Simulating rare structural rearrangements of macromolecules with classical computational methods, such as Molecular Dynamics (MD), is an outstanding challenge. A multitude of technological advancements, from development of petaFLOPS supercomputers to advent of various enhance sampling methods, has granted access to time intervals of microseconds and even milliseconds in recent years. Yet, many key events occur on exponentially longer timescales. Here, path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore significantly different conformational regions remains a problem. To address this issue, we devised a hybrid path-sampling scheme, graph-Transition Path Sampling (gTPS), that generates the trial transition pathways using a quantum annealer. We first employ a classical computer to perform an uncharted exploration of the conformational space using a data-driven MD method. The dataset is then post-processed using a path-integral-based method to obtain a coarse-grained network representation of reactive pathways. By resorting to quantum annealing, the entire ensemble of these pathways can be encoded into a superposition in the initial quantum state of the annealer. Finally, by performing the quantum adiabatic transition on the state of the annealer, one can potentially generate/sample uncorrelated paths while they retain a high statistical probability (follow low free energy regions). We have first validated this scheme on a prototypically simple transition (α_R↔C_5 of alanine dipeptide) which could be extensively characterized on a desktop computer. Subsequently, we scaled up in complexity by generating a protein conformational transition (Bovine Pancreatic Trypsin Inhibitor - BPTI) that occurs on the millisecond timescale, obtaining results that match those of the Anton special-purpose supercomputer. Finally, we dicuss our current investigations on the application of gTPS to the unfolding process of headpiece subdomain of Villin and BPTI. Despite limitations due to the available quantum hardware, our study highlights how realistic biomolecular simulations provide a potentially impactful new ground for applying, testing, and advancing quantum technologies.

In the last decade nanotechnologies have greatly developed in many research ï¬ elds such as engineering, electronic, biological and many others. They can offer several possibilities to design tools, to create new techniques or improve the already existing ones, to discover innovative applications. And nanotechonology research is just at the beginning. One of the most interesting thing of this topic is the size of nanostructures. These materials are thousand times smaller than a cell and have a compatible size with proteins, enzymes and a lot of biological molecules. For this reason many research groups specialized in biotechnology started to invest people and resources in this new scientiï¬ c possibility. Following this very promising trend, BIOtech, a research group for biotechnology at the University of Trento, has proposed the Nanosmart project. Developed together with many prestigious institutes all over the world, this project aims to exploit the nanotechnology possibilities in biological research. The purpose of this challenge is the design, development and production of magnetic nanoparticles to use them in diagnostics and therapy of cancer disease. Magnetic nanoparticles (MNP) are spherical agglomerates of iron oxide, few tens of nanometers, which can be exploited in many ways. Being magnetic they can be used as contrast agents in magnetic resonance imaging MRI. Together having a high absorbing coefficient in the radio frequency band, they can locally increase the temperature of the tissues hosts and this being used for hyperthermia treatments. Entrapping some drugs in one of their multilayers, MNP can be used as inert carriers for drug delivery: due to their small size they can enter biological tissues, cross the plasma membrane of cells and release the drug only on predetermined targets. My Ph.D. started together with the project; so I had the possibility to follow this research from the beginning. In this years many problems have been handled, many errors have been made, many brilliant ideas have been shelved but also new abilities have been acquired, important collaborations were born and alternative structures have been thought and, fortunately, realized. Trying to eliminate unnecessary things and focusing on main purpose of this work, in this thesis I want to illustrate just the long â€œï¬ l rouge†that connects the idea of producing a nanoparticle that can cure tumor to the point of verify its effectiveness.

Insect brain is a very complex but at the same time small, simplified and accessible model with respect to the mammalian one. In neuroscience a huge number of works adopt drosophila as animal model, given its easiness of maintenance and, overall, of genetical manipulation. With such a model one can investigate many behavioral tasks and at the same time have access to a whole brain in vivo, with improved specificity and cellular resolution capabilities. Still, a remarkable goal would be to gain a precise control over the neural network, in order to fully manipulate specific areas of the brain, acting directly on network nodes of interest. This is possible thanks to optogenetics, a technique that exploits photosensitive molecules to modulate molecular events in living cells and neurons. At the same time, it is possible to perform a neuronal readout with light, exploiting calcium-based reporters; in this way, neuronal response investigation can gain in temporal and spatial resolution. This is an all-optical approach that brings many advantages in the neural network study and an insight in the functional connectivity of the system under investigation. We present here a setup that combines a two-photon imaging microscope, capable of in vivo imaging with a sub-cellular resolution and an excellent penetration depth down to hundreds of microns, with a diode laser optogenetic stimulation. With such a setup we investigate the drosophila brain in vivo, stimulating single units of the primary olfactory system (the so-called glomeruli, about 20 μm of diameter). By our knowledge this is one of the first time a similar all-optical approach is used in such an animal model: we confirm, in this way, the possibility to perform these experiments in vivo, with all the advantages coming from the improved accessibility of our model. Moreover, we present the results using a sample co-expressing GCaMP6 and ChR2-XXL, optimal performing sensor and actuator, largely exploited in the field for their high efficiency: these were rarely used in combination, since their spectral overlap, nevertheless we are able to show the feasibility of this combined approach, enabling to take advantage from the use of both these performing molecules. Finally, we will show different approaches of data analysis to infer relevant information about correlation and time response of different areas of the brain, that can give us hints in favor of some functional connectivity between olfactory subunits.

This thesis investigates two biological systems using atomistic modelling and molecular dynamics simulation. The work is focused on: (a) the study of the interaction between a segment of a DNA molecule and a functionalized surface; (b) the dynamical modelling of protein tau, an intrinsically disordered protein. We briefly describe here the two problems; for their detailed introduction we refer respectively to chapter DNA and chapter TAU. The interest in the study of the adsorption of DNA on functionalized surfaces is related to the considerable effort that in recent years has been devoted in developing technologies for faster and cheaper genome sequencing. In order to sequence a DNA molecule, it has to be extracted from the cell where it is stored (e.g. the blood cells). As a consequence any genomic analysis requires a purification process in order to remove from the DNA molecule proteins, lipids and any other contaminants. The extraction and purification of DNA from biological samples is hence the first step towards an efficient and cheap genome sequencing. Using the chemical and physical properties of DNA it is possible to generate an attractive interaction between this macromolecule and a properly treated surface. Once positioned on the surface, the DNA can be more easily purified. In this work we set up a detailed molecular model of DNA interacting with a surface functionalized with amino silanes. The intent is to investigate the free energy of adsorption of small DNA oligomers as a function of the pH and ionic strength of the solution. The tau protein belongs to the category of Intrinsically Disordered Proteins (IDP), which in their native state do not have an average stable structure and fluctuate between many conformations. In its physiological state, tau protein helps nucleating and stabilizing the microtubules in the axons of the neurons. On the other hand, the same tau - in a pathological aggregation - is involved in the development of the Alzheimer disease. IDPs do not have a definite 3D structure, therefore their dynamical simulation cannot start from a known list of atomistic positions, like a protein data bank file. We first introduce a procedure to find an initial dynamical state for a generic IDP, and we apply it to the tau protein. We then analyze the dynamical properties of tau, like the propensity of residues to form temporary secondary structures like beta-sheets or alpha-helices.

In this work we apply simulation techniques, namely Monte Carlo simulations and a path integral based method called Dominant Reaction Pathways (DRP) approach, in order to study aspects of dynamics and thermodynamics in three different families of peculiar proteins. These proteins are, for reasons such as the presence of an intermediate state in the folding path or topological constraints or large size, different from ideal systems, as may be considered small globular proteins that fold in a two state manner. The first treated topic is represented by the colicin immunity proteins IM9 and IM7, very similar in structure but with an apparently different folding mechanism. Our simulations suggest that the two proteins should fold with a similar folding mechanism via a populated on-pathway intermediate state. Then, two classes of pheromones that live in temperate and arctic water respectively are investigated. The two types of pheromones, despite the high structural similarity, show a different thermodynamic behavior, that could be explained, according to our results, by considering the role played by the location of CYS-CYS bonds along the chain. Finally, the conformational changes occurring in serpin proteins are studied. The serpins are very flexible, with a large size, more than 350 residues, and slow dynamics, from hours to weeks, completely beyond the possibilities of the simulation techniques to date. In this thesis we present the first all-atom simulations, obtained with the DRP approach, of the mechanism related to serpins and a complete characterization of the serpin dynamics is performed. Moreover, important implications for what concerns medical research field, in particular in drug design, are drown from this detailed analysis.

The identification of the networks connecting different brain areas, as well as the understanding of their role in executing complex behavioral tasks, are crucial issues in cognitive neurosciences. In this context, several time series analysis approaches are available for the investigation of brain connectivity from non-invasive electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. Among them, multivariate autoregressive (MVAR) models, studied in the frequency domain, allow quantitative assessment of connectivity separately for each specific brain rhythm. In spite of its widespread utilization and great potential, MVAR-based brain connectivity analysis is complicated by a number of theoretical and practical aspects. An important issue is that the MVAR model, commonly applied to neurophysiological time series, accounts only for lagged effects among the series, forsaking instantaneous (i.e., not lagged) effects. Despite this, instantaneous correlations among EEG/MEG signals are largely expected, mainly as a consequence of volume conduction, and the impact of their exclusion on frequency-domain connectivity measures has not been investigated yet. The aim of the present thesis was to introduce and validate a new methodological framework for the frequency-domain evaluation of brain connectivity during visuo-motor integration processes. To this end, we provided first a comprehensive description of the most common MVAR-based connectivity measures, enhancing their theoretical interpretation. Then, we introduced an extended MVAR (eMVAR) model representation explicitly accounting for instantaneous effects. Accordingly, new frequency-domain connectivity measures were defined, and procedures for improving model identification and significance assessment were given. The proposed approach was validated on theoretical illustrative examples, and then applied to EEG and MEG multichannel data recorded from subjects performing a visuo-motor task combining precise grip motor commands with sensory visual feedback. The theoretical validation showed that, in the presence of significant instantaneous correlations, the traditional MVAR formulation may yield misleading connectivity patterns, while the correct patterns can be detected from the new measures based on eMVAR model identification. The practical application showed that instantaneous correlations are non negligible in the considered neurophysiological recordings, strongly suggesting the necessity of using the proposed eMVAR model in place of the traditional one. Results showed that execution of the visuo-motor task evokes the activation of a specific network subserving sensorimotor integration, which involves occipito-parietal and precentral cortices. The new connectivity measures revealed connections which were peculiar of different brain rhythms. Specifically, in the alpha frequency band (8-13 Hz) we documented an enhanced driving role of the visual cortex on the left motor cortex, suggesting a relation between this rhythm and the lateralization of the visuo-motor task. In the beta band (13-30 Hz), task-induced connectivity changes were bilateral, suggesting an involvement of both hemispheres. In both alpha and beta bands, the new connectivity measures suggested an important role for the parietal cortex in mediating the information flow from visual to motor areas, confirming previous evidences from invasive studies based on intra-cranical recordings, TMS or PET examinations. This thesis was produced in collaboration with the Department of Physics of the University of Trento.

In this thesis an innovative approach to assess connectivity in a complex network was proposed. In network connectivity studies, a major problem is to estimate the links between the elements of a system in a robust and reliable way. To address this issue, a statistical method based on Pearson’s correlation coefficient was proposed. The former inherits the versatility of the latter, declined in a general applicability to any kind of system and the capability to evaluate cross–correlation of time series pairs both simultaneously and at different time lags. In addition, our method has an increased “investigation power”, allowing to estimate correlation at different time scale–resolutions. The method was tested on two very different kind of systems: the brain and a set of meteorological stations in the Trentino region. In both cases, the purpose was to reconstruct the existence of significant links between the elements of the two systems at different temporal resolutions. In the first case, the signals used to reconstruct the networks are magnetoencephalographic (MEG) recordings acquired from human subjects in resting–state. Zero–delays cross–correlations were estimated on a set of MEG time series corresponding to the regions belonging to the default mode network (DMN) to identify the structure of the fully–connected brain networks at different time scale resolutions. A great attention was devoted to test the correlation significance, estimated by means of surrogates of the original signal. The network structure is defined by means of the selection of four parameter values: the level of significance α, the efficiency η0, and two ranking parameters, R1 and R2, used to merge the results obtained from the whole dataset in a single average behav- ior. In the case of MEG signals, the functional fully–connected networks estimated at different time scale resolutions were compared to identify the best observation window at which the network dynamics can be highlighted. The resulting best time scale of observation was ∼ 30 s, in line with the results present in the scientific liter- ature. The same method was also applied to meteorological time series to possibly assess wind circulation networks in the Trentino region. Although this study is pre- liminary, the first results identify an interesting clusterization of the meteorological stations used in the analysis.

Functional connectivity represents a powerful approach to describe the intrinsic activity of the brain. It reveals the organization and correlations among anatomically separated regions supporting similar cognitive and sensory processes. Using functional Magnetic Resonance Imaging (fMRI), the recurrent spatial characteristics of these patterns have been extensively explored in the adult brain and their disruption has been found to be associated with psychiatric and developmental disorders. Unveiling the processes of emergence of resting state networks at a very early stage of life could shed light on the neuronal origins of these diseases. However, the study of the inception and development of functional connectivity in the newborn brain poses exceptional challenges, due to the complexity of dealing with non-compliant subjects. To this end, cortical activity at birth can be investigated using functional Near Infrared Spectroscopy (fNIRS) that represents a promising non-invasive neuroimaging method for developmental studies. In the present thesis, I applied fNIRS to assess functional connectivity in term neonates. The first part of the dissertation is dedicated to investigating the maturation of a specific resting state network, the Default Mode Network, within the first 48 hours of life. The study aimed to examine its emergence, for the first time, using optical imaging on newborns immediately after birth. While the majority of fMRI literature focused on large-scale spatial patterns, I took a different approach measuring an intrinsic and localized fingerprint feature of the network, consistently detected in adult subjects. In the second part of the dissertation, I aimed at improving the anatomical representation of brain connectivity, inferred only from signals collected at the scalp. Thus, I developed and validated a method for the reconstruction of spatially distributed functional signals on a dedicated template for term newborn subjects. The intent is to promote the shift from a sensor space description (one signal for each channel) to a source space representation in which the origin of the signal is reconstructed with better anatomical fidelity. The reliability of the reconstruction method was tested on synthetic and real data. In the former case, I simulated spatially correlated neural activity in the cortex, thus enabling assessment of the reconstructed images against a ground-truth map. Analyses of functional connectivity in both sensor and source space showed that the Default Mode Network is still immature at birth, with a lack of homotopic correlation in the lateral parietal cortices, and no evidence of anticorrelation with the Dorsal Attention Network, a well established feature in the adult brain. Overall the work presented in the thesis contributes to the understanding of functional connectivity in the infantâ€TMs brain and provides useful tools for source-based connectivity analysis and for probe design and optimization.

Gamma-ray and Fluorescence Lifetime Spectroscopies are driving the development of non-imaging silicon photon sensors and, in this context, Silicon Photo-Multipliers (SiPM)s are leading the starring role. They are 2D array of optical diodes called Single Photon Avalanche Diodes (SPAD)s, and are normally fabricated with a dedicated silicon process. SPADs amplify the charge produced by the single absorbed photon in a way that recalls the avalanche amplification exploited in Photo-Multiplier Tubes (PMT)s. Recently 2D arrays of SPADs have been realized also in standard CMOS technology, paving the way to the realization of completely custom sensors that can host ancillary electronic and digital logic on-chip. The designs of scientific apparatus have been influenced for years by the bulky PMT-based detectors. An overwhelming interest in both SiPMs and CMOS SPADs lies in the possibility of displacing these small sensors realizing new detectors geometries. This thesis examines the potential deployment of SiPM-based detector in an apparatus built for the study of the Time-Of-Flight (TOF) of Positronium (Ps) and the displacement of 2D array of CMOS SPADs in a lab-on-chip apparatus for Fluorescence Lifetime Spectroscopy. The two design procedures are performed using Monte-Carlo simulations. Characterizations of the two sensor have been carried out, allowing for a performance evaluation and a validation of the two design procedures.

Intracellular chloride and pH are fundamental regulators of neuronal excitability and they are often co-modulated during excitation-inhibition activity. The study of their homeostasis requires simultaneous measurements in vivo in multiple neurons. Combining random mutagenesis screening, protein engineering and two-photon-imaging this thesis work led to the discovery of new chloride-sensitive GFP mutants and to the establishment of ratiometric imaging procedures for the quantitative combined imaging of intraneuronal pH and chloride. These achievements have been demonstrated in vivo in the mouse cortex, in real-time monitoring the dynamic changes of ions concentrations during epileptic-like discharges, and in glioblastoma primary cells, measuring osmotic swelling responses to various drugs treatment.