Network Analysis and Modeling in Systems Biology

This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this thesis? They range from the simplest biological agents, the viruses, in this case the Potyvirus genus to prokariotes such as Escherichia coli and complex eukariotes (Arabidopsis thaliana, Nicotiana benthamiana). The second axis refers to which biological networks were studied. Those are protein-protein interaction (PPIN) and metabolic networks (MN). The final axis relates to the mathematical and modelling tools used to generate knowledge from those networks. These tools can be classify in three main branches: graph theory, constraint-based modelling and multivariate statistics. The document is structured in six parts. The first part states the justification for the thesis, exposes a general thesis roadmap and enumerates its main contributions. In the second part important literature is reviewed, summarized and integrated. From the birth and development of Systems Biology to one of its most popular branches: biological network analysis. Particular focus is put on PPIN and MN and their structure, representations and features. Finally a general overview of the mathematical tools used is presented. The third, fourth and fifth parts represent the central work of this thesis. They deal respectively with genotypephenotype interaction and classical network analysis, constraint-based modelling methods comparison and modelling metabolic networks and community structure. Finally, in the sixth part the main conclusions of the thesis are summarized and enumerated. This thesis highlights the vital importance of studying biological entities as systems and how powerful and promising this integrated analysis is. Particularly, network analysis becomes a fundamental avenue of research to gain insight into those biological systems and to extract, integrate and display this new information. It generates knowledge from just data.Esta tesis está dedicada al estudio y comprensión de redes biológicas a nivel molecular. Los objetivos fueron analizar su topología, integrar esta en un análisis de genotipo-fenotipo, desarrollar descripciones matemáticas más completas para ellas, estudiar su estructura de comunidades y comparar diferentes metodologías para estimar sus flujos internos. El trabajo presentado en este documento gira entorno a tres ejes principales. El primero es el biológico. ¿Qué organismos han sido estudiados en esta tesis? Estos van desde los agentes biológicos mas simples, los virus, en este caso el género Potyvirus, hasta procariotas como Escherichia coli y eucariotas complejos (Arabidopsis thaliana, Nicotiana benthamiana). El segundo eje hace referencia a las redes biológicas estudiadas, que fueron redes de interacción de proteínas (PPIN) y redes metabólicas (MN). El eje final es el de las herramientas matemáticas y de modelización empleadas para interrogar esas redes. Estas herramientas pueden clasificarse en tres grandes grupos: teoría de grafos, modelización basada en restricciones y estadística multivariante. Este documento está estructurado en seis partes. La primera expone la justificación para la tesis, muestra un mapa visual de la misma y enumera sus contribuciones principales. En la segunda parte, la bibliografía relevante es revisada y resumida. Desde el nacimiento y desarrollo de la Biología de Sistemas hasta una de sus ramas más populares: el análisis de redes biomoleculares. Especial interés es puesto en PPIN y MN: su estructura, representación y características. Finalmente, un resumen general de las herramientas matemáticas usadas es presentado. Los capítulos tercero, cuarto y quinto representan el cuerpo central de esta tesis. Estos tratan respectivamente sobre la interacción de genotipo-fenotipo y análisis topolólogico clásico de redes, modelos basados en restricciones y modelización de redes metabólicas y su estructura de comunidades. Finalmente, en la sexta parte las principales conclusiones de la tesis son resumidas y expuestas. Esta tesis pone énfasis en la vital importancia de estudiar los fenómenos biológicos como sistemas y en la potencia y prometedor futuro de este análisis integrativo. En concreto el análisis de redes supone un camino de investigación fundamental para obtener conocimiento sobre estos sistemas biológicos y para extraer y mostrar información sobre los mismos. Este análisis genera conocimiento partiendo únicamente desde datos.Aquesta tesi està dedicada a l'estudi i comprensió de xarxes biològiques a nivell molecular. Els objectius van ser analitzar la seva topologia, integrar aquesta en una anàlisi de genotip-fenotip, desenvolupar descripcions matemàtiques més completes per a elles, estudiar la seva estructura de comunitats o modularitat i comparar diferents metodologies per estimar els fluxos interns. El treball presentat en aquest document gira entorn de tres eixos principals. El primer és el biològic. ¿Què organismes han estat estudiats en aquesta tesi? Aquests van des dels agents biològics mes simples, els virus, en aquest cas el gènere Potyvirus, fins procariotes com Escherichia coli i eucariotes complexos (Arabidopsis thaliana, Nicotiana benthamiana). El segon eix fa referència a les xarxes biològiques estudiades, que van ser les xarxes d'interacció de proteïnes (PPIN) i les xarxes metabòliques (MN). L'eix final és el de les eines matemàtiques i de modelització emprades per interrogar aquestes xarxes. Aquestes eines poden classificarse en tres grans grups: teoria de grafs, modelització basada en restriccions i estadística multivariant. Aquest document està estructurat en sis parts. La primera exposa la justificació per a la tesi, mostra un mapa visual de la mateixa i enumera les seves contribucions principals. A la segona part, la bibliografia rellevant és revisada i resumida. Des del naixement i desenvolupament de la Biologia de Sistemes fins a una de les seves branques més populars: l'anàlisi de xarxes moleculars. Especial interès és posat en PPIN i MN: la seva estructura, representació i característiques. Finalment, un resum general de les eines matemàtiques utilitzades és presentat. Els capítols tercer, quart i cinquè representen el cos central d'aquesta tesi. Aquests tracten respectivament sobre la interacció de genotip-fenotip i anàlisi topolólogico clàssic de xarxes, models basats en restriccions i modelització de xarxes metabòliques i la seva estructura de comunitats. Finalment, en la sisena part les principals conclusions de la tesi són resumides i exposades. Aquesta tesi posa èmfasi en la vital importància d'estudiar els fenòmens biològics com sistemes i en la potència i prometedor futur d'aquesta anàlisi integratiu. En concret l'anàlisi de xarxes suposa un camí d'investigació fonamental per obtenir coneixement sobre aquests sistemes biològics i per extreure i mostrar informació sobre els mateixos. Aquest anàlisi genera coneixement partint únicament des de dades.Bosque Chacón, G. (2017). Network Analysis and Modeling in Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79082TESI

PFA toolbox: a MATLAB tool for Metabolic Flux Analysis

Background: Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. Results: Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User s Guide with a thorough description of its functions and several examples. Conclusions: The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.This research has been partially supported by the Spanish Government (FEDER-CICYT: DPI 2014-55276-C5-1-R). Yeimy Morales is grateful for the BR Grants of the University of Girona (BR2012/26). Gabriel Bosque Chacon is recipient of a doctoral fellowship from the Spanish Government (BES-2012-053772).Morales, Y.; Bosque Chacón, G.; Vehi, J.; Picó Marco, JA.; Llaneras, F. (2016). PFA toolbox: a MATLAB tool for Metabolic Flux Analysis. 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Interval and possibilistic methods for constraint-based metabolic models, PhD Thesis. Universidad Politécnica de Valencia: Departamento de Ingeniería de Sistemas y Automática; 2011.Llaneras F, Picó J. An interval approach for dealing with flux distributions and elementary modes activity patterns. J Theor Biol. 2007;246(2):290–308.Llaneras F, Sala A, Picó J. A possibilistic framework for constraint-based metabolic flux analysis. BMC Syst Biol. 2009;3(1):79.Tortajada M, Llaneras F, Picó J. Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity. BMC Syst Biol. 2010;4(1):115.Llaneras F, Picó J. A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient. BMC Bioinformatics. 2007;8(1):421.Iyer VV, Ovacik MA, Androulakis IP, Roth CM, Ierapetritou MG. Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes. Toxicology and applied pharmacology. 2010;248(3):165–77.Zamorano F, Wouwer A, Bastin G. Detailed metabolic flux analysis of an underdetermined network of CHO cells. J Biotechnol. 2010;150(4):497–508.Iyer V, Yang H, Ierapetritou M, Roth C. Effects of glucose and insulin on HepG2‐C3A cell metabolism. Biotechnol Bioeng. 2010;107(2):347–56.Iyer V, Androulakis I, Roth C, Ierapetritou M. Effects of Triadimefon on the Metabolism of Cultured Hepatocytes. In: BioInformatics and BioEngineering (BIBE), IEEE International Conference on. 2010. p. 118–23.Orman MA, Arai K, Yarmush ML, Androulakis IP, Berthiaume F, Ierapetritou MG. Metabolic flux determination in perfused livers by mass balance analysis: effect of fasting. Biotechnology and bioengineering. 2010;107(5):825–35.Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter H. FASIMU: flexible software for flux-balance computation series in large metabolic networks. 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The interplay of blood flow and temperature in regional hyperthermia: a mathematical approach

In recent decades, hyperthermia has been used to raise oxygenation levels in tumours undergoing other therapeutic modalities, of which radiotherapy is the most prominent one. It has been hypothesized that oxygenation increases would come from improved blood flow associated with vasodilation. However, no test has determined whether this is a relevant assumption or other mechanisms might be acting. Additionally, since hyperthermia and radiotherapy are not usually co-administered, the crucial question arises as to how temperature and perfusion in tumours will change during and after hyperthermia. Overall, it would seem necessary to find a research framework that clarifies the current knowledge, delimits the scope of the different effects and guides future research. Here, we propose a simple mathematical model to account for temperature and perfusion dynamics in brain tumours subjected to regional hyperthermia. Our results indicate that tumours in well-perfused organs like the brain might only reach therapeutic temperatures if their vasculature is highly disrupted. Furthermore, the characteristic times of return to normal temperature levels are markedly shorter than those required to deliver adjuvant radiotherapy. According to this, a mechanistic coupling of perfusion and temperature would not explain any major oxygenation boost in brain tumours immediately after hyperthermia

Synthesis, characterization, crystal structures and computational studies on novel cyrhetrenyl hydrazones

The synthesis of novel cyrhetrenyl hydrazones of general formula [Re{(η5-C5H4)single bondC(R1) = NNHR2}(CO)3] {with R1 = H and R2 = 4-NO2single bondC6H4 (4a), C6H5 (4b) or H (4c) or R1 = Me and R2 = 4-NO2single bondC6H4 (5a), C6H5 (5b) or H (5c)} is described. Compounds 4a-4c and 5a-5c were characterized by mass spectrometry and IR spectroscopy. 1H and 13C{1H} NMR studies revealed that 4a-4c and 5a-5c adopt the anti-(E) configuration in solution. X-ray crystal structures of compounds 4a and 5c confirmed the trans-arrangement of the cyrhetrenyl 'Re(η5-C5H4)(CO)3' and the -NHR2 moieties and the existence of strong hydrogen bonds involving the single bondNHsingle bond unit. Molecular Orbital calculations at a DFT level have also been carried out in order to rationalize the influence of the nature of the substituent R3 of [R3CH = NNH(4-NO2single bondC6H4)] (R3 = ferrocenyl, (3a), cyrhetrenyl (4a), phenyl (6a) or cymantrenyl (7a) on the electronic delocalization, the nucleophilicity of the imine carbon, the polarizability and hyperpolarizability of these compounds, and computational studies using time-dependent density functional (TD-DFT) calculations have also been carried out in order to assign the bands detected in their electronic spectra and to explain the effect produced by the solvent

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from F-18-fluorodeoxyglucose (F-18-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using F-18-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of F-18-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome

Bayesian spatial-temporal model for the diffusion of SARS-CoV2 in Brazilian municipalities

A COVID-19 provocou uma grave crise de proporções mundiais, sem precedentes nesse século (WHO, 2020). Alguns governos, o setor produtivo e a sociedade em geral buscam informações e soluções de curto prazo para enfrentar e mitigar os impactos causados pela pandemia (ANDERSON et al., 2020). Para o efetivo sucesso das ações de combate e mitigação, é necessário o entendimento da difusão da doença, tanto na escala temporal como espacial (SHINDE et al., 2020). Entretanto, existem três importantes lacunas para o rápido desenvolvimento de modelos acurados da difusão da Covid-19 no Brasil: (1) o acesso às bases de dados relevantes, (2) a identificação dos principais fatores de risco e (3) o uso de abordagens espaço-temporais para todos os municípios. Apesar da rápida multiplicação de modelos preditivos do crescimento do número de infectados, são incipientes as abordagens espaço-temporais para prever, no curto prazo, as regiões de maior risco. Propomos a modelagem da variação espaço-temporal de casos e óbitos de Covid-19 nos municípios brasileiros, utilizando inferência bayesiana

Topology analysis and visualization of Potyvirus protein-protein interaction network

Background: One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. Results: After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Conclusions: Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.This work was supported by the Spanish Ministerio de Economia y Competitividad grants BFU2012-30805 (to SFE), DPI2011-28112-C04-02 (to AF) and DPI2011-28112-C04-01 (to JP). The first two authors are recipients of fellowships from the Spanish Ministerio de Economia y Competitividad: BES-2012-053772 (to GB) and BES-2012-057812 (to AF-F).Bosque, G.; Folch Fortuny, A.; Picó Marco, JA.; Ferrer, A.; Elena Fito, SF. (2014). Topology analysis and visualization of Potyvirus protein-protein interaction network. 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