Global dynamic optimization approach to predict activation in metabolic pathways

[Background] During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework.[Results] In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results.[Conclusions] The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.This research received financial support from the Spanish Ministerio de Economía y Competitividad (and the FEDER) through the project “MultiScales” (DPI2011-28112-C04-03), and from the CSIC intramural project “BioREDES” (PIE-201170E018). Gundián M. de Hijas Liste acknowledges financial support from the MICINN-FPI programmePeer Reviewe

Etablierung des E2F1-Interaktoms metastasierungsrelevanter Faktoren durch Integration bioinformatischer und experimenteller Methoden

In dieser Arbeit wurde durch intensive Literatur- und Datenbankrecherche ein Protein-Protein/Gen-Interaktionsnetzwerk um den Transkriptionsfaktor E2F1 herum erstellt. Er ist Schlüsselfaktor für die epithelial-mesenchymale Transition (EMT), Voraussetzung für die Metastasierung. Eine anschließende bioinformatische Analyse identifizierte tumorspezifische Signaturen der E2F1-vermittelten EMT, welche experimentell und anhand von Patientendaten validiert wurden. Gemeinsame Zielgene des näher untersuchten E2F1-TGFβ-Interaktoms bieten mögliche Therapieziele für Krebspatienten.By intensive literature and database research we constructed a comprehensive map of interactions around the transcription factor E2F1, a key driver of the epithelial-mesenchymal transition (EMT) as a prerequisite for metastasis. The subsequent bioinformatics analysis of this map lead to the identification of tumour-specific molecular signatures of E2F1-driven EMT. These signatures were validated experimentally as well as on patient data. Common transcriptional targets of the investigated E2F1-TGFβ co-regulome might be suitable therapeutic targets for cancer patients

Contribution to the development of efficient algorithms for solving complex single-objective and multi-objective optimization models

L’optimització en enginyeria de processos és un àrea molt estesa que ha anat evolucionant al llarg del temps i ha passat de ser una metodologia d'interès purament acadèmic a una tecnologia que té, i que contínua tenint, gran impacte en la indústria. En aquesta tesi ens hem centrat en el desenvolupament mètodes basats en dues eines típiques d'optimització: programació matemàtica i metaheurístiques. Els objectius d'aquesta tesi són: el primer és desenvolupar una metaheuristica híbrida per a l'optimització del disseny de cadenes de subministrament, d'un sol objectiu (cost o benefici), on tots els paràmetres són coneguts a priori; el segon és desenvolupar un algorisme efectiu per a reducció d'objectius facilitant la resolució de problemes multi-objectiu; i finalment s'han implementat una sèrie de millores en el mètode de la restricció èpsilon per millorar l'eficiència en la resolució de problemes multi-objectiu. Tots els algorismes presentats han estat comparats i avaluats amb els mètodes establerts per la literatura.La optimización en ingeniería de procesos es un área muy extensa que ha ido evolucionando a lo largo del tiempo y ha pasado de ser una metodología de interés puramente académico a una tecnología que tiene, y que continua teniendo, gran impacto en la industria. En esta tesis nos hemos centrado en el desarrollo de métodos basados en dos herramientas típicas de optimización: programación matemática y metaheurísticas. Los objetivos de esta tesis son: el primero es desarrollar una metaheuristica híbrida para la optimización del diseño de cadenas de suministro, de un solo objetivo (coste o beneficio), donde todos los parámetros son conocidos a priori; el segundo es desarrollar un algoritmo efectivo para la reducción de objetivos facilitando la resolución de problemas multi-objetivo; y finalmente se han implementado una serie de mejoras en el método de la restricción epsilon para mejorar la eficiencia en la resolución de problemas multi-objetivo. Todos los algoritmos presentados han sido comparados y evaluados con los métodos establecidos por la literatura.Optimization has become a major area in process systems engineering. It has evolved from a methodology of academic interest into a technology that has and continues to make significant impact in industry. In this thesis we have focused on development of tools based on two standard optimization methods: mathematical programming and metaheuristics. The objectives of this thesis are: firstly, the development of a hybrid metaheuristic for optimizing the design of supply chains, single objective (cost or benefit), where all parameters are known previously; secondly, the development of an effective algorithm for objective reduction facilitating the resolution of multi-objective problems; and finally, we improved the epsilon-constraint algorithm in multi-objective optimization. All the algorithms presented have been assessed with the methods established in the literature