Genetic Manipulation, Gene Silencing and Biomarker Development in Multiple Experimental Models.

The thesis is set in three different parts, according to the relative experimental models. First, the domestic pig (Sus scrofa) is part of the study on reproductive biotechnologies: the transgenesis technique of Sperm Mediated Gene Transfer is widely studied starting from the quality of the semen, through the study of multiple uptakes of exogenous DNA and lastly used in the production of multi-transgenic blastocysts. Finally we managed to couple the transgenesis pipeline with sperm sorting and therefore produced transgenic embryos of predetermined sex. In the second part of the thesis the attention is on the fruit fly (Drosophila melanogaster) and on its derived cell line: the S2 cells. The in vitro and in vivo models are used to develop and validate an efficient way to knock down the myc gene. First an efficient in vitro protocol is described, than we demonstrate how the decrease in myc transcript remarkably affects the ribosome biogenesis through the study of Polysome gradients, rRNA content and qPCR. In vivo we identified two optimal drivers for the conditional silencing of myc, once the flies are fed with RU486: the first one is throughout the whole body (Tubulin), while the second is a head fat body driver (S32). With these results we present a very efficient model to study the role of myc in multiple aspects of translation. In the third and last part, the focus is on human derived lung fibroblasts (hLF-1), mouse tail fibroblasts and mouse tissues. We developed an efficient assay to quantify the total protein content of the nucleus on a single cell level via fluorescence. We coupled the protocol with classical immunofluorescence so to have at the same time general and particular information, demonstrating that during senescence nuclear proteins increase by 1.8 fold either in human cells, mouse cells and mouse tissues.

Computational Modeling of Cardiac Excitation-Contraction Coupling in Physiological and Pathological Conditions

The cardiomyocyte is a complex biological system where many mechanisms interact non-linearly to regulate the coupling between electrical excitation and mechanical contraction. For this reason, the development of mathematical models is fundamental in the field of cardiac electrophysiology, where the use of computational tools has become complementary to the classical experimentation. My doctoral research has been focusing on the development of such models for investigating the regulation of ventricular excitation-contraction coupling at the single cell level. In particular, the following researches are presented in this thesis: 1) Study of the unexpected deleterious effect of a Na channel blocker on a long QT syndrome type 3 patient. Experimental results were used to tune a Na current model that recapitulates the effect of the mutation and the treatment, in order to investigate how these influence the human action potential. Our research suggested that the analysis of the clinical phenotype is not sufficient for recommending drugs to patients carrying mutations with undefined electrophysiological properties. 2) Development of a model of L-type Ca channel inactivation in rabbit myocytes to faithfully reproduce the relative roles of voltage- and Ca-dependent inactivation. The model was applied to the analysis of Ca current inactivation kinetics during normal and abnormal repolarization, and predicts arrhythmogenic activity when inhibiting Ca-dependent inactivation, which is the predominant mechanism in physiological conditions. 3) Analysis of the arrhythmogenic consequences of the crosstalk between β-adrenergic and Ca-calmodulin dependent protein kinase signaling pathways. The descriptions of the two regulatory mechanisms, both enhanced in heart failure, were integrated into a novel murine action potential model to investigate how they concur to the development of cardiac arrhythmias. These studies show how mathematical modeling is suitable to provide new insights into the mechanisms underlying cardiac excitation-contraction coupling and arrhythmogenesis.

Closed-loop investigation of the dynamical properties of cardiomyocyte electrophysiology by means of Dynamic clamp and Mathematical modelling

The research field of my PhD concerns mathematical modeling and numerical simulation, applied to the cardiac electrophysiology analysis at a single cell level. This is possible thanks to the development of mathematical descriptions of single cellular components, ionic channels, pumps, exchangers and subcellular compartments. Due to the difficulties of vivo experiments on human cells, most of the measurements are acquired in vitro using animal models (e.g. guinea pig, dog, rabbit). Moreover, to study the cardiac action potential and all its features, it is necessary to acquire more specific knowledge about single ionic currents that contribute to the cardiac activity. Electrophysiological models of the heart have become very accurate in recent years giving rise to extremely complicated systems of differential equations. Although describing the behavior of cardiac cells quite well, the models are computationally demanding for numerical simulations and are very difficult to analyze from a mathematical (dynamical-systems) viewpoint. Simplified mathematical models that capture the underlying dynamics to a certain extent are therefore frequently used. The results presented in this thesis have confirmed that a close integration of computational modeling and experimental recordings in real myocytes, as performed by dynamic clamp, is a useful tool in enhancing our understanding of various components of normal cardiac electrophysiology, but also arrhythmogenic mechanisms in a pathological condition, especially when fully integrated with experimental data.

Modelli e Strumenti di Classifciazione Automatica per il Controllo dell'Accesso su Piattaforme Big Data

Dall'analisi dei big data si possono trarre degli enormi benefici in svariati ambiti applicativi. Uno dei fattori principali che contribuisce alla ricchezza dei big data, consiste nell'uso non previsto a priori di dati immagazzinati in precedenza, anche in congiunzione con altri dataset eterogenei: questo permette di trovare correlazioni significative e inaspettate tra i dati. Proprio per questo, il Valore, che il dato potenzialmente porta con sè, stimola le organizzazioni a raccogliere e immagazzinare sempre più dati e a ricercare approcci innovativi e originali per effettuare analisi su di essi. L’uso fortemente innovativo che viene fatto dei big data in questo senso e i requisiti tecnologici richiesti per gestirli hanno aperto importanti problematiche in materia di sicurezza e privacy, tali da rendere inadeguati o difficilmente gestibili, gli strumenti di sicurezza utilizzati finora nei sistemi tradizionali. Con questo lavoro di tesi si intende analizzare molteplici aspetti della sicurezza in ambito big data e offrire un possibile approccio alla sicurezza dei dati. In primo luogo, la tesi si occupa di comprendere quali sono le principali minacce introdotte dai big data in ambito di privacy, valutando la fattibilità delle contromisure presenti all’attuale stato dell’arte. Tra queste anche il controllo dell’accesso ha riscontrato notevoli sfide causate dalle necessità richieste dai big data: questo elaborato analizza pregi e difetti del controllo dell’accesso basato su attributi (ABAC), un modello attualmente oggetto di discussione nel dibattito inerente sicurezza e privacy nei big data. Per rendere attuabile ABAC in un contesto big data, risulta necessario l’ausilio di un supporto per assegnare gli attributi di visibilità alle informazioni da proteggere. L’obiettivo di questa tesi consiste nel valutare fattibilità, caratteristiche significative e limiti del machine learning come possibile approccio di utilizzo.

Diagnostic microchip to assay 3D colony-growth potential of captured circulating tumor cells

Microfluidic technology has been successfully applied to isolate very rare tumor-derived epithelial cells (circulating tumor cells, CTCs) from blood with relatively high yield and purity, opening up exciting prospects for early detection of cancer. However, a major limitation of state-of-the-art CTC-chips is their inability to characterize the behavior and function of captured CTCs, for example to obtain information on proliferative and invasive properties or, ultimately, tumor re-initiating potential. Although CTCs can be efficiently immunostained with markers reporting phenotype or fate (e.g. apoptosis, proliferation), it has not yet been possible to reliably grow captured CTCs over long periods of time and at single cell level. It is challenging to remove CTCs from a microchip after capture, therefore such analyses should ideally be performed directly on-chip. To address this challenge, we merged CTC capture with three-dimensional (3D) tumor cell culture on the same microfluidic platform. PC3 prostate cancer cells were isolated from spiked blood on a transparent PDMS CTC-chip, encapsulated on-chip in a biomimetic hydrogel matrix (QGel™) that was formed in situ, and their clonal 3D spheroid growth potential was assessed by microscopy over one week in culture. The possibility to clonally expand a subset of captured CTCs in a near-physiological in vitro model adds an important element to the expanding CTC-chip toolbox that ultimately should improve prediction of treatment responses and disease progression.

The pollen tube: a soft shell with a hard core

Plant cell expansion is controlled by a fine-tuned balance between intracellular turgor pressure, cell wall loosening and cell wall biosynthesis. To understand these processes, it is important to gain in-depth knowledge of cell wall mechanics. Pollen tubes are tip-growing cells that provide an ideal system to study mechanical properties at the single cell level. With the available approaches it was not easy to measure important mechanical parameters of pollen tubes, such as the elasticity of the cell wall. We used a cellular force microscope (CFM) to measure the apparent stiffness of lily pollen tubes. In combination with a mechanical model based on the finite element method (FEM), this allowed us to calculate turgor pressure and cell wall elasticity, which we found to be around 0.3 MPa and 20–90 MPa, respectively. Furthermore, and in contrast to previous reports, we showed that the difference in stiffness between the pollen tube tip and the shank can be explained solely by the geometry of the pollen tube. CFM, in combination with an FEM-based model, provides a powerful method to evaluate important mechanical parameters of single, growing cells. Our findings indicate that the cell wall of growing pollen tubes has mechanical properties similar to rubber. This suggests that a fully turgid pollen tube is a relatively stiff, yet flexible cell that can react very quickly to obstacles or attractants by adjusting the direction of growth on its way through the female transmitting tissue.

Natriuretic peptides and nitric oxide stimulate cGMP synthesis in different cellular compartments.

Cyclic nucleotide-gated (CNG) channels are a family of ion channels activated by the binding of cyclic nucleotides. Endogenous channels have been used to measure cyclic nucleotide signals in photoreceptor outer segments and olfactory cilia for decades. Here we have investigated the subcellular localization of cGMP signals by monitoring CNG channel activity in response to agonists that activate either particulate or soluble guanylyl cyclase. CNG channels were heterologously expressed in either human embryonic kidney (HEK)-293 cells that stably overexpress a particulate guanylyl cyclase (HEK-NPRA cells), or cultured vascular smooth muscle cells (VSMCs). Atrial natriuretic peptide (ANP) was used to activate the particulate guanylyl cyclase and the nitric oxide donor S-nitroso-n-acetylpenicillamine (SNAP) was used to activate the soluble guanylyl cyclase. CNG channel activity was monitored by measuring Ca2+ or Mn2+ influx through the channels using the fluorescent dye, fura-2. We found that in HEK-NPRA cells, ANP-induced increases in cGMP levels activated CNG channels in a dose-dependent manner (0.05-10 nM), whereas SNAP (0.01-100 microM) induced increases in cGMP levels triggered little or no activation of CNG channels (P < 0.01). After pretreatment with 100 microM 3-isobutyl-1-methylxanthine (IBMX), a nonspecific phosphodiesterase inhibitor, ANP-induced Mn2+ influx through CNG channels was significantly enhanced, while SNAP-induced Mn2+ influx remained small. In contrast, we found that in the presence of IBMX, both 1 nM ANP and 100 microM SNAP triggered similar increases in total cGMP levels. We next sought to determine if cGMP signals are compartmentalized in VSMCs, which endogenously express particulate and soluble guanylyl cyclase. We found that 10 nM ANP induced activation of CNG channels more readily than 100 muM SNAP; whereas 100 microM SNAP triggered higher levels of total cellular cGMP accumulation. These results suggest that cGMP signals are spatially segregated within cells, and that the functional compartmentalization of cGMP signals may underlie the unique actions of ANP and nitric oxide.

Synaptotagmin-2 controls regulated exocytosis but not other secretory responses of mast cells.

Mast cell degranulation is a highly regulated, calcium-dependent process, which is important for the acute release of inflammatory mediators during the course of many pathological conditions. We previously found that Synaptotagmin-2, a calcium sensor in neuronal exocytosis, was expressed in a mast cell line. We postulated that this protein may be involved in the control of mast cell-regulated exocytosis, and we generated Synaptotagmin-2 knock-out mice to test our hypothesis. Mast cells from this mutant animal conferred an abnormally decreased passive cutaneous anaphylaxis reaction on mast cell-deficient mice that correlated with a specific defect in mast cell-regulated exocytosis, leaving constitutive exocytosis and nonexocytic mast cell effector responses intact. This defect was not secondary to abnormalities in the development, maturation, migration, morphology, synthesis, and storage of inflammatory mediators, or intracellular calcium transients of the mast cells. Unlike neurons, the lack of Synaptotagmin-2 in mast cells was not associated with increased spontaneous exocytosis.

Quantifying the turnover of transcriptional subclasses of HIV-1-infected cells

HIV-1-infected cells in peripheral blood can be grouped into different transcriptional subclasses. Quantifying the turnover of these cellular subclasses can provide important insights into the viral life cycle and the generation and maintenance of latently infected cells. We used previously published data from five patients chronically infected with HIV-1 that initiated combination antiretroviral therapy (cART). Patient-matched PCR for unspliced and multiply spliced viral RNAs combined with limiting dilution analysis provided measurements of transcriptional profiles at the single cell level. Furthermore, measurement of intracellular transcripts and extracellular virion-enclosed HIV-1 RNA allowed us to distinguish productive from non-productive cells. We developed a mathematical model describing the dynamics of plasma virus and the transcriptional subclasses of HIV-1-infected cells. Fitting the model to the data allowed us to better understand the phenotype of different transcriptional subclasses and their contribution to the overall turnover of HIV-1 before and during cART. The average number of virus-producing cells in peripheral blood is small during chronic infection. We find that a substantial fraction of cells can become defectively infected. Assuming that the infection is homogenous throughout the body, we estimate an average in vivo viral burst size on the order of 104 virions per cell. Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells, and indicates that cells containing solely unspliced viral RNA are a good marker for viral latency. The model illustrates how the pool of latently infected cells becomes rapidly established during the first months of acute infection and continues to increase slowly during the first years of chronic infection. Having a detailed understanding of this process will be useful for the evaluation of viral eradication strategies that aim to deplete the latent reservoir of HIV-1.

Tissue Crowding Induces Caspase-Dependent Competition for Space.

Regulation of tissue size requires fine tuning at the single-cell level of proliferation rate, cell volume, and cell death. Whereas the adjustment of proliferation and growth has been widely studied [1, 2, 3, 4 and 5], the contribution of cell death and its adjustment to tissue-scale parameters have been so far much less explored. Recently, it was shown that epithelial cells could be eliminated by live-cell delamination in response to an increase of cell density [6]. Cell delamination was supposed to occur independently of caspase activation and was suggested to be based on a gradual and spontaneous disappearance of junctions in the delaminating cells [6]. Studying the elimination of cells in the midline region of the Drosophila pupal notum, we found that, contrary to what was suggested before, Caspase 3 activation precedes and is required for cell delamination. Yet, using particle image velocimetry, genetics, and laser-induced perturbations, we confirmed [ 6] that local tissue crowding is necessary and sufficient to drive cell elimination and that cell elimination is independent of known fitness-dependent competition pathways [ 7, 8 and 9]. Accordingly, activation of the oncogene Ras in clones was sufficient to compress the neighboring tissue and eliminate cells up to several cell diameters away from the clones. Mechanical stress has been previously proposed to contribute to cell competition [ 10 and 11]. These results provide the first experimental evidences that crowding-induced death could be an alternative mode of super-competition, namely mechanical super-competition, independent of known fitness markers [ 7, 8 and 9], that could promote tumor growth.

The role of the intracellular second messenger cAMP in the induction of long-term morphological changes in sensory neurons of {\it Aplysia\/}

The present work examines the role of cAMP in the induction of the type of long-term morphological changes that have been shown to be correlated with long-term sensitization in Aplysia.^ To examine this issue, cAMP was injected into individual tail sensory neurons in the pleural ganglion to mimic, at the single cell level, the effects of behavioral training. After a 22 hr incubation period, the same cells were filled with horseradish peroxidase and 2 hours later the tissue was fixed and processed. Morphological analysis revealed that cAMP induced an increase in two morphological features of the neurons, varicosities and branch points. These structural alterations, which are similar to those seen in siphon sensory neurons of the abdominal ganglion following long-term sensitization training of the siphon-gill withdrawal reflex, could subserve the altered behavioral response of the animal. These results expose another role played by cAMP in the induction of learning, the initiation of a structural substrate, which, in concert with other correlates, underlies learning.^ cAMP was injected into sensory neurons in the presence of the reversible protein synthesis inhibitor, anisomycin. The presence of anisomycin during and immediately following the nucleotide injection completely blocked the structural remodeling. These results indicate that the induction of morphological changes by cAMP is a process dependent on protein synthesis.^ To further examine the temporal requirement for protein synthesis in the induction of these changes, the time of anisomycin exposure was varied. The results indicate that the cellular processes triggered by cAMP are sensitive to the inhibition of protein synthesis for at least 7 hours after the nucleotide injection. This is a longer period of sensitivity than that for the induction of another correlate of long-term sensitization, facilitation of the sensory to motor neuron synaptic connection. Thus, these findings demonstrate that the period of sensitivity to protein synthesis inhibition is not identical for all correlates of learning. In addition, since the induction of the morphological changes can be blocked by anisomycin pulses administered at different times during and following the cAMP injection, this suggests that cAMP is triggering a cascade of protein synthesis, with successive rounds of synthesis being dependent on successful completion of preceding rounds. Inhibition at any time during this cascade can block the entire process and so prevent the development of the structural changes.^ The extent to which cAMP can mimic the structural remodeling induced by long-term training was also examined. Animals were subjected to unilateral sensitization training and the morphology of the sensory neurons was examined twenty-four hours later. Both cAMP injection and long-term training produced a twofold increase in varicosities and approximately a fifty percent increase in the number of branch points in the sensory neuron arborization within the pleural ganglion. (Abstract shortened by UMI.) ^

Comparison and Assessment of Two Emission inventories for the Madrid Region

Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

About the origin of low wafer performance and crystal defect generation on seed-cast growth of industrial mono-like silicon ingots

The era of the seed-cast grown monocrystalline-based silicon ingots is coming. Mono-like, pseudomono or quasimono wafers are product labels that can be nowadays found in the market, as a critical innovation for the photovoltaic industry. They integrate some of the most favorable features of the conventional silicon substrates for solar cells, so far, such as the high solar cell efficiency offered by the monocrystalline Czochralski-Si (Cz-Si) wafers and the lower cost, high productivity and full square-shape that characterize the well-known multicrystalline casting growth method. Nevertheless, this innovative crystal growth approach still faces a number of mass scale problems that need to be resolved, in order to gain a deep, 100% reliable and worldwide market: (i) extended defects formation during the growth process; (ii) optimization of the seed recycling; and (iii) parts of the ingots giving low solar cells performance, which directly affect the production costs and yield of this approach. Therefore, this paper presents a series of casting crystal growth experiments and characterization studies from ingots, wafers and cells manufactured in an industrial approach, showing the main sources of crystal defect formation, impurity enrichment and potential consequences at solar cell level. The previously mentioned technological drawbacks are directly addressed, proposing industrial actions to pave the way of this new wafer technology to high efficiency solar cells.

Large scale multiphysics modeling of neurites

Situado en el límite entre Ingeniería, Informática y Biología, la mecánica computacional de las neuronas aparece como un nuevo campo interdisciplinar que potencialmente puede ser capaz de abordar problemas clínicos desde una perspectiva diferente. Este campo es multiescala por naturaleza, yendo desde la nanoescala (como, por ejemplo, los dímeros de tubulina) a la macroescala (como, por ejemplo, el tejido cerebral), y tiene como objetivo abordar problemas que son complejos, y algunas veces imposibles, de estudiar con medios experimentales. La modelización computacional ha sido ampliamente empleada en aplicaciones Neurocientíficas tan diversas como el crecimiento neuronal o la propagación de los potenciales de acción compuestos. Sin embargo, en la mayoría de los enfoques de modelización hechos hasta ahora, la interacción entre la célula y el medio/estímulo que la rodea ha sido muy poco explorada. A pesar de la tremenda importancia de esa relación en algunos desafíos médicos—como, por ejemplo, lesiones traumáticas en el cerebro, cáncer, la enfermedad del Alzheimer—un puente que relacione las propiedades electrofisiológicas-químicas y mecánicas desde la escala molecular al nivel celular todavía no existe. Con ese objetivo, esta investigación propone un marco computacional multiescala particularizado para dos escenarios respresentativos: el crecimiento del axón y el acomplamiento electrofisiológicomecánico de las neuritas. En el primer caso, se explora la relación entre los constituyentes moleculares del axón durante su crecimiento y sus propiedades mecánicas resultantes, mientras que en el último, un estímulo mecánico provoca deficiencias funcionales a nivel celular como consecuencia de sus alteraciones electrofisiológicas-químicas. La modelización computacional empleada en este trabajo es el método de las diferencias finitas, y es implementada en un nuevo programa llamado Neurite. Aunque el método de los elementos finitos es también explorado en parte de esta investigación, el método de las diferencias finitas tiene la flexibilidad y versatilidad necesaria para implementar mode los biológicos, así como la simplicidad matemática para extenderlos a simulaciones a gran escala con un coste computacional bajo. Centrándose primero en el efecto de las propiedades electrofisiológicas-químicas sobre las propiedades mecánicas, una versión adaptada de Neurite es desarrollada para simular la polimerización de los microtúbulos en el crecimiento del axón y proporcionar las propiedades mecánicas como función de la ocupación de los microtúbulos. Después de calibrar el modelo de crecimiento del axón frente a resultados experimentales disponibles en la literatura, las características mecánicas pueden ser evaluadas durante la simulación. Las propiedades mecánicas del axón muestran variaciones dramáticas en la punta de éste, donde el cono de crecimiento soporta las señales químicas y mecánicas. Bansándose en el conocimiento ganado con el modelo de diferencias finitas, y con el objetivo de ir de 1D a 3D, este esquema preliminar pero de una naturaleza innovadora allana el camino a futuros estudios con el método de los elementos finitos. Centrándose finalmente en el efecto de las propiedades mecánicas sobre las propiedades electrofisiológicas- químicas, Neurite es empleado para relacionar las cargas mecánicas macroscópicas con las deformaciones y velocidades de deformación a escala microscópica, y simular la propagación de la señal eléctrica en las neuritas bajo carga mecánica. Las simulaciones fueron calibradas con resultados experimentales publicados en la literatura, proporcionando, por tanto, un modelo capaz de predecir las alteraciones de las funciones electrofisiológicas neuronales bajo cargas externas dañinas, y uniendo lesiones mecánicas con las correspondientes deficiencias funcionales. Para abordar simulaciones a gran escala, aunque otras arquitecturas avanzadas basadas en muchos núcleos integrados (MICs) fueron consideradas, los solvers explícito e implícito se implementaron en unidades de procesamiento central (CPU) y unidades de procesamiento gráfico (GPUs). Estudios de escalabilidad fueron llevados acabo para ambas implementaciones mostrando resultados prometedores para casos de simulaciones extremadamente grandes con GPUs. Esta tesis abre la vía para futuros modelos mecánicos con el objetivo de unir las propiedades electrofisiológicas-químicas con las propiedades mecánicas. El objetivo general es mejorar el conocimiento de las comunidades médicas y de bioingeniería sobre la mecánica de las neuronas y las deficiencias funcionales que aparecen de los daños producidos por traumatismos mecánicos, como lesiones traumáticas en el cerebro, o enfermedades neurodegenerativas como la enfermedad del Alzheimer. ABSTRACT Sitting at the interface between Engineering, Computer Science and Biology, Computational Neuron Mechanics appears as a new interdisciplinary field potentially able to tackle clinical problems from a new perspective. This field is multiscale by nature, ranging from the nanoscale (e.g., tubulin dimers) to the macroscale (e.g., brain tissue), and aims at tackling problems that are complex, and sometime impossible, to study through experimental means. Computational modeling has been widely used in different Neuroscience applications as diverse as neuronal growth or compound action potential propagation. However, in the majority of the modeling approaches done in this field to date, the interactions between the cell and its surrounding media/stimulus have been rarely explored. Despite of the tremendous importance of such relationship in several medical challenges—e.g., traumatic brain injury (TBI), cancer, Alzheimer’s disease (AD)—a bridge between electrophysiological-chemical and mechanical properties of neurons from the molecular scale to the cell level is still lacking. To this end, this research proposes a multiscale computational framework particularized for two representative scenarios: axon growth and electrophysiological-mechanical coupling of neurites. In the former case, the relation between the molecular constituents of the axon during its growth and its resulting mechanical properties is explored, whereas in the latter, a mechanical stimulus provokes functional deficits at cell level as a consequence of its electrophysiological-chemical alterations. The computational modeling approach chosen in this work is the finite difference method (FDM), and was implemented in a new program called Neurite. Although the finite element method (FEM) is also explored as part of this research, the FDM provides the necessary flexibility and versatility to implement biological models, as well as the mathematical simplicity to extend them to large scale simulations with a low computational cost. Focusing first on the effect of electrophysiological-chemical properties on the mechanical proper ties, an adaptation of Neurite was developed to simulate microtubule polymerization in axonal growth and provide the axon mechanical properties as a function of microtubule occupancy. After calibrating the axon growth model against experimental results available in the literature, the mechanical characteristics can be tracked during the simulation. The axon mechanical properties show dramatic variations at the tip of the axon, where the growth cone supports the chemical and mechanical signaling. Based on the knowledge gained from the FDM scheme, and in order to go from 1D to 3D, this preliminary yet novel scheme paves the road for future studies with FEM. Focusing then on the effect of mechanical properties on the electrophysiological-chemical properties, Neurite was used to relate macroscopic mechanical loading to microscopic strains and strain rates, and simulate the electrical signal propagation along neurites under mechanical loading. The simulations were calibrated against experimental results published in the literature, thus providing a model able to predict the alteration of neuronal electrophysiological function under external damaging load, and linking mechanical injuries to subsequent acute functional deficits. To undertake large scale simulations, although other state-of-the-art architectures based on many integrated cores (MICs) were considered, the explicit and implicit solvers were implemented for central processing units (CPUs) and graphics processing units (GPUs). Scalability studies were done for both implementations showing promising results for extremely large scale simulations with GPUs. This thesis opens the avenue for future mechanical modeling approaches aimed at linking electrophysiological- chemical properties to mechanical properties. Its overarching goal is to enhance the bioengineering and medical communities knowledge on neuronal mechanics and functional deficits arising from damages produced by direct mechanical insults, such as TBI, or neurodegenerative evolving illness, such as AD.