Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Study of the thermal effects induced by magnetic resonance on endocardial leads: numerical models and experimental validation

Magnetic resonance imaging (MRI) is today precluded to patients bearing active implantable medical devices AIMDs). The great advantages related to this diagnostic modality, together with the increasing number of people benefiting from implantable devices, in particular pacemakers(PM)and carioverter/defibrillators (ICD), is prompting the scientific community the study the possibility to extend MRI also to implanted patients. The MRI induced specific absorption rate (SAR) and the consequent heating of biological tissues is one of the major concerns that makes patients bearing metallic structures contraindicated for MRI scans. To date, both in-vivo and in-vitro studies have demonstrated the potentially dangerous temperature increase caused by the radiofrequency (RF) field generated during MRI procedures in the tissues surrounding thin metallic implants. On the other side, the technical evolution of MRI scanners and of AIMDs together with published data on the lack of adverse events have reopened the interest in this field and suggest that, under given conditions, MRI can be safely performed also in implanted patients. With a better understanding of the hazards of performing MRI scans on implanted patients as well as the development of MRI safe devices, we may soon enter an era where the ability of this imaging modality may be more widely used to assist in the appropriate diagnosis of patients with devices. In this study both experimental measures and numerical analysis were performed. Aim of the study is to systematically investigate the effects of the MRI RF filed on implantable devices and to identify the elements that play a major role in the induced heating. Furthermore, we aimed at developing a realistic numerical model able to simulate the interactions between an RF coil for MRI and biological tissues implanted with a PM, and to predict the induced SAR as a function of the particular path of the PM lead. The methods developed and validated during the PhD program led to the design of an experimental framework for the accurate measure of PM lead heating induced by MRI systems. In addition, numerical models based on Finite-Differences Time-Domain (FDTD) simulations were validated to obtain a general tool for investigating the large number of parameters and factors involved in this complex phenomenon. The results obtained demonstrated that the MRI induced heating on metallic implants is a real risk that represents a contraindication in extending MRI scans also to patient bearing a PM, an ICD, or other thin metallic objects. On the other side, both experimental data and numerical results show that, under particular conditions, MRI procedures might be consider reasonably safe also for an implanted patient. The complexity and the large number of variables involved, make difficult to define a unique set of such conditions: when the benefits of a MRI investigation cannot be obtained using other imaging techniques, the possibility to perform the scan should not be immediately excluded, but some considerations are always needed.

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.

Computational modelling of human stem cell-derived cardiomyocytes - Texture descriptors for biological image processing

This thesis investigates two distinct research topics. The main topic (Part I) is the computational modelling of cardiomyocytes derived from human stem cells, both embryonic (hESC-CM) and induced-pluripotent (hiPSC-CM). The aim of this research line lies in developing models of the electrophysiology of hESC-CM and hiPSC-CM in order to integrate the available experimental data and getting in-silico models to be used for studying/making new hypotheses/planning experiments on aspects not fully understood yet, such as the maturation process, the functionality of the Ca2+ hangling or why the hESC-CM/hiPSC-CM action potentials (APs) show some differences with respect to APs from adult cardiomyocytes. Chapter I.1 introduces the main concepts about hESC-CMs/hiPSC-CMs, the cardiac AP, and computational modelling. Chapter I.2 presents the hESC-CM AP model, able to simulate the maturation process through two developmental stages, Early and Late, based on experimental and literature data. Chapter I.3 describes the hiPSC-CM AP model, able to simulate the ventricular-like and atrial-like phenotypes. This model was used to assess which currents are responsible for the differences between the ventricular-like AP and the adult ventricular AP. The secondary topic (Part II) consists in the study of texture descriptors for biological image processing. Chapter II.1 provides an overview on important texture descriptors such as Local Binary Pattern or Local Phase Quantization. Moreover the non-binary coding and the multi-threshold approach are here introduced. Chapter II.2 shows that the non-binary coding and the multi-threshold approach improve the classification performance of cellular/sub-cellular part images, taken from six datasets. Chapter II.3 describes the case study of the classification of indirect immunofluorescence images of HEp2 cells, used for the antinuclear antibody clinical test. Finally the general conclusions are reported.

Nature and relevance of inherited blueprints on business models choices and innovation: an assessment

One important metaphor, referred to biological theories, used to investigate on organizational and business strategy issues is the metaphor about heredity; an area requiring further investigation is the extent to which the characteristics of blueprints inherited from the parent, helps in explaining subsequent development of the spawned ventures. In order to shed a light on the tension between inherited patterns and the new trajectory that may characterize spawned ventures’ development we propose a model aimed at investigating which blueprints elements might exert an effect on business model design choices and to which extent their persistence (or abandonment) determines subsequent business model innovation. Under the assumption that academic and corporate institutions transmit different genes to their spin-offs, we hence expect to have heterogeneity in elements that affect business model design choices and its subsequent evolution. This is the reason why we carry on a twofold analysis in the biotech (meta)industry: under a multiple-case research design, business model and especially its fundamental design elements and themes scholars individuated to decompose the construct, have been thoroughly analysed. Our purpose is to isolate the dimensions of business model that may have been the object of legacy and the ones along which an experimentation and learning process is more likely to happen, bearing in mind that differences between academic and corporate might not be that evident as expected, especially considering that business model innovation may occur.

Influence of Electromagnetic Fields On Biological Signalling: An Experimental and Theoretical Approach

The primary goals of this study were to develop a cell-free in vitro assay for the assessment of nonthermal electromagnetic (EMF) bioeffects and to develop theoretical models in accord with current experimental observations. Based upon the hypothesis that EMF effects operate by modulating Ca2+/CaM binding, an in vitro nitric oxide (NO) synthesis assay was developed to assess the effects of a pulsed radiofrequency (PRF) signal used for treatment of postoperative pain and edema. No effects of PRF on NO synthesis were observed. Effects of PRF on Ca2+/CaM binding were also assessed using a Ca2+-selective electrode, also yielding no EMF Ca2+/CaM binding. However, a PRF effect was observed on the interaction of hemoglobin (Hb) with tetrahydrobiopterin, leading to the development of an in vitro Hb deoxygenation assay, showing a reduction in the rate of Hb deoxygenation for exposures to both PRF and a static magnetic field (SMF). Structural studies using pyranine fluorescence, Gd3+ vibronic sideband luminescence and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy were conducted in order to ascertain the mechanism of this EMF effect on Hb. Also, the effect of SMF on Hb oxygen saturation (SO2) was assessed under gas-controlled conditions. These studies showed no definitive changes in protein/solvation structure or SO2 under equilibrium conditions, suggesting the need for real-time instrumentation or other means of observing out-of-equilibrium Hb dynamics. Theoretical models were developed for EMF transduction, effects on ion binding, neuronal spike timing, and dynamics of Hb deoxygenation. The EMF sensitivity and simplicity of the Hb deoxygenation assay suggest a new tool to further establish basic biophysical EMF transduction mechanisms. If an EMF-induced increase in the rate of deoxygenation can be demonstrated in vivo, then enhancement of oxygen delivery may be a new therapeutic method by which clinically relevant EMF-mediated enhancement of growth and repair processes can occur.

Master Equation: Biological Applications and Thermodynamic Description

It is well known that many realistic mathematical models of biological systems, such as cell growth, cellular development and differentiation, gene expression, gene regulatory networks, enzyme cascades, synaptic plasticity, aging and population growth need to include stochasticity. These systems are not isolated, but rather subject to intrinsic and extrinsic fluctuations, which leads to a quasi equilibrium state (homeostasis). The natural framework is provided by Markov processes and the Master equation (ME) describes the temporal evolution of the probability of each state, specified by the number of units of each species. The ME is a relevant tool for modeling realistic biological systems and allow also to explore the behavior of open systems. These systems may exhibit not only the classical thermodynamic equilibrium states but also the nonequilibrium steady states (NESS). This thesis deals with biological problems that can be treat with the Master equation and also with its thermodynamic consequences. It is organized into six chapters with four new scientific works, which are grouped in two parts: (1) Biological applications of the Master equation: deals with the stochastic properties of a toggle switch, involving a protein compound and a miRNA cluster, known to control the eukaryotic cell cycle and possibly involved in oncogenesis and with the propose of a one parameter family of master equations for the evolution of a population having the logistic equation as mean field limit. (2) Nonequilibrium thermodynamics in terms of the Master equation: where we study the dynamical role of chemical fluxes that characterize the NESS of a chemical network and we propose a one parameter parametrization of BCM learning, that was originally proposed to describe plasticity processes, to study the differences between systems in DB and NESS.

Semi-synthetic bile acids as novel drug candidate in liver diseases: physico-chemical characterization and HPLC-ES-MS/MS methods for their quali-quantitative analysis in different experimental animal models

The physico-chemical characterization, structure-pharmacokinetic and metabolism studies of new semi synthetic analogues of natural bile acids (BAs) drug candidates have been performed. Recent studies discovered a role of BAs as agonists of FXR and TGR5 receptor, thus opening new therapeutic target for the treatment of liver diseases or metabolic disorders. Up to twenty new semisynthetic analogues have been synthesized and studied in order to find promising novel drugs candidates. In order to define the BAs structure-activity relationship, their main physico-chemical properties (solubility, detergency, lipophilicity and affinity with serum albumin) have been measured with validated analytical methodologies. Their metabolism and biodistribution has been studied in “bile fistula rat”, model where each BA is acutely administered through duodenal and femoral infusion and bile collected at different time interval allowing to define the relationship between structure and intestinal absorption and hepatic uptake ,metabolism and systemic spill-over. One of the studied analogues, 6α-ethyl-3α7α-dihydroxy-5β-cholanic acid, analogue of CDCA (INT 747, Obeticholic Acid (OCA)), recently under approval for the treatment of cholestatic liver diseases, requires additional studies to ensure its safety and lack of toxicity when administered to patients with a strong liver impairment. For this purpose, CCl4 inhalation to rat causing hepatic decompensation (cirrhosis) animal model has been developed and used to define the difference of OCA biodistribution in respect to control animals trying to define whether peripheral tissues might be also exposed as a result of toxic plasma levels of OCA, evaluating also the endogenous BAs biodistribution. An accurate and sensitive HPLC-ES-MS/MS method is developed to identify and quantify all BAs in biological matrices (bile, plasma, urine, liver, kidney, intestinal content and tissue) for which a sample pretreatment have been optimized.

Altered transcriptional and epigenetic regulation affects brain cells proliferation and differentiation in the ultra-rare genetic demyelinating disease AGC1 deficiency: a study on in vitro models

AGC1 deficiency is a rare demyelinating disease caused by mutations in the SLC25A12 gene, which encodes for the mitochondrial glutamate-aspartate carrier 1 (AGC1/Alarar), highly expressed in the central nervous system. In neurons, impairment in AGC1 activity leads to reduction in N-acetyl-aspartate, the main lipid precursor for myelin synthesis (Profilo et al., 2017); in oligodendrocytes progenitors cells, AGC1 down regulation has been related to early arrest proliferation and premature differentiation (Petralla et al., 2019). Additionally, in vivo AGC1 deficiency models i.e., heterozygous mice for AGC1 knock-out and neurospheres from their subventricular zone, respectively, showed a global decrease in cells proliferation and a switch in neural stem cells (NSCs) commitment, with specific reduction in OPCs number and increase in neural and astrocytic pools (Petralla et al., 2019). Therefore, the present study aims to investigate the transcriptional and epigenetic regulation underlying the alterations observed in OPCs and NSCs biological mechanisms, in either AGC1 deficiency models of Oli-neu cells (murine immortalized oligodendrocytes precursors cells), partially silenced by a shRNA for SLC25A12 gene, and SVZ-derived neurospheres from AGC1+/- mice. Western blot and immunofluorescence analysis revealed significant variations in the expression of transcription factors involved in brain cells’ proliferation and differentiation, in association with altered histone post-translational modifications, as well as histone acetylases (HATs) and deacetylases (HDACs) activity/expression, suggesting an improper transcriptional and epigenetic regulation affecting both AGC1 deficiency in vitro models. Furthermore, given the large role of acetylation in controlling in specific time-windows OPC maturation (Hernandez and Casaccia; 2015), pharmacological HATs/HDACs inhibitions were performed, confirming the involvement of chromatin remodelling enzymes in the altered proliferation and early differentiation observed in the AGC1 deficiency models of siAGC1 Oli-neu cells and AGC1+/- mice-derived neurospheres.

Novel hydroxystearoyl-, heterocyclic and carbocyclic derivatives: synthesis and applications in biological field

This PhD project has been mainly focused on the synthesis of novel organic compounds containing heterocyclic and/or carbocyclic scaffold and on the study of stearic acid derivatives and their applications in biological field. The synthesis of novel derivatives of 9-hydroxystearic acid (9-HSA) evidenced how the presence of substituents on C9, able to make hydrogen bonds is of crucial importance for the biological activity. Also the position of the hydroxy group along the chain of hydroxystearic acids was investigated: regioisomers with the hydroxy group bound to odd carbons resulted more active than those bearing the hydroxy group on even carbons. Further, the insertion of (R)-9-HSA in magnetic nanoparticles gave a novel material which characterization remarked its suitability for drug delivery. Structural hybrids between amino aza-heterocycles and azelaic acid have been synthesized and some of them showed a selective activity towards osteosarcoma cell line U2OS. Several Apcin analogues bearing indole, benzothiazole, benzofurazan moieties connected to tryptaminyl-, amino pyridinyl-, pyrimidinyl- and pyrazinyl ring through a 1,1,1-trichloroethyl group were synthesized. Biological tests showed the importance of both the tryptaminyl and the pyrimidinyl moieties, confirming the effectiveness against acute leukemia models. The SNAr between 2-aminothiazole derivatives and 7-chlorodinitrobenzofuroxan revealed different behaviour depending from amino substituent of the thiazole. The reaction with 2-N-piperidinyl-, 2-N-morpholinyl-, or 2-N-pyrrolidinyl thiazole gave two isomeric species derived from the attack on C-5 of thiazole ring. Thiazoles substituted with primary- or not-cyclic secondary amines reacted with the exocyclic amino nitrogen atom giving a series of compounds whose biological activity have highlighted as they might be promising candidates for further development of antitumor agents. A series of 9-fluorenylidene derivatives, of interest in medical and optoelectronic field as organic scintillators, was synthesized through Wittig or Suzuky reaction and will be analyzed to test their potential scintillatory properties.

3D bioprinted organ models for drug screening

In recent years, 3D bioprinting has emerged as an innovative and versatile technology able to produce in vitro models that resemble the native spatial organization of organ tissues, by employing or more bioinks composed of various types of cells suspended in hydrogels. Natural and semi-synthetic hydrogels are extensively used for 3D bioprinting models since they can mimic the natural composition of the tissues, they are biocompatible and bioactive with customizable mechanical properties, allowing to support cell growth. The possibility to tailor hydrogels mechanical properties by modifying the chemical structures to obtain photo-crosslinkable materials, while maintaining their biocompatibility and biomimicry, make their use versatile and suitable to simulate a broad spectrum of physiological features. In this PhD Thesis, 3D bioprinted in vitro models with tailored mechanical properties and physiologically-like features were fabricated. AlgMa-based bioinks were employed to produce a living platform with gradient stiffness, with the aim to create an easy to handle and accessible biological tool to evaluate mechanobiology. In addition, GelMa, collagen and IPN of GelMa and collagen were used as bioinks to fabricate a proof-of-concept of 3D intestinal barrier, which include multiple cell components and multi-layered structure. A useful rheological guide to drive users to the selection of the suitable bioinks for 3D bioprinting and to correlate the model’s mechanical stability after crosslinking is proposed. In conclusion, a platform capable to reproduce models with physiological gradient stiffness was developed and the fabrication of 3D bioprinted intestinal models displaying a good hierarchical structure and cells composition was fully reported and successfully achieved. The good biological results obtained demonstrated that 3D bioprinting can be used for the fabrications of 3D models and that the mechanical properties of the external environment plays a key role on the cell pathways, viability and morphology.

Chemo-physical and biological mechanisms behind the anticancer activity of plasma activated Ringer's Lactate solution for the treatment of peritoneal carcinosis from primitive epithelial ovarian/tubular tumor

Cancer research and development of targeting agents in this field is based on robust studies using preclinical models. The failure rate of standardized treatment approaches for several solid tumors has led to the urgent need to fine-tune more sophisticated and faithful preclinical models able to recapitulate the features of in vivo human tumors, with the final aim to shed light on new potential therapeutic targets. Epithelial Ovarian Cancer (EOC) serous histotype (HGSOC) is one of the most lethal diseases in women due to its high aggressiveness (75% of patients diagnosed at FIGO III-IV state) and poor prognosis (less of 50% in 5 years), whose therapy often fails as chemoresistance sets in. This thesis aimed at using the novel perfusion-based bioreactor U-CUP that provides direct perfusion throughout the tumor tissue seeking to obtain an EOC 3D ex vivo model able to recapitulate the features of the original tumor including the tumor microenvironment and maintaining its cellular heterogeneity. Moreover, we optimized this approach so that it can be successfully applied to slow-frozen tumoral tissues, further extending the usefulness of this tool. We also investigated the effectiveness of Plasma Activated Ringer’s Lactate solution (PA-RL) against Epithelial Ovarian Cancer (EOC) serous histotype in both 2D and 3D cultures using ex-vivo specimens from HGSOC patients. We propose PA-RL as a novel therapy with local intraperitoneal administration, which could act on primary or metastatic ovarian tumors inducing a specific cancer cell death with reduced damage on the surrounding healthy tissues.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.