Integration of multi-scale protein interactions for biomedical data analysis

With the advancement of modern technologies, we observe an increasing accumulation of biomedical data about diseases. There is a need for computational methods to sift through and extract knowledge from the diverse data available in order to improve our mechanistic understanding of diseases and improve patient care. Biomedical data come in various forms as exemplified by the various omics data. Existing studies have shown that each form of omics data gives only partial information on cells state and motivated jointly mining multi-omics, multi-modal data to extract integrated system knowledge. The interactome is of particular importance as it enables the modelling of dependencies arising from molecular interactions. This Thesis takes a special interest in the multi-scale protein interactome and its integration with computational models to extract relevant information from biomedical data. We define multi-scale interactions at different omics scale that involve proteins: pairwise protein-protein interactions, multi-protein complexes, and biological pathways. Using hypergraph representations, we motivate considering higher-order protein interactions, highlighting the complementary biological information contained in the multi-scale interactome. Based on those results, we further investigate how those multi-scale protein interactions can be used as either prior knowledge, or auxiliary data to develop machine learning algorithms. First, we design a neural network using the multi-scale organization of proteins in a cell into biological pathways as prior knowledge and train it to predict a patient's diagnosis based on transcriptomics data. From the trained models, we develop a strategy to extract biomedical knowledge pertaining to the diseases investigated. Second, we propose a general framework based on Non-negative Matrix Factorization to integrate the multi-scale protein interactome with multi-omics data. We show that our approach outperforms the existing methods, provide biomedical insights and relevant hypotheses for specific cancer types

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs

Discovery and Interpretation of Subspace Structures in Omics Data by Low-Rank Representation

Indiana University-Purdue University Indianapolis (IUPUI)Biological functions in cells are highly complicated and heterogenous, and can be reflected by omics data, such as gene expression levels. Detecting subspace structures in omics data and understanding the diversity of the biological processes is essential to the full comprehension of biological mechanisms and complicated biological systems. In this thesis, we are developing novel statistical learning approaches to reveal the subspace structures in omics data. Specifically, we focus on three types of subspace structures: low-rank subspace, sparse subspace and covariates explainable subspace. For low-rank subspace, we developed a semi-supervised model SSMD to detect cell type specific low-rank structures and predict their relative proportions across different tissue samples. SSMD is the first computational tool that utilizes semi-supervised identification of cell types and their marker genes specific to each mouse tissue transcriptomics data, for better understanding of the disease microenvironment and downstream disease mechanism. For sparsity-driven sparse subspace, we proposed a novel positive and unlabeled learning model, namely PLUS, that could identify cancer metastasis related genes, predict cancer metastasis status and specifically address the under-diagnosis issue in studying metastasis potential. We found PLUS predicted metastasis potential at diagnosis have significantly strong association with patient’s progression-free survival in their follow-up data. Lastly, to discover the covariates explainable subspace, we proposed an analytical pipeline based on covariance regression, namely, scCovReg. We utilized scCovReg to detect the pathway level second-order variations using scRNA-Seq data in a statistically powerful manner, and to associate the second-order variations with important subject-level characteristics, such as disease status. In conclusion, we presented a set of state-of-the-art computational solutions for identifying sparse subspaces in omics data, which promise to provide insights into the mechanism in complex diseases

Complex Network Analysis for Scientific Collaboration Prediction and Biological Hypothesis Generation

With the rapid development of digitalized literature, more and more knowledge has been discovered by computational approaches. This thesis addresses the problem of link prediction in co-authorship networks and protein--protein interaction networks derived from the literature. These networks (and most other types of networks) are growing over time and we assume that a machine can learn from past link creations by examining the network status at the time of their creation. Our goal is to create a computationally efficient approach to recommend new links for a node in a network (e.g., new collaborations in co-authorship networks and new interactions in protein--protein interaction networks). We consider edges in a network that satisfies certain criteria as training instances for the machine learning algorithms. We analyze the neighborhood structure of each node and derive the topological features. Furthermore, each node has rich semantic information when linked to the literature and can be used to derive semantic features. Using both types of features, we train machine learning models to predict the probability of connection for the new node pairs. We apply our idea of link prediction to two distinct networks: a co-authorship network and a protein--protein interaction network. We demonstrate that the novel features we derive from both the network topology and literature content help improve link prediction accuracy. We also analyze the factors involved in establishing a new link and recurrent connections

Modelling and inferring connections in complex networks

Network phenomena are of key importance in the majority of scientific disciplines. They motivate the desire to better understand the implications of interactions between connected entities. In the focus of this thesis are two of the most prominent tasks in the research of such phenomena: the modelling and the inference of connections within networks. In particular, I provide a systematic framework for using the topology and unifying characteristics of networks from fields as diverse as biology, sociology, and economics to predict and validate connections. I build on existing random graph models and node similarity measures, which I then employ in both unsupervised and supervised machine learning approaches. Furthermore, I present novel methods for identifying the statistically significant connections in network settings that involve multiple types of entities and connections — a crucial element of modelling, which most available methods fail to address. To demonstrate the potential of these new tools, I use them to filter networks that were constructed from large-scale noisy data generated by biological experiments as well as records of online social activity. Subsequently, I predict previously unobserved connections within these networks and evaluate the performance of the developed tools based on ground truth data. In further data sets without direct evidence for the connections in the network, a second, bipartite network serves as proxy for the analysis. Specifically, in an e-commerce setting I use connections between products and customers to deduce similarities between the products based on customer behaviour. In an analysis of high-throughput screening data on the other hand, I utilize relations between proteins and experimental conditions to identify potential functional affinities among the proteins. The findings presented here show that the computational prediction of connections can both help researchers gain a better understanding of costly large-scale data and guide further experimental design. The thesis demonstrates the potential of a network analytic approach to modelling and inference on multiple applications, such as the uncovering of possible privacy issues in the context of online social networking platforms and the optimization of drug development in cancer treatment

Graph Pattern Mining Techniques to Identify Potential Model Organisms

Recent advances in high throughput technologies have led to an increasing amount of rich and diverse biological data and related literature. Model organisms are classically selected as subjects for studying human disease based on their genotypic and phenotypic features. A significant problem with model organism identification is the determination of characteristic features related to biological processes that can provide insights into the mechanisms underlying diseases. These insights could have a positive impact on the diagnosis and management of diseases and the development of therapeutic drugs. The increased availability of biological data presents an opportunity to develop data mining methods that can address these challenges and help scientists formulate and test data-driven hypotheses. In this dissertation, data mining methods were developed to provide a quantitative approach for the identification of potential model organisms based on underlying features that may be correlated with disease manifestation in humans. The work encompassed three major types of contributions that aimed to address challenges related to inferring information from biological data available from a range of sources. First, new statistical models and algorithms for graph pattern mining were developed and tested on diverse genres of data (biological networks, drug chemical compounds, and text documents). Second, data mining techniques were developed and shown to identify characteristic disease patterns (disease fingerprints), predict potentially new genetic pathways, and facilitate the assessment of organisms as potential disease models. Third, a methodology was developed that combined the application of graph-based models with information derived from natural language processing methods to identify statistically significant patterns in biomedical text. Together, the approaches developed for this dissertation show promise for summarizing the information about biological processes and phenomena associated with organisms broadly and for the potential assessment of their suitability to study human diseases

Ανάλυση προωτεομικών δεδομένων απο φασματομετρία μάζας και ενσωμάτωσή τους με άλλα κλινικά και μοριακά δεδομένα σε κλινικά δείγματα και καρκινικές σειρές

Οι μοριακοί υπότυποι μιας ασθένειας συχνά συσχετίζονται με διαφορές ως προς την επιβίωση ή πρόοδο της νόσου και άλλοτε ως προς την απόκριση σε συγκεκριμένη θεραπεία. Την τελευταία δεκαετία, μελέτες μοριακής ταξινόμησης του ουροθηλιακού καρκίνου εστιάζουν κυρίως στον διηθητικό τύπο της ασθένειας (~20% των ασθένων στην αρχική διάγνωση) ο οποίος χαρακτηρίζεται από υψηλό κίνδυνο για μετάσταση και χαμηλά ποσοστά πενταετούς επιβίωσης. Οι παραπάνω μελέτες επέτρεψαν την ταυτοποιήση πολλαπλών γενομικών και μεταγραφικών υποτύπων οι οποίοι διαφέρουν ριζικά ως προς το μοριακό τους προφίλ, σχηματίζοντας δύο μεγάλες κατηγορίες: τους basal και τους luminal όγκους. Οι πρώτοι φαίνεται να σχετίζονται με πιο επιθετικούς καρκίνους εμπερικλείοντας όμως ένα σημαντικό ποσοστό ασθενών που ανταποκρίνονται στο βασικό χημειοθεραπευτικό σχήμα. Οι δέυτεροι (luminal) αρχικά προσδιορίστηκαν ως λιγότερο επιθετικοί, επόμενες μελέτες όμως αποκάλυψαν την σημαντική μοριακή ετερογένεια που τους χαρακτηρίζει και που αντανακλάται σε κλινικές παραμέτρους. Σήμερα, πιστέυεται ότι ο διηθητικός καρκίνος της ουροδόχου κύστης ταξινομείται σε 6 βασικούς υποτύπους, αλλά τα δεδομένα που υπάρχουν για να υποστηρίξουν την ένταξη των υποτύπων στην κλινική πράξη είναι ατελή και δεν συμφωνούν μεταξύ τους. Από την άλλη, ο μη διηθητικός τύπος της ασθενεις (~80% των περιπτώσεων στην αρχική διάγνωση) χαρακτηρίζεται από υψηλά ποσοστά υποτροπής και προόδου σε ανώτερο στάδιο καθώς και από σημαντικό δημόσιο οικονομικό κόστος εξαιτίας της αυξημένης συχνότητας παρακολούθησης που απαιτεί. Το μοριακό προφίλ του μη-διηθητικού καρκίνου έχει μελετηθεί σημαντικά λιγότερο από αυτό του διηθητικού, και μέχρι σήμερα υπάρχουν δύο μελέτες που επιχειρούν την ταξινόμησή του σε μοριακούς υποτύπους: η πρώτη στη βάση του μεταγραφώματος, η δέυτερη στη βάση της διακύμνασης αριθμού αντιγράφων. Το πρωτεομικό προφίλ όμως, τόσο του διηθητικού όσο και του μη-διηθητικού καρκίνου της ουροδόχου κύστης, μέχρι και σήμερα έχει μελετηθεί υποτυπωδώς. Σκοπός της παρούσας μελέτης είναι η διερεύνηση της ύπαρξης πρωτεομικών υποτύπων του μη διηθητικού ουροθηλιακού καρκίνου, ο μοριακός χαρακτηρισμός τους, η σχέση τους με προηγούμενα συστήματα ταξινόμησης, καθώς και η ταυτοποίηση απορυθμισμένων πρωτεϊνών και μονοπατιών με δυνητική προγνωστική αξία. Για την εξυπηρέτηση του παραπάνω σκοπού, 117 δείγματα καρκινικού ιστού από ασθενείς που πρωτοδιαγνώσθηκαν με ουροθηλιακό καρκίνο (98 μη-διηθητικό, 19 διηθητικό) συλλέχθησαν και το ολικό πρωτέομά τους απομονώθηκε και αρχικά ποσοτικοποιήθηκε με τη μέθοδο Bradford. Κατόπιν διάσπασης με θρυψίνη, τα πεπτίδια διαχωρίστηκαν σε χρωματογραφική στήλη συνδεδεμένη με φασματογράφο μάζας τύπου Orbitrap. Οι φασματικές πληροφορίες για τα πεπτίδια αναλύθηκαν με το πρόγραμμα Proteome Discoverer θέτοντας FDR (False Discovery Rate) <0.01 και αντιστοιχήθηκαν σε πρωτεινικές ταυτότητες. Η πρωτεϊνική ποσοτικοποίηση έγινε με τη χρήση των τριών πιο άφθονων και μοναδικών πεπτιδίων ανά πρωτεΐνη, ενώ κατόπιν επεξεργασίας τα πρωτεομικά δεδομένα υποβλήθηκαν σε μια σειρά από υπολογιστικές αναλύσεις: μη επιτηρούμενη k-means συσταδοποίηση, ανάλυση κύριων συνιστωσών, ανάλυση για στατιστική σημαντικόντητα πρωτεϊνών, πρωτεϊνικών μονοπατιών, βιολογικών λειτουργιών και γονιδιακής έκφρασης καθώς και στην μοντελοιποίηση ενός μοριακού ταξινομητή Radnom Forest. Μέγιστη σταθερότητα συσταδοποίησης επιτεύχηκε για κ = 3 ομάδες, υποδηλώνοντας την ύπαρξη τριών πρωτεομικών υποτύπων στα δεδομένα. Η ομάδα 1 ήταν η μικρότερη σε μέγεθος (17/98), περιείχε κυρίως καρκίνους υψηλού σταδίου, αλλοίωσης και ρίσκου και παρουσίασε ένα μοριακό φαινότυπο ανοσοδιήθησης με υψηλά επιπέδα των μεταγραφικών παραγόντων STAT1, STAT3 και SND1, καθώς και πρωτεϊνων της αντιγονοπαρουσίασης, υποδηλώνοντας ενεργή ανταλλαγή πληροφοριών μεταξύ του ανοσοποιητικού και των καρκινικών κυττάρων. Παράλληλα, χαρακτηρίζονταν απο υψηλότερες ποσότητες πρωτεϊνών που συμμετέχουν στο κυτταρικό κύκλο, και στη μετάδοση στρεσογόνων σημάτων (αντίδραση μη αναδιπλωμένης πρωτεϊνης και επιδιόρθωση βλαβών του DNA). Η όμαδα 2 συγκέντρωσε ασθενείς με ποικίλα κλινικά χαρακτηριστικά που όμως έφεραν κοινώς, αυξημένες ποσότητες εξωκυττάριων πρωτεϊνών (στρώματος), και χαμηλά επιθηλιακά σήματα. Οι ασθενείς στην ομάδα 3 παρουσίασαν έναν πιο διαφοροποιημένο μοριακό φαινότυπο με υψηλότερα επίπεδα (UPKs και KRT20 κάθως και CDH1) που συμβαδίζει με τα κλινικά χαρακτηριστικά τους αφού οι περισσότεροι διαγιγνώσθηκαν με καρκίνους χαμηλού σταδίου και κινδύνου. Η ανάλυση για ενεργοποιημένα πρωτεϊνικά μονοπάτια έδειξε ότι οι ασθενείς της ομάδας 1 έιχαν ενεργή σηματοδότηση για βιοσυνθετικές διεργασίες, για ιντερφερόνη-γ, και αυξημένη δραστηριότητα των μεταγραφικών παραγόντων MYC και E2F, που ελέγχουν θετικά τον κυτταρικό κύκλο. Από την άλλη οι ασθνενείς της ομάδας 3 σχετίστηκαν με ενεργοποίηση μεταβολικών μονοπατιών όπως αυτό της αποτοξίνωσης μεσολαβούμενο από γλουταθειόνη καθώς και της γλυκογονόλυσης – γλυκόλυσης, αλλά και της απόπτωσης. Συγκρίνοντας το πρωτεομικό προφιλ των ασθένων με μη-διηθητικό καρκίνο με ασθενέις που είχαν διηθητικό καρκίνο χρησιμοποιώντας ανάλυση κύριων συνιστωσών, αποκαλύφθηκε κοντινή σχέση της ομάδας 1 με ασθενείς που έφεραν διηθητικό ουροθηλιακό καρκίνο και αντίστροφα, μακρινή σχέση της ομάδας 3 με τους τελευταίους. Η ομάδα 2 εμφάνισε μεγάλη διασπορά επικαλύπτοντας περιοχές των προηγούμενων δύο ομάδων. Για την επικύρωση των πρωτεομικών αποτελεσμάτων, δεδομένα από μεταγραφικές έρευνες (UROMOL και LUND) αναλύθηκαν αναδρομικά. Στην UROMOL έρευνα επίσης ταυτοποιήθηκαν 3 υπότυποι ο ένας εκ των οποίων συγκέντρωσε τους περισσότερους ασθενείς με πρόδοο σε ανώτερο στάδιο (κακής πρόγνωσης υπότυπος). Συγκριτική ανάλυση μεταξύ των τριών πρωτεομικών ομάδων και των τριών υποτύπων της UROMOL έρευνας με το στατιστικό εργαλείο GSEA, έδειξε στατιστικώς σημαντικές φαινοτυπικές ομοιότητες μεταξύ της πρωτεομικής ομάδας 1 και του υποτύπου «κακής» πρόγνωσης της UROMOL καθώς και μεταξύ της πρωτεομικής ομάδας 3 και του υποτύπου «καλής πρόγνωσης». Χρησιμοποιώντας έναν μη επιτηρούμενο μοριακό ταξινομητή Random Forest, οι υψηλού κινδύνου και χαμηλού κινδύνου φαινότυποι των πρωτεομικών ομάδων 1 και 3, επιβεβαιώθηκαν ύστερα από την ταξινόμηση των ασθενών στους υποτύπους «κακής» και «καλής» πρόγνωσης αντίστοιχα, της UROMOL έρευνας. Στατιστικώς σημαντικες πρωτεΐνες που ξεχωρίζουν αυτές τις δυο ακραίες πρωτεομικές ομάδες αλλά και ταυτόχρονα τον διηθητικό από τον μη διηθητικό καρκίνο βρέθηκαν να διαφέρουν σημαντικά και στο επίπεδο του μεταγραφώματος μεταξύ των ομάδων «κακής» και «καλής» πρόγνωσης σε δύο ανεξάρτητες έρευνες (UROMOL και LUND). Τα παραπάνω μόρια συμμετέχουν σε βιολογικές λειτουργίες-κλειδιά για την ανάπτυξη του μη-διηθητικού καρκίνου, όπως στην επαγωγή αποκρίσεων πρωτεϊνικής σταθερότητας, στη σηματοδότηση κυτοκινών και ιντερφερονών, στην αντιγονοπαρουσίαση, στην επεξεργασία πρώιμων mRNAs, σε μετα-μεταφραστικές τροποποιήσεις αλλά και σε μονοπάτια κυτταρικής αύξησης. Συνολικά, η παρούσα μελέτη ταυτοποιεί τρεις πρωτεομικούς υποτύπους του μη διηθητικού καρκίνου και ακολουθώντας μια σύγκριτική ανάλυση με δύο ανεξάρτητες μεταγραφικές έρευνες, παρέχει ομάδες μορίων που μπορεί να οδηγούν τη πρόοδο του καρκίνου και που χρειάζονται επιπλέον επικύρωση στη κλινική πράξη.DNA/RNA-based classification of Bladder Cancer (BC) supports the existence of multiple molecular subtypes, while investigations at the protein level are scarce. The purpose of this study was to investigate if Non-Muscle Invasive Bladder Cancer (NMIBC) can be stratified to biologically meaningful proteomic groups, to establish associations between the proteomics subtypes and previous transcriptomics classification systems and to characterize the continuum of transcriptomics alterations observed in the different stages of the disease. Subsequently, tissue specimens from 117 patients at primary diagnosis (98 with NMIBC and 19 with MIBC), were processed for high resolution LC-MS/MS analysis. Protein quantification was conducted by utilizing the mean abundance of the top three most abundant unique peptides per protein. The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA), and investigation of subtype-specific features, pathways, and genesets, as well as for the construction and validation of a Random Forest based classifier. NMIBC patients were optimally stratified to 3 proteomic subtypes (classes), differing at size, clinico-pathological and molecular backgrounds: Class 1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and expressed an immune/inflammatory phenotype, along with features involved in cell proliferation, unfolded protein response and DNA damage response, whereas class 2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile. Class 3 was rich in luminal/differentiation markers, in line with its pathological composition (mostly low stage/grade/risk samples). PCA revealed a close proximity of class 1 and conversely, remoteness of class 3 to the proteome of MIBC. Samples from class 2 were distributed in a wider fashion at the rotated space. Comparative analysis with GSEA between the three proteomic classes and the three UROMOL subtypes indicated statistically significant associations between the proteomics class 1 and UROMOL subtype 2 (subtype with a bad prognosis) and also between the proteomics class 3 and UROMOL subtype 1 (subtype with the best prognosis). Utilizing a Random Forest based classifier, the predicted high- and low-risk phenotypes for the proteomic class 1 and class 3, were further supported by their classification into the “progressed” and “non-progressed” subtypes of the UROMOL study, respectively. Statistically significant proteins distinguishing these two extreme classes (1 and 3) and also MIBC from NMIBC samples were found to consistently differ at the mRNA levels between NMIBC “Progressors” and “Non-Progressors” groups of the UROMOL and LUND cohorts. Functional assessment of the observed molecular de-regulations suggested severe pathway alterations at unfolded protein response, cytokine and inferferone-γ signaling, antigen presentation, mRNA processing, post translational modifications and in cell growth/division. Collectively, this study identifies three proteomic NMIBC subtypes and following a cross-omics analysis using transcriptomic data from two independent cohorts, shortlists molecular features potentially driving non-invasive carcinogenesis, meriting further validation in clinical trials

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality