Metabolomics for mitochondrial and cancer studies

AbstractMetabolomics, a high-throughput global metabolite analysis, is a burgeoning field, and in recent times has shown substantial evidence to support its emerging role in cancer diagnosis, cancer recurrence, and prognosis, as well as its impact in identifying novel cancer biomarkers and developing cancer therapeutics. Newly evolving advances in disease diagnostics and therapy will further facilitate future growth in the field of metabolomics, especially in cancer, where there is a dire need for sensitive and more affordable diagnostic tools and an urgency to develop effective therapies and identify reliable biomarkers to predict accurately the response to a therapy. Here, we review the application of metabolomics in cancer and mitochondrial studies and its role in enabling the understanding of altered metabolism and malignant transformation during cancer growth and metastasis. The recent developments in the area of metabolic flux analysis may help to close the gap between clinical metabolomics research and the development of cancer metabolome. In the era of personalized medicine with more and more patient specific targeted therapies being used, we need reliable, dynamic, faster, and yet sensitive biomarkers both to track the disease and to develop and evolve therapies during the course of treatment. Recent advances in metabolomics along with the novel strategies to analyze, understand, and construct the metabolic pathways opens this window of opportunity in a very cost-effective manner. This article is part of a Special Issue entitled: Bioenergetics of Cancer

A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling

Multiscale computational models integrating sub-cellular, cellular, and multicellular levels can be powerful tools that help researchers replicate, understand, and predict multicellular biological phenomena. To leverage their potential, these models need correct parameter values, which specify cellular physiology and affect multicellular outcomes. This work presents a robust parameter optimization method, utilizing a parallel and distributed genetic-algorithm software package. A genetic algorithm was chosen because of its superiority in fitting complex functions for which mathematical techniques are less suited. Searching for optimal parameters proceeds by comparing the multicellular behavior of a simulated system to that of a real biological system on the basis of features extracted from each which capture high-level, emergent multicellular outcomes. The goal is to find the set of parameters which minimizes discrepancy between the two sets of features. The method is first validated by demonstrating its effectiveness on synthetic data, then it is applied to calibrating a simple mechanical model of biofilm wrinkling, a common type of morphology observed in biofilms. Spatiotemporal convergence of cellular movement derived from experimental observations of different strains of Bacillus subtilis colonies is used as the basis of comparison

Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.

Neue bioinformatische und statistische Methoden für die Analyse von Massenspektrometrie-basierten phosphoproteomischen Daten

In living cells, reversible protein phosphorylation events propagate signals caused by external stimuli from the plasma membrane to their intracellular destinations. Aberrations in these signaling cascades can lead to diseases such as cancer. To identify and quantify phosphorylation events on a large scale, mass spectrometry (MS) has become the predominant technology. The large amount of data generated by MS requires efficient, tailor-made computational tools in order to draw meaningful biological conclusions. In this work, four new methods for analyzing MS-based phosphoproteomic data are presented. The first method, called SubExtractor, combines phosphoproteomic data with protein network information to identify differentially regulated subnetworks. The method is based on a Bayesian probabilistic model that accounts for information about both differential regulation and network topology, combined with a genetic algorithm and rigorous significance testing. The second method, called MeanRank test, is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. The test successfully deals with small numbers of replicates, missing values without the need of imputation, non-normally distributed expression levels, and non-identical distribution of up- and down-regulated features, while its statistical power scales well with the number of replicates. The third method is a biomarker discovery workflow that aims at identifying a multivariate response prediction biomarker for treatment of non-small cell lung cancer cell lines with the kinase inhibitor dasatinib from phosphoproteomic data (referred to as NSCLC biomarker). An elaborate biomarker workflow based on robust feature selection in combination with a support vector machine (SVM) was designed in order to find a phosphorylation signature that accurately predicts the response to dasatanib. The fourth method, called Pareto biomarker, extends the previous NSCLC biomarker workflow by optimizing not only one single objective (i.e. best possible separation of responders and non-responders), but also the objectives signature size and relevance (i.e. association of signature proteins with dasatinib’s main target). This is achieved by employing a multiobjective optimization algorithm based on the principle of Pareto optimality, which allows for a simultaneous optimization of all three objectives. These novel data analysis methods were thoroughly validated using experimental data and compared to existing methods. They can be used on their own, or they can be combined into a joint workflow in order to efficiently answer complex biological questions in the field of large-scale omics in general and phosphoproteomics in particular.In lebenden Zellen sind reversible Proteinphosphorylierungen für die Weiterleitung von Signalen externer Stimuli zu deren intrazellulären Bestimmungsorten verantwortlich. Anomalien in solchen Signaltransduktionswegen können zu Krankheiten wie beispielsweise Krebs führen. Um Phosphorylierungsstellen in großem Maßstab zu identifizieren und zu quantifizieren, hat sich die Massenspektrometrie (MS) zur vorherrschenden Technologie entwickelt. Die große Menge an Daten, die von Massenspektrometern generiert wird, erfordert effiziente maßgeschneiderte Computerprogramme, um aussagekräftige biologische Schlüsse ziehen zu können. In dieser Arbeit werden vier neue Methoden zur Analyse von MS-basierten phosphoproteomischen Daten präsentiert. Die erste Methode, genannt SubExtractor, kombiniert phosphoproteomische Daten mit Proteinnetzwerkinformationen um differentiell regulierte Subnetzwerke zu identifizieren. Die Methode basiert auf einem Bayesschen Wahrscheinlichkeitsmodell, das sowohl Information über die differentielle Regulation der Einzelknoten als auch die Netzwerktopologie berücksichtigt. Das Modell ist kombiniert mit einem genetischen Algorithmus und stringenter Signifikanzanalyse. Die zweite Methode, genannt MeanRank-Test, ist ein globaler Einstichproben-Lagetest, der auf den mittleren Rängen der Replikate beruht, und die False Discovery Rate implizit abschätzt und kontrolliert. Der Test eignet sich für die Anwendung auf Daten mit wenigen Replikate, fehlenden und nicht normalverteilten Werten, sowie nicht gleichverteilter Hoch- und Runterregulation. Gleichzeitig skaliert die Teststärke gut mit der Anzahl an Replikaten. Die dritte Methode ist ein Arbeitsablauf zur Biomarkeridentifizierung und hat zum Ziel, einen multivariaten Stratifikationsbiomarker aus phosphoproteomischen Daten zu extrahieren, der das Ansprechen von nichtkleinzelligen Bronchialkarzinomzelllinien auf den Kinaseinhibitor Dasatinib vorhersagt (bezeichnet als NSCLC-Biomarker). Dazu wurde ein ausführlicher Biomarkerarbeitsablauf basierend auf einer robusten Feature Selection in Kombination mit Support Vector Machine-Klassifizierung erstellt, um eine Phosphorylierungssignatur zu finden, die das Ansprechen auf Dasatinib richtig vorhersagt. Die vierte Methode, genannt Pareto-Biomarker, erweitert den vorherigen Biomarkerarbeitsablauf, indem nicht nur eine Zielfunktion (d.h. die bestmögliche Trennung von Respondern und Nichtrespondern) optimiert wird, sondern zusätzlich noch die Signaturgröße und Relevanz (d.h. die Verbindung der Signaturproteine mit dem Targetprotein von Dasatinib). Dies wird durch die Verwendung eines multiobjektiven Optimierungsalgorithmus erreicht, der auf dem Prinzip der Pareto-Optimalität beruht und die gleichzeitige Optimierung aller drei Zielfunktionen ermöglicht. Die hier präsentierten neuen Datenanalysemethoden wurden gründlich mittels experimenteller Daten validiert und mit bereits bestehenden Methoden verglichen. Sie können einzeln verwendet werden, oder man kann sie zu einem gemeinsamen Arbeitsablauf zusammenfügen, um komplexe biologische Fragestellungen in Omik-Gebieten im Allgemeinen und Phosphoproteomik im Speziellen zu beantworten

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process