D'Onofrio, Giuseppe (2016) Stochastic methods and models for neuronal activity and motor proteins. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Stochastic methods and models for neuronal activity and motor proteins
Creators:
CreatorsEmail
D'Onofrio, Giuseppegiuseppe.donofrio@unina.it
Date: 30 March 2016
Number of Pages: 103
Institution: Università degli Studi di Napoli Federico II
Department: Matematica e Applicazioni "Renato Caccioppoli"
Scuola di dottorato: Scienze matematiche ed informatiche
Dottorato: Scienze computazionali e informatiche
Ciclo di dottorato: 28
Coordinatore del Corso di dottorato:
nomeemail
Moscariello, Giocondagioconda.moscariello@unina.it
Tutor:
nomeemail
Pirozzi, EnricaUNSPECIFIED
Date: 30 March 2016
Number of Pages: 103
Uncontrolled Keywords: Gauss-Markov processes, first passage time, acto-myosin dynamics
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > MAT/06 - Probabilità e statistica matematica
Additional Information: Il file è in formato PDF/A
Date Deposited: 12 Apr 2016 14:31
Last Modified: 02 Nov 2016 11:47
URI: http://www.fedoa.unina.it/id/eprint/10846

Abstract

The main topic of this thesis is to specialize mathematical methods and construct stochastic models for describing and predict biological dynamics such as neuronal firing and acto-myosin sliding. The apparently twofold theme on which we focused the research is part of a strictly unitary context as the methods and tools of investigation adapt naturally to both issues. Models describing the stochastic evolution in the case of neuronal activity or that of the acto-myosin dynamics are governed by stochastic differential equations whose solutions are diffusion processes and Gauss-Markov processes. Of fundamental importance in the study of these phenomena is the determination of the density of the first passage times through one or two time-depending boundaries. For this purpose the development or use of numerical solution algorithms and the comparison of the results with those obtained by simulation algorithms is essential. A particular type of Gauss-Markov process (the time-inhomogeneous Ornstein-Uhlenbeck process) and its first passage time through suitable boundaries are fundamental for modeling phenomena subject to additional (external) time-dependent forces.

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