Μοντελοποίηση Συμπεριφοράς κυττάρων μέσω ανάλυσης σημάτων

286 σ.Η εν λόγω διδακτορική διατριβή πραγματεύεται την μοντελοποίηση ενδοκυτταρικών σηματοδοτικών μονοπατιών με σκοπό την κατανόηση της λειτουργίας και συμπεριφοράς βιολογικών συστημάτων σε περίπλοκες ασθένειες. Τα σηματοδοτικά μονοπάτια απεικονίζουν αλληλεπιδράσεις μεταξύ πρωτεινών και περιγράφουν πως τα κύτταρα αποκρίνονται σε εξωτερικά ερεθίσματα. Τα μονοπάτια αυτά είναι διαθέσιμα στην βιβλιογραφία, σε διαδικτυακές βάσεις δεδομένων. Τα τελευταία χρόνια η διεθνής κοινότητα κάνει προσπάθιες να τα μοντελοποιήσει υιοθετώντας μεθοδολογίες από την θεωρία συστημάτων, προς την δημιουργία εκτελέσιμων μοντέλων που θα δίνουν την δυνατότητα προσομοίωσης σημαντικών κυτταρικών διεργασιών. Στην παρούσα διδακτορική διατριβή, ο υποψήφιος εφαρμόζει μεθόδους Ακέραιου γραμμικού προγραμματισμού για την μοντελοποίηση ενδοκυτταρικών σηματοδοτικών μονοπατιών και την εκπαίδευση των εν λόγω μοντέλων σε πειραματικά δεδομένα με σκοπό την πιστή απεικόνιση των ενδοκυτταρικών σηματοδοτικών διεργασιών στις υπο εξέταση κυτταρικές σειρές. Αποτελέσματα της έρευνας αυτής δημοσιεύτηκαν σε έγκριτα επιστημονικά περιδικά και διεθνή συνέδρεια.Modeling of signal transduction pathways is of the utmost importance in understanding how cells respond to environmental perturbations. Signaling pathways consist of a set of protein protein interactions, identified via high throughput proteomic experiments and made available through on line pathway databases. Over the past few years a range of methods have been proposed to model these networks in an attempt to gain insight into the cells function and uncover the etiology underlying complex disease. Aim of this work is the development of a novel class of methodologies that model signal transduction networks as logic models, and using regular optimizatino formulations cross reference them with high throughput proteomic data to construct predictive models of the signaling mechanisms of the interrogated cell linesΙωάννης Ν. Μελά

Data on verbal expressions for thermal sensation and comfort in the Greek language

This article presents data collected during a web-based survey on expressions used to describe thermal sensation and comfort in the Greek language. The survey used a structured questionnaire and delivered through Google Forms. The survey was promoted through social networks and conducted in spring 2019. The data presented herein comprise of the participants’ responses to the questionnaire. A total of 359 questionnaires were completed. The participants were Greek speakers, older than 12, with at least a basic knowledge of the English language. The participants were asked to: (a) select the most appropriate translation, from English to Greek, of the nine-point ISO 10551 scale of perceptual judgment on personal thermal state, (b) formulate five, seven and nine-point thermal sensation scales, (c) report the category of the thermal sensation scale that signifies thermal comfort and (d) to assess the relative distances between the thermal sensation categories of the five, seven and nine-point thermal sensation scales. For the translation of the ISO 10551, the respondents were allowed to choose from a list of 30 Greek wordings. The data have been analysed in the research article entitled “Native influences on the construction of thermal sensation scales” [1]

Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data

Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC.National Institutes of Health (U.S.) (Grant U54-CA119267)National Institutes of Health (U.S.) (Grant R01-CA96504

Combined logical and data-driven models for linking signalling pathways to cellular response

Background Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity. Results In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines. Conclusions We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.Marie Curie International Reintegration Grants (MIRG-14-CT-2007-046531)Vertex Pharmaceuticals IncorporatedBundesministerium für Wissenschaft und Forschung (HepatoSys)Massachusetts Institute of Technology (Rockwell International Career Development Professorship)Bundesministerium für Wissenschaft und Forschung (HepatoSys 0313081D

The Friction Coefficient of Fractal Aggregates in the Continuum and Transition Regimes

A methodology is introduced for friction-coefficient calculations of fractal-like aggregates that relates the friction coefficient to a solution of the diffusion equation. Synthetic fractal aggregates were created with a cluster-cluster aggregation algorithm. Their fiction coefficients were obtained from gas molecule-aggregate collision rates that were calculated with the COMSOL Multiphysics software. Results were compared and validated with literature values. The effect of aggregate structure on dynamical properties of the aggregate, in particular mobility, was also studied. Both the fractal dimension and the fractal prefactor are required to characterize fully an aggregate.JRC.F.8-Sustainable Transpor

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142916/1/psp412272.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142916/2/psp412272_am.pd

Translational Systems Pharmacology-Based Predictive Assessment of Drug-Induced Cardiomyopathy

Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug’s signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs’ signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein–protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury.Institute for Collaborative Biotechnologies (Grant W911NF-09-0001

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.National Institutes of Health (U.S.) (Grant P50-GM068762)National Institutes of Health (U.S.) (Grant R24-DK090963)United States. Army Research Office (Grant W911NF-09-0001)German Research Foundation (Grant GSC 111