Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-29T13:02:55Z No. of bitstreams: 1 arquivo total.pdf: 683890 bytes, checksum: 0a793cef55b22424f093f0e99992e623 (MD5) / Made available in DSpace on 2016-03-29T13:02:55Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 683890 bytes, checksum: 0a793cef55b22424f093f0e99992e623 (MD5) Previous issue date: 2015-05-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The Dissertation study the relations between control theory, stochastic calculus and parabolic partial di erential equations. The aim is to study representations of viscosity solutions for parabolic equations via the Feynman-Kac nonlinear formulas. To this end, the control theory plays an important role in the connection between the stochastic and deterministic approaches. / A Disserta c~ao aborda algumas rela c~oes existentes entre teoria de controle, c alculo estoc astico e equa c~oes diferenciais parciais parab olicas. O interesse e estudar representa c~oes de solu c~oes de viscosidade para equa c~oes parab olicas via f ormulas de Feynman-Kac n~ao lineares. Para isso, o ferramental de teoria de controle tem papel importante na conex~ao entre a abordagem estoc astica e determin stica.

Chemical reactions involving small number of reacting molecules are noisy processes. They are simulated using stochastic simulation algorithms like the Gillespie SSA, which are valid when the reaction environment is well-mixed. This is not the case in reactions occuring on biological media like cell membranes, where alternative simulation methods have to be used to account for the crowded nature of the reacting environment. Ion channels, which are membrane proteins controlling the flow of ions into and out of the cell, offer excellent single molecule conditions to test stochastic simulation schemes in crowded biological media. Single molecule reactions are of great importance in determining the functions of biological molecules. Access to their experimental data have increased the scope of com-putational modeling of biological processes. Recently, single molecule experiments have revealed the non-Markovian nature of chemical reactions, due to a phenomenon called `dynamic disorder', which makes the rate constants a deterministic function of time or a random process. This happens when there are additional slow scale conformational transitions, giving the molecule a memory of its previous states. In a previous work, the hTREK1 two pore domain potassium channel was revealed to have long term memory in its kinetics, prompting alternate non-Markovian schemes to analyze its gating. Traditionally, ion channel gating is modeled as Markovian transitions between fixed states. In this work, we have used single channel data from hTREK1 ion channel and have provided a simple diffusion model for its gating. The main assumption of this model is that the ion channel diffuses through a continuum of states on its potential energy landscape, which is derived from the steady state probability distribution of ionic current recorded from patch clamp experiments. A stochastic differential equation (SDE) driven by Gaussian white noise is proposed to model this motion in an asymmetric double well potential. The method is computationally very simple and efficient and reproduces the amplitude histogram very well. For the case when ligands are added, leading to incorporation of long term memory in the kinetics, the SDE is modified to run on coloured noise. This has been done by introducing an auxiliary variable into the equation. It has been shown that increasing the noise correlation with ligand concentration improves the fits to the experimental data. This has been validated for several datasets. These methods are more advantageous for simulation than the Markovian models as they are true to the physical picture of gating and also computationally very efficient. Reproducing the whole raw data trace takes no more than a few seconds with our scheme, with the only input being the amplitude histogram and four parameters. Finally a quantitative model based on a modified version of the Chemical Langevin equation is given, which works on random rate parameters. This model is computationally simple to implement and reproduces the catalytic activity of the channel as a function of time. From the computational analysis undertaken in this work, we can infer that ion channel activity can be modeled using the framework of non-Markovian processes, lending credence to the recent understanding that single molecule reactions are basically processes with long-term memory. Since the ion channel is basically a protein, we can also hypothesize that the some of the properties that make proteins so vital to living organ-isms could be attributed to long-term memory in their folding kinetics, giving them the ability to sample specific regions of their conformation space, which are of interest to biological functions.

<p>Doktorska disertacija je posvećena raznim klasama uop&scaron;tenih stohastičkih procesa i njihovim primenama na re&scaron;avanje singularnih stohastičkih parcijalnih diferencijalnih jednačina. U osnovi, disertacija se može podeliti na dva dela. Prvi deo disertacije (Glava 2) je posvećen strukturnoj karakterizaciji uop&scaron;tenih stohastičkih procesa u vidu haos ekspanzije i integralne reprezentacije. Drugi deo disertacije (Glava 3) čini primena dobijenih rezultata na re&middot;savanje stohastičkog Dirihleovog problema u kojem se množenje modelira Vikovim proizvodom, a koefcijenti eliptičnog diferencijalnog operatora su Kolomboovi uop&scaron;teni stohastički procesi.</p> / <p>Subject of the dissertation are various classes of generalized<br />stochastic processes and their applications to solving singular stochastic<br />partial di&reg;erential equations. Basically, the dissertation can be divided into<br />two parts. The &macr;rst part (Chapter 2) is devoted to structural characteri-<br />zations of generalized random processes in terms of chaos expansions and<br />integral representations. The second part of the dissertation (Chapter 3)<br />involves applications of the obtained results to solving a stochastic Dirichlet<br />problem, where multiplication is modeled by the Wick product, and the<br />coe&plusmn;cients of the elliptic di&reg;erential operator are Colombeau generalized<br />random processes.</p>