712 research outputs found

    Coupled biological and hydrological processes shape spatial food-web structures in riverine metacommunities

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    IntroductionUnderstanding how species are distributed in space and how they interact with each other is central for scientific and conservation purposes. Species' distributions and interactions result from a complex interplay of local trophic dynamics, dispersal processes, resource availability, and abiotic factors governed by the landscape matrix, which also determines the spatial connectivity for organisms' dispersal and resource fluxes. River networks not only exhibit universal spatial structures, but their dendritic landscape structure is tightly linked to species and metacommunity processes therein.MethodsHere, using a mechanistic model of spatially connected food webs integrating both essential biological and hydrological aspects, we investigate how food-web properties vary in space, and how these patterns are influenced by key model parameters. We then contrast our predictions with a suite of null models, where different aspects (such as spatial structure or trophic interactions) of the spatial food-web model are alternatively relaxed.ResultsWe find that species richness is highest in areas where local nutrient load is maximal (lowland headwaters, according to our default assumption). Overall, species richness is positively associated with link density, modularity and omnivory, and negatively related to connectance, nestedness, and niche overlap. However, for metrics such as connectance and omnivory, stochasticity of trophic interactions is a much stronger predictor than spatial variables such as distance to outlet and drainage area. Remarkably, relationships between species richness and food-web metrics do not generally hold in null models, and are hence the outcome of coupled biological and physical (i.e., hydrological) processes characteristic to river networks.DiscussionOur model generates realistic patterns of species richness and food-web properties, shows that no universal food-web patterns emerge as a result of the riverine landscape structure, and paves the way for future applications aimed at disentangling metacommunity dynamics in river networks

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Universal dynamics of biological pattern formation in spatio-temporal morphogen variations

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    In biological systems, chemical signals termed morphogens self-organize into patterns that are vital for many physiological processes. As observed by Turing in 1952, these patterns are in a state of continual development, and are usually transitioning from one pattern into another. How do cells robustly decode these spatio-temporal patterns into signals in the presence of confounding effects caused by unpredictable or heterogeneous environments? Here, we answer this question by developing a general theory of pattern formation in spatio-temporal variations of ‘pre-pattern’ morphogens, which determine gene-regulatory network parameters. Through mathematical analysis, we identify universal dynamical regimes that apply to wide classes of biological systems. We apply our theory to two paradigmatic pattern-forming systems, and predict that they are robust with respect to non-physiological morphogen variations. More broadly, our theoretical framework provides a general approach to classify the emergent dynamics of pattern-forming systems based on how the bifurcations in their governing equations are traversed

    Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

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    Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields

    Deep learning for intracellular particle tracking and motion analysis

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    Deep learning for intracellular particle tracking and motion analysis

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    Analysis of the physics of the musculoskeletal soft tissue

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    Les propietats lubricants del líquid sinovial són vitals per al moviment suau de l'articulació del genoll i una càrrega efectiva, assegurant una fricció mínima en condicions normals. No obstant això, diversos processos patològics poden provocar canvis en la composició física i química del líquid sinovial, reflectint la progressió de la lesió i el seu impacte en l'articulació. Aquesta tesi té com a objectiu simular el líquid sinovial sota diferents condicions de contorn i analitzar els moviments que ocorren dins de la cavitat sinovial. Per a aconseguir aquest objectiu i millorar la geometria, s'ha utilitzat el programari SALOME. Posteriorment, s'han definit les condicions de frontera i un perfil de velocitat mitjançant OpenFOAM. Les dades resultants s'han visualitzat i analitzat utilitzant ParaView. El flux del líquid s'ha examinat en quatre condicions de contorn diferents, cadascuna amb resultats diversos. Les observacions demostren que a mesura que disminueix la viscositat, les velocitats del líquid augmenten durant el moviment. En conseqüència, el líquid s'expulsa més ràpidament de l'àrea on es genera pressió a causa del contacte amb l'os. A més, la capa prima de líquid entre els dos cartílags disminueix, provocant un augment de la fricció entre ells. Aquestes anàlisis proporcionen informació sobre la complexa relació entre les propietats del líquid sinovial i la salut de l'articulació, oferint coneixements valuosos per a una major exploració i possibles enfocaments terapèutics.Las propiedades lubricantes del líquido sinovial son vitales para el movimiento suave de la articulación de la rodilla y para una carga efectiva, asegurando una fricción mínima en condiciones normales. Sin embargo, diversos procesos patológicos pueden provocar cambios en la composición física y química del líquido sinovial, reflejando la progresión de la enfermedad y su impacto en la articulación. Esta tesis tiene como objetivo simular el líquido sinovial bajo diferentes condiciones de salud y analizar los movimientos que ocurren dentro de la cavidad sinovial. Al fin de lograr este objetivo y mejorar la geometría, se utilizó el software SALOME. Posteriormente, se definieron las condiciones de frontera y un perfil de velocidad mediante OpenFOAM. Los datos resultantes se visualizaron y analizaron utilizando ParaView. El flujo del líquido se examinó en cuatro condiciones de contorno distintas, cada una con resultados diferentes. Las observaciones demuestran que a medida que disminuye la viscosidad, las velocidades del líquido aumentan durante el movimiento. En consecuencia, el líquido se expulsa más rápidamente del área donde se genera presión debido al contacto con el hueso. Además, la capa delgada de líquido entre los dos cartílagos disminuye, lo que provoca un aumento de la fricción entre ellos. Estos análisis brindan información sobre la compleja relación entre las propiedades del líquido sinovial y la salud de la articulación, ofreciendo conocimientos valiosos para una mayor exploración y posibles enfoques terapéuticos.The lubricating properties of synovial fluid are vital for smooth knee joint movement and effective load- bearing, ensuring minimal friction in normal conditions. However, various pathological processes can cause changes in the physical and chemical composition of synovial fluid, reflecting the progression of the disease and its impact on the joint. This thesis aims to simulate synovial fluid under different health conditions and analyse the movements occurring within the synovial cavity. To accomplish this, the geometry was made more realistic using SALOME software. Subsequently, boundary conditions and a velocity profile were defined through OpenFOAM. The resulting data were then visualized and analysed using ParaView. The fluid flow was examined across four distinct health conditions, each yielding different outcomes. The observations demonstrate that a decrease in viscosity leads to an increase in fluid velocities during movement. Consequently, fluid is expelled more rapidly from the area where pressure is generated due to bone contact. Additionally, the thin layer of liquid between the two cartilages decreases, causing an increase in friction between them. These findings provide insights into the intricate relationship between synovial fluid properties and joint health, offering valuable knowledge for further exploration and potential therapeutic approaches.Incomin

    Nonlinear Dynamic Analysis and Control of Chemical Processes Using Dynamic Operability

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    Nonlinear dynamic analysis serves an increasingly important role in process systems engineering research. Understanding the nonlinear dynamics from the mathematical model of a process helps to find the boundaries of all achievable process conditions and identify the system instabilities. The information on such boundaries is beneficial for optimizing the design and formulating a control structure. However, a systematic approach to analyzing nonlinear dynamics of chemical processes considering such boundaries in a quantifiable and adaptable way is yet to exist in the literature. The primary aim of this work is to formulate theoretical concepts for dynamic operability, as well as develop the practical implementation methods for the analysis of dynamic performance in chemical processes. Process operability is a powerful tool for analyzing the relationships between the input variables, the output variables, and the disturbances via the geometric computation of variable sets. The operability sets are described by unions of polyhedra, which can be translated to sets of inequality constraints, so the results of the operability analysis can be used for process optimization and advanced process control. Nonetheless, existing process operability approaches in the literature are currently limited for steady-state processes and a generalized definition of dynamic operability that retains the core principles of steady-state operability as a controllability measure. A unified dynamic operability concept is proposed in this dissertation with two different adaptations to represent the complex relationships between the design, control structure, and control law of a given process. The existing operability mapping methods discretize the input space by partitioning the ranges of each input variable evenly, and all possible input combinations are simulated to achieve the output sets. The procedure is repeated for each value in the expected disturbance set to find the output regions that are guaranteed to be achieved regardless of the disturbance scenario. However, for dynamic systems, the same set of manipulated inputs can take different values at different time intervals, so the number of possible input combinations, which is also the number of simulations required, increases exponentially with the number of time intervals. This tractability challenge motivates the development of novel dynamic operability mapping approaches. A linear time-invariant dynamic system is first considered to tackle the dynamic mapping of achievable output sets. For a linear system, the achievable output set (AOS) at a fixed predicted time is the smallest convex hull that contains all the images of the extreme points of the available input set (AIS) when propagated through the dynamic model. Given a collection of AOS’s at all predicted times, referred to as the achievable funnel, a set of output constraints is infeasible if its intersection with the achievable funnel is empty. Under the influence of a stochastic disturbance, the achievable funnel is shifted according to the definition of the expected disturbance set (EDS). If the EDS is bounded, the intersection of all achievable funnels at each disturbance realization is the tightest set of transient output constraints that is operable. Additionally, given a fixed setpoint, an AOS is referred to as a feasible AOS if a series of inputs from the AIS always brings any output to the setpoint regardless of the realization of the disturbance within the EDS. Thus, novel developed theories and algorithms to update the dynamic operability mapping according to the current state variables and the disturbance propagations are proposed to reduce the online computational time of the constraint calculation task. Dynamic operability mapping for nonlinear processes is an expansion of the above linear mapping. A novel state-space projection mapping is proposed by taking advantage of the discrete-time state-space structure of the dynamic model to reduce the number of input mapping combinations. This method augments the AIS at the current step to include the AOS of the state variables from the previous time step. The nonlinear dynamic operability mapping framework consists of three components: the AOS inspector, the AIS divider, and the merger of the AOS from the previous time with the AIS. Specifically, the AOS inspector evaluates if the current input-output combinations are approximately accurate to the real AOS when all input combinations are mapped to the output space. If the AOS inspector gauges that the current AOS is not sufficiently precise, the AIS divider systematically generates more input-output combinations based on the current AOS. This feedback process is repeated until an accuracy tolerance is reached. Finally, a novel grey-box model identification algorithm for process control is developed by integrating dynamic operability mapping and Bayesian calibration. The proposed dynamic discrepancy reduced-order model-based approach calibrates the rates of changes of the grey-box model to match the plant instead of compensating for the time-varying output differences. The model reduction framework is divided into three steps: formulating the dynamic discrepancy terms, calibrating the hyperparameters, and selecting the least complex model that is neither underfitted nor overfitted. To demonstrate the effectiveness of the reduced-order model, the developed approach is implemented into a model predictive controller for a high-fidelity model as the simulated plant

    Computational modeling of biological nanopores

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    Throughout our history, we, humans, have sought to better control and understand our environment. To this end, we have extended our natural senses with a host of sensors-tools that enable us to detect both the very large, such as the merging of two black holes at a distance of 1.3 billion light-years from Earth, and the very small, such as the identification of individual viral particles from a complex mixture. This dissertation is devoted to studying the physical mechanisms that govern a tiny, yet highly versatile sensor: the biological nanopore. Biological nanopores are protein molecules that form nanometer-sized apertures in lipid membranes. When an individual molecule passes through this aperture (i.e., "translocates"), the temporary disturbance of the ionic current caused by its passage reveals valuable information on its identity and properties. Despite this seemingly straightforward sensing principle, the complexity of the interactions between the nanopore and the translocating molecule implies that it is often very challenging to unambiguously link the changes in the ionic current with the precise physical phenomena that cause them. It is here that the computational methods employed in this dissertation have the potential to shine, as they are capable of modeling nearly all aspects of the sensing process with near atomistic precision. Beyond familiarizing the reader with the concepts and state-of-the-art of the nanopore field, the primary goals of this dissertation are fourfold: (1) Develop methodologies for accurate modeling of biological nanopores; (2) Investigate the equilibrium electrostatics of biological nanopores; (3) Elucidate the trapping behavior of a protein inside a biological nanopore; and (4) Mapping the transport properties of a biological nanopore. In the first results chapter of this thesis (Chapter 3), we used 3D equilibrium simulations [...]Comment: PhD thesis, 306 pages. Source code available at https://github.com/willemsk/phdthesis-tex
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