Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals

Knowledge Management approaches to model pathophysiological mechanisms and discover drug targets in Multiple Sclerosis

Multiple Sclerosis (MS) is one of the most prevalent neurodegenerative diseases for which a cure is not yet available. MS is a complex disease for numerous reasons; its etiology is unknown, the diagnosis is not exclusive, the disease course is unpredictable and therapeutic response varies from patient to patient. There are four established subtypes of MS, which are segregated based on different characteristics. Many environmental and genetic factors are considered to play a role in MS etiology, including viral infection, vitamin D deficiency, epigenetical changes and some genes. Despite the large body of diverse scientific knowledge, from laboratory findings to clinical trials, no integrated model which portrays the underlying mechanisms of the disease state of MS is available. Contemporary therapies only provide reduction in the severity of the disease, and there is an unmet need of efficient drugs. The present thesis provides a knowledge-based rationale to model MS disease mechanisms and identify potential drug candidates by using systems biology approaches. Systems biology is an emerging field which utilizes the computational methods to integrate datasets of various granularities and simulate the disease outcome. It provides a framework to model molecular dynamics with their precise interaction and contextual details. The proposed approaches were used to extract knowledge from literature by state of the art text mining technologies, integrate it with proprietary data using semantic platforms, and build different models (molecular interactions map, agent based models to simulate disease outcome, and MS disease progression model with respect to time). For better information representation, disease ontology was also developed and a methodology of automatic enrichment was derived. The models provide an insight into the disease, and several pathways were explored by combining the therapeutics and the disease-specific prescriptions. The approaches and models developed in this work resulted in the identification of novel drug candidates that are backed up by existing experimental and clinical knowledge

A matter of timing : A modelling-based investigation of the dynamic behaviour of reproductive hormones in girls and women

Hypothalamus-hypofyse-gonade aksen er en del av det kvinnelige endokrine systemet, og regulerer evnen til reproduksjon. Hormoner produsert og utskilt fra tre kjertler (hypotalamus, hypofysen, eggstokkene) påvirker hverandre via tilbakemeldingsinteraksjoner, som er nødvendige for å etablere en regelmessig menstruasjonssyklus hos kvinner. Matematiske modeller som forutsier utviklingen av slike hormonkonsentrasjoner og modning av eggstokkfollikler er nyttige verktøy for å forstå menstruasjonssyklusens dynamiske oppførsel. Slike modeller kan for eksempel hjelpe oss med å undersøke patologiske tilstander som endometriose og polycystisk ovariesyndrom. Videre kan de brukes til systematiske undersøkelser av effekten av medikamenter på det kvinnelige endokrine systemet. Derfor kan vi potensielt bruke slike menstruasjonsyklusmodeller som kliniske beslutningsstøttessystemer. Vi trenger modeller som forutsier hormonkonsentrasjoner sammen med modningen av eggstokkfollikler hos enkeltindivider gjennom påfølgende sykluser. Dette for å kunne simulere hormonelle behandlinger som stimulerer vekst av eggstokkfolliklene (eggstokkstimuleringsprotokoller). Her legger jeg fram et forslag til en matematisk menstruasjonsyklusmodell og viser modellens evne til å forutsi resultatet av eggstokkstimuleringsprotokoller. For å kalibrere denne typen modell trenges individuelle tidsseriedata. Innsamling av slike data er tidskrevende, og forutsetter høy grad av engasjement fra deltakerne i studien. Det er derfor viktig å finne brukbare datatyper som er mindre tid- og ressurskrevende å samle inn, og som likevel kan brukes til modellkalibrering. En type data som er enklere å samle inn er tversnittdata. I denne avhandlingen har jeg utviklet en prosedyre for å bruke tversnittpopulasjonsdata i modellens kalibreringsprosess, og viser hvordan en modell kalibrert med tversnittdata kan brukes til å forutsi individuelle resultater ved oppdatering av en del av modellens parametere. I tillegg til det vitenskapelige bidraget, håper jeg at avhandlingen min skaper oppmerksomhet rundt viktigheten av forskning på kvinners reproduktive helse, og at avhandlingen underbygger verdien av matematiske modeller i forskning på kvinnehelse.The hypothalamic-pituitary-gonadal axis (HPG axis), a part of the human endocrine system, regulates the female reproductive function. Feedback interactions between hormones secreted from the glands forming the HPG axis are essential for establishing a regular menstrual cycle. Mathematical models predicting the time evolution of hormone concentrations and the maturation of ovarian follicles are useful tools for understanding the dynamic behaviour of the menstrual cycle. Such models can, for example, help us to investigate pathological conditions, such as endometriosis or Polycystic Ovary Syndrome. Furthermore, they can be used to systematically study the effects of drugs on the endocrine system. In doing so, menstrual cycle models could potentially be integrated into clinical routines as clinical decision support systems. For the simulation-based investigation of hormonal treatments aiming to stimulate the growth of ovarian follicles (Controlled Ovarian Stimulation (COS)), we need models that predict hormone concentrations and the maturation of ovarian follicles in biological units throughout consecutive cycles. Here, I propose such a mechanistic menstrual cycle model. I also demonstrate its capability to predict the outcome of COS. Individual time series data is usually used to calibrate mechanistic models having clinical implications. Collecting these data, however, is time-consuming and requires a high commitment from study participants. Therefore, integrating different data sets into the model calibration process is of interest. One type of data that is often more feasible to collect than individual time series is cross-sectional data. As part of my thesis, I developed a workflow based on Bayesian updating to integrate cross-sectional data into the model calibration process. I demonstrate the workflow using a mechanistic model describing the time evolution of reproductive hormones during puberty in girls. Exemplary, I show that a model calibrated with cross-sectional data can be used to predict individual dynamics after updating a subset of model parameters. In addition to the scientific contributions of this thesis, I hope that it creates attention for the importance of research in the area of women's reproductive health and underpins the value of mathematical modelling for this field.Doktorgradsavhandlin

Bioinformatics: A Way Forward to Explore “Plant Omics”

Bioinformatics, a computer-assisted science aiming at managing a huge volume of genomic data, is an emerging discipline that combines the power of computers, mathematical algorithms, and statistical concepts to solve multiple genetic/biological puzzles. This science has progressed parallel to the evolution of genome-sequencing tools, for example, the next-generation sequencing technologies, that resulted in arranging and analyzing the genome-sequencing information of large genomes. Synergism of “plant omics” and bioinformatics set a firm foundation for deducing ancestral karyotype of multiple plant families, predicting genes, etc. Second, the huge genomic data can be assembled to acquire maximum information from a voluminous “omics” data. The science of bioinformatics is handicapped due to lack of appropriate computational procedures in assembling sequencing reads of the homologs occurring in complex genomes like cotton (2n = 4x = 52), wheat (2n = 6x = 42), etc., and shortage of multidisciplinary-oriented trained manpower. In addition, the rapid expansion of sequencing data restricts the potential of acquisitioning, storing, distributing, and analyzing the genomic information. In future, inventions of high-tech computational tools and skills together with improved biological expertise would provide better insight into the genomes, and this information would be helpful in sustaining crop productivities on this planet

Inferring Gene Regulatory Networks from Time Series Microarray Data

The innovations and improvements in high-throughput genomic technologies, such as DNA microarray, make it possible for biologists to simultaneously measure dependencies and regulations among genes on a genome-wide scale and provide us genetic information. An important objective of the functional genomics is to understand the controlling mechanism of the expression of these genes and encode the knowledge into gene regulatory network (GRN). To achieve this, computational and statistical algorithms are especially needed. Inference of GRN is a very challenging task for computational biologists because the degree of freedom of the parameters is redundant. Various computational approaches have been proposed for modeling gene regulatory networks, such as Boolean network, differential equations and Bayesian network. There is no so called golden method which can generally give us the best performance for any data set. The research goal is to improve inference accuracy and reduce computational complexity. One of the problems in reconstructing GRN is how to deal with the high dimensionality and short time course gene expression data. In this work, some existing inference algorithms are compared and the limitations lie in that they either suffer from low inference accuracy or computational complexity. To overcome such difficulties, a new approach based on state space model and Expectation-Maximization (EM) algorithms is proposed to model the dynamic system of gene regulation and infer gene regulatory networks. In our model, GRN is represented by a state space model that incorporates noises and has the ability to capture more various biological aspects, such as hidden or missing variables. An EM algorithm is used to estimate the parameters based on the given state space functions and the gene interaction matrix is derived by decomposing the observation matrix using singular value decomposition, and then it is used to infer GRN. The new model is validated using synthetic data sets before applying it to real biological data sets. The results reveal that the developed model can infer the gene regulatory networks from large scale gene expression data and significantly reduce the computational time complexity without losing much inference accuracy compared to dynamic Bayesian network

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Enhanced understanding of protein glycosylation in CHO cells through computational tools and experimentation

Chinese hamster ovary (CHO) cells are the workhorse of the multibillion-dollar biopharmaceuticals industry. They have been extensively harnessed for recombinant protein synthesis, as they exhibit high titres and human-like post translational modifications (PTM), such as protein N-linked glycosylation. More specifically, N-linked glycosylation is a crucial PTM that includes the addition of an oligosaccharide in the backbone of the protein and strongly affects therapeutic efficacy and immunogenicity. In addition, the Quality by Design (QbD) paradigm that is broadly applied in academic research, necessitates a comprehensive understanding of the underlying biological relationships between the process parameters and the product quality attributes. To that end, computational tools have been vastly employed to elucidate cellular functions and predict the effect of process parameters on cell growth, product synthesis and quality. This thesis reports several advancements in the use of mathematical models for describing and optimizing bioprocesses. Firstly, a kinetic mathematical model describing CHO cell growth, metabolism, antibody synthesis and N-linked glycosylation was proposed, in order to capture the effect of galactose and uridine supplementation on cell growth and monoclonal antibody (mAb) glycosylation. Subsequently, the model was utilized to optimize galactosylation, a desired quality attribute of therapeutic mAbs. Following the QbD paradigm for ensuring product titre and quality, the kinetic model was subsequently used to identify an in silico Design Space (DS) that was also experimentally verified. An elaborate parameter estimation methodology was also developed in order to adapt the existing model to data from a newly introduced CHO cell line, without altering model structure. In an effort to reduce the burden of parameter estimation, the N-linked glycosylation submodel was replaced with an artificial neural network that was used as a standalone machine learning algorithm to predict the effect of feeding alterations and genetic engineering on the glycan distribution of several therapeutic proteins. In addition, a hybrid model configuration (HyGlycoM) incorporating the ANN-glycosylation model was also formulated to link extracellular process conditions to glycan distribution. The latter was found to outperform its fully kinetic equivalent when compared to experimental data. Finally, a comprehensive investigation of mAb galactosylation bottlenecks was carried out. Five fed-batch experiments with different concentrations of galactose and uridine supplemented throughout the culturing period, were carried out and were found to present similar mAb galactosylation. In order to identify the bottlenecks that limit galactosylation, further experimental analysis, including the investigation of glycans microheterogeneity of CHO host cell proteins (HCPs), was conducted. The experimental results were used to parameterize a novel and significant extension of the kinetic glycosylation model that simultaneously describes the N-linked glycosylation of both HCPs and the mAb product. Flux balance analysis was also used to analyse carbon and nitrogen metabolism using the experimental amino acid concentration profiles. In addition to the expression levels of the beta-1,4-galactosyltransferase enzyme, constraints imposed by the transport of the galactosylation sugar donor in the Golgi compartments and the consumption of resources towards HCPs glycosylation, were found to considerably influence mAb galactosylation.Open Acces

Multiscale Modeling and Simulation of Human Heart Failure

Tesis por compendio[EN] Heart failure (HF) constitutes a major public health problem worldwide. Operationally it is defined as a clinical syndrome characterized by the marked and progressive inability of the ventricles to fill and generate adequate cardiac output to meet the demands of cellular metabolism that may have significant variability in its etiology and it is the final common pathway of various cardiac pathologies. Much attention has been paid to the understanding of the arrhythmogenic mechanisms induced by the structural, electrical, and metabolic remodeling of the failing heart. Due to the complexity of the electrophysiological changes that may occur during heart failure, the scientific literature is complex and sometimes equivocal. Nevertheless, a number of common features of failing hearts have been documented. At the cellular level, prolongation of the action potential (AP) involving ion channel remodeling and alterations in calcium handling have been established as the hallmark characteristics of myocytes isolated from failing hearts. At the tissue level, intercellular uncoupling and fibrosis are identified as major arrhythmogenic factors. In this Thesis a computational model for cellular heart failure was proposed using a modified version of Grandi et al. model for human ventricular action potential that incorporates the formulation of the late sodium current (INaL) in order to study the arrhythmogenic processes due to failing phenotype. Experimental data from several sources were used to validate the model. Due to extensive literature in the subject a sensitivity analysis was performed to assess the influence of main ionic currents and parameters upon most related biomarkers. In addition, multiscale simulations were carried out to characterize this pathology (transmural cardiac fibres and tissues). The proposed model for the human INaL and the electrophysiological remodeling of myocytes from failing hearts accurately reproduce experimental observations. An enhanced INaL appears to be an important contributor to the electrophysiological phenotype and to the dysregulation of calcium homeostasis of failing myocytes. Our strand simulation results illustrate how the presence of M cells and heterogeneous electrophysiological remodeling in the human failing ventricle modulate the dispersion of action potential duration (APD) and repolarization time (RT). Conduction velocity (CV) and the safety factor for conduction (SF) were also reduced by the progressive structural remodeling during heart failure. In our transmural ventricular tissue simulations, no reentry was observed in normal conditions or in the presence of HF ionic remodeling. However, defined amount of fibrosis and/or cellular uncoupling were sufficient to elicit reentrant activity. Under conditions where reentry was generated, HF electrophysiological remodeling did not alter the width of the vulnerable window (VW). However, intermediate fibrosis and cellular uncoupling significantly widened the VW. In conclusion, enhanced fibrosis in failing hearts, as well as reduced intercellular coupling, combine to increase electrophysiological gradients and reduce electrical propagation. In that sense, structural remodeling is a key factor in the genesis of vulnerability to reentry, mainly at intermediates levels of fibrosis and intercellular uncoupling.[ES] La insuficiencia cardíaca (IC) constituye un importante problema de salud pública en todo el mundo. Operacionalmente se define como un síndrome clínico caracterizado por la incapacidad marcada y progresiva de los ventrículos para llenar y generar gasto cardíaco adecuado para satisfacer las demandas del metabolismo celular, que puede tener una variabilidad significativa en su etiología y es la vía final común de varias patologías cardíacas. Se ha prestado mucha atención a la comprensión de los mecanismos arritmogénicos inducidos por la remodelación estructural, eléctrica, y metabólica del corazón afectado de IC. Debido a la complejidad de los cambios electrofisiológicos que pueden ocurrir durante la IC, la literatura científica es compleja y, a veces equívoca. Sin embargo, se han documentado una serie de características comunes en corazones afectados de IC. A nivel celular, se han establecido como las características distintivas de los miocitos aislados de corazones afectados de IC la prolongación del potencial de acción (PA), que implica la remodelación de los canales iónicos y las alteraciones en la dinámica del calcio. A nivel de los tejidos, el desacoplamiento intercelular y la fibrosis se identifican como los principales factores arritmogénicos. En esta tesis se propuso un modelo celular computacional para la insuficiencia cardíaca utilizando una versión modificada del modelo de potencial de acción ventricular humano de Grandi y colaboradores que incorpora la formulación de la corriente tardía de sodio (INaL) con el fin de estudiar los procesos arritmogénicas debido al fenotipo de la IC. Los datos experimentales de varias fuentes se utilizaron para validar el modelo. Debido a la extensa literatura en la temática se realizó un análisis de sensibilidad para evaluar la influencia de las principales corrientes iónicas y los parámetros sobre los biomarcadores relacionados. Además, se llevaron a cabo simulaciones multiescala para caracterizar esta patología (en fibras y tejidos transmurales). El modelo propuesto para la corriente tardía de sodio y la remodelación electrofisiológica de los miocitos de corazones afectados de IC reprodujeron con precisión las observaciones experimentales. Una INaL incrementada parece ser un importante contribuyente al fenotipo electrofisiológico y la desregulación de la homeostasis del calcio de los miocitos afectados de IC. Nuestros resultados de la simulaciones en fibra ilustran cómo la presencia de células M y el remodelado electrofisiológico heterogéneo en el ventrículo humano afectado de IC modulan la dispersión de la duración potencial de acción (DPA) y el tiempo de repolarización (TR). La velocidad de conducción (VC) y el factor de seguridad para la conducción (FS) también se redujeron en la remodelación estructural progresiva durante la insuficiencia cardíaca. En nuestras simulaciones transmurales de tejido ventricular, no se observó reentrada en condiciones normales o en presencia de la remodelación iónica de la IC. Sin embargo, determinadas cantidades de fibrosis y / o desacoplamiento celular eran suficientes para provocar la actividad reentrante. En condiciones donde se había generado la reentrada, el remodelado electrofisiológico de la IC no alteró la anchura de la ventana vulnerable (VV). Sin embargo, niveles intermedios de fibrosis y el desacoplamiento celular ampliaron significativamente la VV. En conclusión, niveles elevados de fibrosis en corazones afectados de IC, así como la reducción de acoplamiento intercelular, se combinan para aumentar los gradientes electrofisiológicos y reducir la propagación eléctrica. En ese sentido, la remodelación estructural es un factor clave en la génesis de la vulnerabilidad a las reentradas, principalmente en niveles intermedios de fibrosis y desacoplamiento intercelular. El remodelado electrofisiológico promueve la arritmogénesis y puede ser alterado dependi[CA] La insuficiència cardíaca (IC) constitueix un important problema de salut pública arreu del món. A efectes pràctics, es defineix com una síndrome clínica caracteritzada per la incapacitat marcada i progressiva dels ventricles per omplir i generar el cabal cardíac adequat, per tal de satisfer les demandes del metabolisme cel·lular, el qual pot tenir una variabilitat significativa en la seua etiologia i és la via final comuna de diverses patologies cardíaques. S'ha prestat molta atenció a la comprensió dels mecanismes aritmogènics induïts per la remodelació estructural, elèctrica, i metabòlica del cor afectat d'IC. A causa de la complexitat dels canvis electrofisiològics que poden ocórrer durant la IC, trobem que la literatura científica és complexa i, de vegades, equívoca. No obstant això, s'han documentat una sèrie de característiques comunes en cors afectats d'IC. A nivell cel·lular, com característiques distintives dels miòcits aïllats de cors afectats d'IC, s'han establert la prolongació del potencial d'acció (PA), que implica la remodelació dels canals iònics, i les alteracions en la dinàmica del calci. A nivell dels teixits, el desacoblament intercel·lular i la fibrosi s'identifiquen com els principals factors aritmogènics. Per tal d'estudiar els processos aritmogènics a causa del fenotip de la IC, es va proposar un model cel·lular computacional d'IC utilitzant una versió modificada del model de potencial d'acció ventricular humà de Grandi i els seus col·laboradors, el qual incorpora la formulació del corrent de sodi tardà (INaL). Amb l'objectiu de validar el model es van utilitzar dades experimentals de diverses fonts. A causa de l'extensa literatura en la temàtica, es va realitzar una anàlisi de sensibilitat per tal d'avaluar la influència de les principals corrents iòniques i els paràmetres sobre els biomarcadors relacionats. A més, es van dur a terme simulacions multiescala per a la caracterització d'aquesta patología (fibres i teixits transmurals). El model proposat per al corrent de sodi tardà i la remodelació electrofisiològica dels miòcits de cors afectats d'IC van reproduir amb precisió les observacions experimentals. Una INaL incrementada sembla contribuir de manera important al fenotip electrofisiològic i a la desregulació de l'homeòstasi del calci dels miòcits afectats d'IC. Els resultats de les nostres simulacions en fibra indiquen que la presència de cèl·lules M i el remodelat electrofisiològic heterogeni en el ventricle humà afectat d'IC modulen la dispersió de la durada del potencial d'acció (DPA) i el temps de repolarització (TR). La velocitat de conducció (VC) i el factor de seguretat per a la conducció (FS) també es van reduir en la remodelació estructural progressiva durant la IC. A les nostres simulacions transmurals de teixit ventricular, no s'observà cap reentrada ni en condicions normals ni en presència de la remodelació iònica de la IC. No obstant això, amb determinades quantitats de fibrosi i/o desacoblament cel·lular sí que es provocà l'activitat reentrant. I amb les condicions que produïren la reentrada, el remodelat electrofisiològic de la IC no va alterar l'amplada de la finestra vulnerable (FV). Tanmateix, nivells intermedis de fibrosi i el desacoblament cel·lular sí que ampliaren significativament la FV. En conclusió, nivells elevats de fibrosi en cors afectats d'IC, així com la reducció d'acoblament intercel·lular, es combinen per augmentar els gradients electrofisiològics i reduir la propagació elèctrica. Per tant, la remodelació estructural és un factor clau en la gènesi de la vulnerabilitat a les reentrades, principalment en nivells intermedis de fibrosi i desacoblament intercel·lular.Gómez García, JF. (2015). Multiscale Modeling and Simulation of Human Heart Failure [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/52389TESISCompendi