Analisi di modelli e sviluppo di algoritmi computazionali per lo studio della dinamica di una rete neuro-astrocitaria

Sin dalla loro scoperta si è pensato che le cellule gliali avessero la sola funzione di sostegno e nutrizionale per i neuroni e che l’elaborazione dell’informazione nel cervello fosse un compito adibito ai soli neuroni. Tra le cellule gliali la varietà più numerosa e più studiata è quella degli astrociti, cosiddetti per la loro caratteristica forma a stella. Negli ultimi dieci anni è stato dimostrato che essi costituiscono il terzo elemento attivo della sinapsi chimica, modulando la sua funzione: tali cellule ascoltano ed intervengono nella comunicazione neuronale mediante una codifica tra linguaggio astrocitario (variazioni nella concentrazione di calcio intracellulare) e linguaggio neurale (emissione di neurotrasmettitori). L’obiettivo principale di questo lavoro consiste nell’analisi di modelli per la comprensione del funzionamento delle interazioni tra neuroni ed astrociti, al fine di creare un sistema computazionale di complessità trattabile con cui poter simulare e quindi cercare di capire le dinamiche che potrebbe presentare una rete neuro-astrocitaria

Variational inference for Gaussian-jump processes with application in gene regulation

In the last decades, the explosion of data from quantitative techniques has revolutionised our understanding of biological processes. In this scenario, advanced statistical methods and algorithms are becoming fundamental to decipher the dynamics of biochemical mechanisms such those involved in the regulation of gene expression. Here we develop mechanistic models and approximate inference techniques to reverse engineer the dynamics of gene regulation, from mRNA and/or protein time series data. We start from an existent variational framework for statistical inference in transcriptional networks. The framework is based on a continuous-time description of the mRNA dynamics in terms of stochastic differential equations, which are governed by latent switching variables representing the on/off activity of regulating transcription factors. The main contributions of this work are the following. We speeded-up the variational inference algorithm by developing a method to compute a posterior approximate distribution over the latent variables using a constrained optimisation algorithm. In addition to computational benefits, this method enabled the extension to statistical inference in networks with a combinatorial model of regulation. A limitation of this framework is the fact that inference is possible only in transcriptional networks with a single-layer architecture (where a single or couples of transcription factors regulate directly an arbitrary number of target genes). The second main contribution in this work is the extension of the inference framework to hierarchical structures, such as feed-forward loop. In the last contribution we define a general structure for transcription-translation networks. This work is important since it provides a general statistical framework to model complex dynamics in gene regulatory networks. The framework is modular and scalable to realistically large systems with general architecture, thus representing a valuable alternative to traditional differential equation models. All models are embedded in a Bayesian framework; inference is performed using a variational approach and compared to exact inference where possible. We apply the models to the study of different biological systems, from the metabolism in E. coli to the circadian clock in the picoalga O. tauri

A subsystems approach for parameter estimation of ODE models of hybrid systems

We present a new method for parameter identification of ODE system descriptions based on data measurements. Our method works by splitting the system into a number of subsystems and working on each of them separately, thereby being easily parallelisable, and can also deal with noise in the observations.Comment: In Proceedings HSB 2012, arXiv:1208.315

Multiple Antitumor Molecular Mechanisms Are Activated by a Fully Synthetic and Stabilized Pharmaceutical Product Delivering the Active Compound Sulforaphane (SFX-01) in Preclinical Model of Human Glioblastoma

Frequent relapses and therapeutic resistance make the management of glioblastoma (GBM, grade IV glioma), extremely difficult. Therefore, it is necessary to develop new pharmacological compounds to be used as a single treatment or in combination with current therapies in order to improve their effectiveness and reduce cytotoxicity for non-tumor cells. SFX-01 is a fully synthetic and stabilized pharmaceutical product containing the α-cyclodextrin that delivers the active compound 1-isothiocyanato-4-methyl-sulfinylbutane (SFN) and maintains biological activities of SFN. In this study, we verified whether SFX-01 was active in GBM preclinical models. Our data demonstrate that SFX-01 reduced cell proliferation and increased cell death in GBM cell lines and patient-derived glioma initiating cells (GICs) with a stem cell phenotype. The antiproliferative effects of SFX-01 were associated with a reduction in the stemness of GICs and reversion of neural-to-mesenchymal trans-differentiation (PMT) closely related to epithelial-to-mesenchymal trans-differentiation (EMT) of epithelial tumors. Commonly, PMT reversion decreases the invasive capacity of tumor cells and increases the sensitivity to pharmacological and instrumental therapies. SFX-01 induced caspase-dependent apoptosis, through both mitochondrion-mediated intrinsic and death-receptor-associated extrinsic pathways. Here, we demonstrate the involvement of reactive oxygen species (ROS) through mediating the reduction in the activity of essential molecular pathways, such as PI3K/Akt/mTOR, ERK, and STAT-3. SFX-01 also reduced the in vivo tumor growth of subcutaneous xenografts and increased the disease-free survival (DFS) and overall survival (OS), when tested in orthotopic intracranial GBM models. These effects were associated with reduced expression of HIF1α which, in turn, down-regulates neo-angiogenesis. So, SFX-01 may have potent anti-glioma effects, regulating important aspects of the biology of this neoplasia, such as hypoxia, stemness, and EMT reversion, which are commonly activated in this neoplasia and are responsible for therapeutic resistance and glioma recurrence. SFX-01 deserves to be considered as an emerging anticancer agent for the treatment of GBM. The possible radio- and chemo sensitization potential of SFX-01 should also be evaluated in further preclinical and clinical studies