BioTuCSoN: biochemical extension of TuCSoN to support self-organising coordination

Starting from pervasive computing paradigm, we want to face the new system's requirements, concerning, mainly, self-organisation, situatedness and adaptivity, through the definition and execution of nature-inspired patterns. They are extracted by the study of dynamics in biological systems and we consider for their implementation the biochemical tuple spaces model. In particular, the aim of the thesis is to design and realize a first biochemical extension of TuCSoN (technology based on tuple spaces model) and, then, to verify its capabilities by means of a proper case study, that deals with local self-organisation and competition of services in an open and highly-dynamic environment.

Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.

Functional dna nanostructures for in vitro biosensing in live cells

DNA is a fascinating biomolecule that is well known for its genetic role in living systems. The emerging area of DNA nanotechnology provides an alternative view that exploits unparallel self-assembly ability of DNA molecules for material use of DNA. Although many reports exist on the results of DNA self-assembling systems, still few of them focus on the in vitro study about the function of such DNA nanostructures in live cells. Due to this, there are still a limited research about the in vitro functionality of such designs. To address an aspect of this issue, we have designed, synthesized and characterized two multifunctional fluorescencent nanobiosensors by DNA self-assembling. Each structure was designed and implemented to be introduced in live cells in order to give information on their functioning in real-time. Computational tools were used in order to design a graphic model of two new DNA motifs and also to obtain the specific sequences to all the ssDNA molecules. By thermal self-assembly techniques we have successfully synthesized the structure and corroborate their formation by the PAGE technique. In addition, we have established the conditions to characterize their structural conformation change when they perform their sensor response. The sensing behavior was also accomplished by fluorescence spectroscopy techniques; FRET evaluation and fluorescence microscopy imaging. Providing the evidence about their adequate sensing performance outside and inside the cells detected in real-time. In a preliminary evaluation we have tried to show the in vitro functionality of our structures in different cancer cell lines with the ability to perform local sensing responses. Our findings suggest that DNA sensor nanostructures could serve as a platform to exploit further therapeutic achievements in live cells.

Mathematical models for cellular aggregation: the chemotactic instability and clustering formation

In this thesis we present a mathematical formulation of the interaction between microorganisms such as bacteria or amoebae and chemicals, often produced by the organisms themselves. This interaction is called chemotaxis and leads to cellular aggregation. We derive some models to describe chemotaxis. The first is the pioneristic Keller-Segel parabolic-parabolic model and it is derived by two different frameworks: a macroscopic perspective and a microscopic perspective, in which we start with a stochastic differential equation and we perform a mean-field approximation. This parabolic model may be generalized by the introduction of a degenerate diffusion parameter, which depends on the density itself via a power law. Then we derive a model for chemotaxis based on Cattaneo's law of heat propagation with finite speed, which is a hyperbolic model. The last model proposed here is a hydrodynamic model, which takes into account the inertia of the system by a friction force. In the limit of strong friction, the model reduces to the parabolic model, whereas in the limit of weak friction, we recover a hyperbolic model. Finally, we analyze the instability condition, which is the condition that leads to aggregation, and we describe the different kinds of aggregates we may obtain: the parabolic models lead to clusters or peaks whereas the hyperbolic models lead to the formation of network patterns or filaments. Moreover, we discuss the analogy between bacterial colonies and self gravitating systems by comparing the chemotactic collapse and the gravitational collapse (Jeans instability).

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Tetrafenilciclopentadienon-derivati ed analoghi: sintesi, "self-assembly" e caratterizzazione reologica

Gels are materials that are easier to recognize than to define. For all practical purpose, a material is termed a gel if the whole volume of liquid is completely immobilized as usually tested by the ‘tube inversion’ method. Recently, supramolecular gels obtained from low molecular weight gelators (LMWGs) have attracted considerable attention in materials science since they represent a new class of smart materials sensitive to external stimuli, such as temperature, ultrasounds, light, chemical species and so on. Accordingly, during the past years a large variety of potentialities and applications of these soft materials in optoelectronics, as electronic devices, light harvesting systems and sensors, in bio-materials and in drug delivery have been reported. Spontaneous self-assembly of low molecular weight molecules is a powerful tool that allows complex supramolecular nanoscale structures to be built. The weak and non-covalent interactions such as hydrogen bonding, π–π stacking, coordination, electrostatic and van der Waals interactions are usually considered as the most important features for promoting sol-gel equilibria. However, the occurrence of gelation processes is ruled by further “external” factors, among which the temperature and the nature of the solvents that are employed are of crucial importance. For example, some gelators prefer aromatic or halogenated solvents and in some cases both the gelation temperature and the type of the solvent affect the morphologies of the final aggregation. Functionalized cyclopentadienones are fascinating systems largely employed as building blocks for the synthesis of polyphenylene derivatives. In addition, it is worth noting that structures containing π-extended conjugated chromophores with enhanced absorption properties are of current interest in the field of materials science since they can be used as “organic metals”, as semiconductors, and as emissive or absorbing layers for OLEDs or photovoltaics. The possibility to decorate the framework of such structures prompted us to study the synthesis of new hydroxy propargyl arylcyclopentadienone derivatives. Considering the ability of such systems to give π–π stacking interactions, the introduction on a polyaromatic structure of polar substituents able to generate hydrogen bonding could open the possibility to form gels, although any gelation properties has been never observed for these extensively studied systems. we have synthesized a new class of 3,4-bis (4-(3-hydroxy- propynyl) phenyl) -2, 5-diphenylcyclopentadienone derivatives, one of which (1a) proved to be, for the first time, a powerful organogelator. The experimental results indicated that the hydroxydimethylalkynyl substituents are fundamental to guarantee the gelation properties of the tetraarylcyclopentadienone unit. Combining the results of FT-IR, 1H NMR, UV-vis and fluorescence emission spectra, we believe that H-bonding and π–π interactions are the driving forces played for the gel formation. The importance of soft materials lies on their ability to respond to external stimuli, that can be also of chemical nature. In particular, high attention has been recently devoted to anion responsive properties of gels. Therefore the behaviour of organogels of 1a in toluene, ACN and MeNO2 towards the addition of 1 equivalent of various tetrabutylammonium salts were investigated. The rheological properties of gels in toluene, ACN and MeNO2 with and without the addition of Bu4N+X- salts were measured. In addition a qualitative analysis on cation recognition was performed. Finally the nature of the cyclic core of the gelator was changed in order to verify how the carbonyl group was essential to gel solvents. Until now, 4,5-diarylimidazoles have been synthesized.

Analytic models of self-gravitating rotating gaseous tori with central black hole

In this thesis project, I present stationary models of rotating fluids with toroidal distributions that can be used to represent the active galactic nuclei (AGN) central obscurers, i.e. molecular tori (Combes et al., 2019), as well as geometrically thick accretion discs, like ADAF discs (Narayan and Yi, 1995) or Polish doughnuts (Abramowicz, 2005). In particular, I study stationary rotating systems with a more general baroclinic distribution (with a vertical gradient of the angular velocity), which are often more realistic and less studied, due to their complexity, than the barotropic ones (with cylindrical rotation), which are easier to construct. In the thesis, I compute analytically the main intrinsic and projected properties of the power-law tori based on the potential-density pairs of Ciotti and Bertin (2005). I study the density distribution and the resulting gravitational potential for different values of α, in the range 2 < α < 5. For the same models, I compute the surface density of the systems when seen face-on and edge-on. I then apply the stationary Euler equations to obtain rotational velocity and temperature distributions of the self-gravitating models in the absence of an external gravitational potential. In the thesis I also consider the power-law tori with the presence of a central black hole in addition to the gas self-gravity, and solving analytically the stationary Euler equations, I compute how the properties of the system are modified by the black hole and how they vary as a function of the black hole mass. Finally, applying the Solberg-Høiland criterion, I show that these baroclinic stationary models are linearly stable in the absence of the black hole. In the presence of the black hole I derive the analytical condition for stability, which depends on α and on the black hole mass. I also study the stability of the tori in the hypothesis that they are weakly magnetized, finding that they are always unstable to this instability.

Self-supervised Information Retrieval basato su deep metric learning

La tesi ha lo scopo di ricercare, esaminare ed implementare un sistema di Machine Learning, un Recommendation Systems per precisione, che permetta la racommandazione di documenti di natura giuridica, i quali sono già stati analizzati e categorizzati appropriatamente, in maniera ottimale, il cui scopo sarebbe quello di accompagnare un sistema già implementato di Information Retrieval, istanziato sopra una web application, che permette di ricercare i documenti giuridici appena menzionati.