Lecture Notes in Mathematics - Mathematical Modeling and Validation in Physiology ◽

10.1007/978-3-642-32882-4_2 ◽

2012 ◽

pp. 21-41 ◽

Cited By ~ 3

Author(s):

Thomas Heldt ◽

George C. Verghese ◽

Roger G. Mark

Keyword(s):

Mathematical Modeling ◽

Physiological Systems

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Neuromuscular Modulation in Aplysia. I. Dynamic Model

Journal of Neurophysiology ◽

10.1152/jn.01091.2002 ◽

2003 ◽

Vol 90 (4) ◽

pp. 2592-2612 ◽

Cited By ~ 18

Author(s):

Vladimir Brezina ◽

Irina V. Orekhova ◽

Klaudiusz R. Weiss

Keyword(s):

Mathematical Modeling ◽

Steady State ◽

Motor Neuron ◽

Dynamic Model ◽

Motor Neurons ◽

Neuromuscular System ◽

Feeding Behaviors ◽

Cellular Effects ◽

Dynamical Time ◽

Physiological Systems

Many physiological systems are regulated by complex networks of modulatory actions. Here we use mathematical modeling and complementary experiments to study the dynamic behavior of such a network in the accessory radula closer (ARC) neuromuscular system of Aplysia. The ARC muscle participates in several types of rhythmic consummatory feeding behavior. The muscle's motor neurons release acetylcholine to produce basal contractions, but also modulatory peptide cotransmitters that, through multiple cellular effects, shape the contractions to meet behavioral demands. We construct a dynamic model of the modulatory network and examine its operation as the motor neurons fire in realistic patterns that change gradually over an hour-long meal and abruptly with switches between the different feeding behaviors. The modulatory effects have very disparate dynamical time scales. Some react to the motor neuron firing only over many cycles of the behavior, but one key effect is fast enough to respond to each individual cycle. Switches between the behaviors are therefore followed by rapid relaxations along some modulatory dimensions but not others. The trajectory of the modulatory state is a transient throughout the meal, ranging widely over regions of the modulatory space not accessible in the steady state. There is a pronounced history-dependency: the modulatory state associated with a cycle of a particular behavior depends on when that cycle occurs and what behaviors preceded it. On average, nevertheless, each behavior is associated with a different modulatory state. In the following companion study, we add a model of the neuromuscular transform to reconstruct and evaluate the actual modulated contraction shapes.

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Decision Choice Optimization With Genetic Algorithm in Communication Networks

Advances in Wireless Technologies and Telecommunication - Innovative Perspectives on Interactive Communication Systems and Technologies ◽

10.4018/978-1-7998-3355-0.ch009 ◽

2020 ◽

pp. 194-209

Author(s):

Driss Ait Omar ◽

Mohamed El Amrani ◽

Hamid Garmani ◽

Mohamed Baslam ◽

Mohamed Fakir

Keyword(s):

Mathematical Modeling ◽

Genetic Algorithm ◽

Inverse Problem ◽

Decision Support ◽

Communication Networks ◽

Service Providers ◽

Telecommunication Networks ◽

Essential Tool ◽

Decision Choice

Optimization is an essential tool in the field of decision support. In this chapter, the authors study an inverse problem applied in the telecommunication networks. Indeed, in the telecommunication networks, service providers have subscription offers to customers. Since competition is strong in this sector, most of these advertising offerings, totally or partially ambiguous, are prepared to attract the attention of consumers. For this reason, customers face problems in making decisions about the choice of the operators that gives them a better report price/QoS. Mathematical modeling of this decision support problem led to the resolution of an inverse problem. More precisely, the inverse problem is to find the function of the QoS real knowing the QoS theoretical or advertising. This model will help customers who seek to know the degree of sincerity of their operators, and it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers.

Download Full-text

Mathematical Modeling of Physiological Systems

Handbook of Biomedical Engineering ◽

10.1016/b978-0-12-415145-1.50029-3 ◽

1988 ◽

pp. 609-618 ◽

Cited By ~ 1

Author(s):

MICHAEL CHILBERT

Keyword(s):

Mathematical Modeling ◽

Physiological Systems

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Improving Neural Simulations with the EMI Model

Modeling Excitable Tissue - Simula SpringerBriefs on Computing ◽

10.1007/978-3-030-61157-6_7 ◽

2020 ◽

pp. 87-98

Author(s):

Alessio Paolo Buccino ◽

Miroslav Kuchta ◽

Jakob Schreiner ◽

Kent-André Mardal

Keyword(s):

Mathematical Modeling ◽

Neuronal Activity ◽

Computationally Efficient ◽

Neuronal Dynamics ◽

Modeling Framework ◽

Essential Tool ◽

Experimental Approaches ◽

Extracellular Potentials ◽

Ephaptic Effect

Abstract Mathematical modeling of neurons is an essential tool to investigate neuronal activity alongside with experimental approaches. However, the conventional modeling framework to simulate neuronal dynamics and extracellular potentials makes several assumptions that might need to be revisited for some applications. In this chapter we apply the EMI model to investigate the ephaptic effect and the effect of the extracellular probes on the measured potential. Finally, we introduce reduced EMI models, which provide a more computationally efficient framework for simulating neurons with complex morphologies.

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