Analysis of ion channel stochastic signals for biosensing

In biological world, life of cells is guaranteed by their ability to sense and to respond to a large variety of internal and external stimuli. In particular, excitable cells, like muscle or nerve cells, produce quick depolarizations in response to electrical, mechanical or chemical stimuli: this means that they can change their internal potential through a quick exchange of ions between cytoplasm and the external environment. This can be done thanks to the presence of ion channels, proteins that span the lipid bilayer and act like switches, allowing ionic current to flow opening and shutting in a stochastic way. For a particular class of ion channels, ligand-gated ion channels, the gating processes is strongly influenced by binding between receptive sites located on the channel surface and specific target molecules. These channels, inserted in biomimetic membranes and in presence of a proper electronic system for acquiring and elaborating the electrical signal, could give us the possibility of detecting and quantifying concentrations of specific molecules in complex mixtures from ionic currents across the membrane; in this thesis work, this possibility is investigated. In particular, it reports a description of experiments focused on the creation and the characterization of artificial lipid membranes, the reconstitution of ion channels and the analysis of their electrical and statistical properties. Moreover, after a chapter about the basis of the modelling of the kinetic behaviour of ligand gated ion channels, a possible approach for the estimation of the target molecule concentration, based on a statistical analysis of the ion channel open probability, is proposed. The fifth chapter contains a description of the kinetic characterisation of a ligand gated ion channel: the homomeric α2 isoform of the glycine receptor. It involved both experimental acquisitions and signal analysis. The last chapter represents the conclusions of this thesis, with some remark on the effective performance that may be achieved using ligand gated ion channels as sensing elements.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

In situ real-time investigation of Organic Ultra-Thin-Film transistors: growth, electrical properties and biosensing applications

Organic electronics has grown enormously during the last decades driven by the encouraging results and the potentiality of these materials for allowing innovative applications, such as flexible-large-area displays, low-cost printable circuits, plastic solar cells and lab-on-a-chip devices. Moreover, their possible field of applications reaches from medicine, biotechnology, process control and environmental monitoring to defense and security requirements. However, a large number of questions regarding the mechanism of device operation remain unanswered. Along the most significant is the charge carrier transport in organic semiconductors, which is not yet well understood. Other example is the correlation between the morphology and the electrical response. Even if it is recognized that growth mode plays a crucial role into the performance of devices, it has not been exhaustively investigated. The main goal of this thesis was the finding of a correlation between growth modes, electrical properties and morphology in organic thin-film transistors (OTFTs). In order to study the thickness dependence of electrical performance in organic ultra-thin-film transistors, we have designed and developed a home-built experimental setup for performing real-time electrical monitoring and post-growth in situ electrical characterization techniques. We have grown pentacene TFTs under high vacuum conditions, varying systematically the deposition rate at a fixed room temperature. The drain source current IDS and the gate source current IGS were monitored in real-time; while a complete post-growth in situ electrical characterization was carried out. At the end, an ex situ morphological investigation was performed by using the atomic force microscope (AFM). In this work, we present the correlation for pentacene TFTs between growth conditions, Debye length and morphology (through the correlation length parameter). We have demonstrated that there is a layered charge carriers distribution, which is strongly dependent of the growth mode (i.e. rate deposition for a fixed temperature), leading to a variation of the conduction channel from 2 to 7 monolayers (MLs). We conciliate earlier reported results that were apparently contradictory. Our results made evident the necessity of reconsidering the concept of Debye length in a layered low-dimensional device. Additionally, we introduce by the first time a breakthrough technique. This technique makes evident the percolation of the first MLs on pentacene TFTs by monitoring the IGS in real-time, correlating morphological phenomena with the device electrical response. The present thesis is organized in the following five chapters. Chapter 1 makes an introduction to the organic electronics, illustrating the operation principle of TFTs. Chapter 2 presents the organic growth from theoretical and experimental points of view. The second part of this chapter presents the electrical characterization of OTFTs and the typical performance of pentacene devices is shown. In addition, we introduce a correcting technique for the reconstruction of measurements hampered by leakage current. In chapter 3, we describe in details the design and operation of our innovative home-built experimental setup for performing real-time and in situ electrical measurements. Some preliminary results and the breakthrough technique for correlating morphological and electrical changes are presented. Chapter 4 meets the most important results obtained in real-time and in situ conditions, which correlate growth conditions, electrical properties and morphology of pentacene TFTs. In chapter 5 we describe applicative experiments where the electrical performance of pentacene TFTs has been investigated in ambient conditions, in contact to water or aqueous solutions and, finally, in the detection of DNA concentration as label-free sensor, within the biosensing framework.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.

Stochastic models and dynamic measures for the characterization of bistable circuits

During the last few years, a great deal of interest has risen concerning the applications of stochastic methods to several biochemical and biological phenomena. Phenomena like gene expression, cellular memory, bet-hedging strategy in bacterial growth and many others, cannot be described by continuous stochastic models due to their intrinsic discreteness and randomness. In this thesis I have used the Chemical Master Equation (CME) technique to modelize some feedback cycles and analyzing their properties, including experimental data. In the first part of this work, the effect of stochastic stability is discussed on a toy model of the genetic switch that triggers the cellular division, which malfunctioning is known to be one of the hallmarks of cancer. The second system I have worked on is the so-called futile cycle, a closed cycle of two enzymatic reactions that adds and removes a chemical compound, called phosphate group, to a specific substrate. I have thus investigated how adding noise to the enzyme (that is usually in the order of few hundred molecules) modifies the probability of observing a specific number of phosphorylated substrate molecules, and confirmed theoretical predictions with numerical simulations. In the third part the results of the study of a chain of multiple phosphorylation-dephosphorylation cycles will be presented. We will discuss an approximation method for the exact solution in the bidimensional case and the relationship that this method has with the thermodynamic properties of the system, which is an open system far from equilibrium.In the last section the agreement between the theoretical prediction of the total protein quantity in a mouse cells population and the observed quantity will be shown, measured via fluorescence microscopy.

Model reduction of stochastic groundwater flow and transport processes

This work presents a comprehensive methodology for the reduction of analytical or numerical stochastic models characterized by uncertain input parameters or boundary conditions. The technique, based on the Polynomial Chaos Expansion (PCE) theory, represents a versatile solution to solve direct or inverse problems related to propagation of uncertainty. The potentiality of the methodology is assessed investigating different applicative contexts related to groundwater flow and transport scenarios, such as global sensitivity analysis, risk analysis and model calibration. This is achieved by implementing a numerical code, developed in the MATLAB environment, presented here in its main features and tested with literature examples. The procedure has been conceived under flexibility and efficiency criteria in order to ensure its adaptability to different fields of engineering; it has been applied to different case studies related to flow and transport in porous media. Each application is associated with innovative elements such as (i) new analytical formulations describing motion and displacement of non-Newtonian fluids in porous media, (ii) application of global sensitivity analysis to a high-complexity numerical model inspired by a real case of risk of radionuclide migration in the subsurface environment, and (iii) development of a novel sensitivity-based strategy for parameter calibration and experiment design in laboratory scale tracer transport.

Permutation-based stochastic ordering using pairwise comparisons

The topic of this work concerns nonparametric permutation-based methods aiming to find a ranking (stochastic ordering) of a given set of groups (populations), gathering together information from multiple variables under more than one experimental designs. The problem of ranking populations arises in several fields of science from the need of comparing G>2 given groups or treatments when the main goal is to find an order while taking into account several aspects. As it can be imagined, this problem is not only of theoretical interest but it also has a recognised relevance in several fields, such as industrial experiments or behavioural sciences, and this is reflected by the vast literature on the topic, although sometimes the problem is associated with different keywords such as: "stochastic ordering", "ranking", "construction of composite indices" etc., or even "ranking probabilities" outside of the strictly-speaking statistical literature. The properties of the proposed method are empirically evaluated by means of an extensive simulation study, where several aspects of interest are let to vary within a reasonable practical range. These aspects comprise: sample size, number of variables, number of groups, and distribution of noise/error. The flexibility of the approach lies mainly in the several available choices for the test-statistic and in the different types of experimental design that can be analysed. This render the method able to be tailored to the specific problem and the to nature of the data at hand. To perform the analyses an R package called SOUP (Stochastic Ordering Using Permutations) has been written and it is available on CRAN.

Effects of stochastic hypoxia events on the benthic communities of transitional waters

Hypoxia is one of the most important and faster spreading threats to marine life and its occurrence has significantly increased in the last century. The effects of hypoxia on marine organisms and communities has mostly been studied in light of the intensity of the disturbance but not a lot of attention has been given to its interaction with other stressors and the timing of its appearance. In this thesis I started to explore these topics through laboratory and manipulative field experiments. I studied the interactive effects of thermal stress and hypoxia on a European native bivalve species (Cerastoderma edule; Linnaeus, 1758 ) and a non native one (Ruditapes philippinarum; Adams & Reeve, 1850) through a laboratory experiment performed in the Netherlands. The non native species displayed a greater tolerance to oxygen depletion than the native one. The first field experiment was performed in an Italian brackish coastal lagoon (Pialassa Baiona) and tested the effects of different timing regimes of hypoxia on the benthic community. It emerged that the main factor affecting the community is the duration of the hypoxia. The ability of the communities to recover after repeated hypoxic periods was explored in the second manipulative field experiment. We imposed three different timing regimes of hypoxia on sediment patches in Pialassa Baiona and we monitored the changes of both the benthic and the microbial communities after the disturbances. The preliminary analyses of the data from this last work suggest that the experimental manipulations caused limited detrimental effects on the communities. Overall this thesis work suggests that the duration of hypoxic events, their repetitive nature and the associated thermal stress are key factors in determining their effects on the communities and that management measures should point towards a reduction of the duration of the single hypoxic periods more than their frequency.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

Drive up electrochemical and electrochemiluminescence signal through biosensing mechanism optimization

Biomarkers are biological indicators of human health conditions. Their ultra-sensitive quantification is of cardinal importance in clinical monitoring and early disease diagnosis. Biosensors are some worldwide simple and easy-to-use analytical devices as a matter of fact, biosensors using electrochemiluminescence (ECL) are one of the most promising biosensors that needs an ever-increasing sensitivity for improving its clinical effectiveness. The principal aspiration of this project is the investigation of the ECL generation mechanisms for enhancing the ECL intensity and the development of an ultrasensitive sensor, the use of metal-oxide materials (Mox) and the substitution of metal-free dyes. Novel dyes such as BODIPY, TADF are used to improve the sensitivity of ECL techniques thanks to their advantageous and tunable properties, enhancing the signal and also the ECL efficiency. Additionally, the use of Mox could be beneficial for the investigation of two different ECL mechanisms, which occur simultaneously. In this thesis, the investigation of size and distance effects on electrochemical (EC) mechanisms was carried out through the innovative combination of a standard detection system using different size of micromagnetic beads (MBs). That allowed the discovery of an unexpected and highly efficient mechanistic path for electrochemical generation at small distances from the electrode’s surface. The smallest MBs (0.1μm) demostrate an enhancement of electrochemical signal than the bigger one (2.8μm) until 4 times of magnitude. Finally, a novel ultrasensitive sensor, based on the coreactant-luminophores mechanism, was developed for the determination of whole viral genome specific for cardiac HBV and COVID-19 virus. In conclusion, the ECL and the use of EC techniques (such as amperometry), improved the understanding of mechanisms responsible for the ECL/EC signal led to a great enhancement in the signal.