Biological mechanism and identifiability of a class of stationary conductance model for Voltage-gated Ion channels

The physiology of voltage gated ion channels is complex and insights into their gating mechanism is incomplete. Their function is best represented by Markov models with relatively large number of distinct states that are connected by thermodynamically feasible transitions. On the other hand, popular models such as the one of Hodgkin and Huxley have empirical assumptions that are generally unrealistic. Experimental protocols often dictate the number of states in proposed Markov models, thus creating disagreements between various observations on the same channel. Here we aim to propose a limit to the minimum number of states required to model ion channels by employing a paradigm to define stationary conductance in a class of ion-channels. A simple expression is generated using concepts in elementary thermodynamics applied to protein conformational transitions. Further, it matches well many published channel current-voltage characteristics and parameters of the model are found to be identifiable and easily determined from usual experimental protocols

Positive Feedback in the AKT/mTOR pathway and its implications for growth signal progression in skeletal muscle cells: An analytical study

The IGF-1 mediated AKT/mTOR pathway has been recently proposed as mediator of skeletal muscle growth and a positive feedback between Akt and mTOR was suggested to induce homogenous growth signals along the whole spatial extension of such long cells. Here we develop two biologically justied approximations which we study under the presence of four dierent initial conditions that describe dierent paradigms of IGF-1 receptor{induced Akt/mTOR activation. In rst scenario the activation of the feedback cascade was assumed to be mild or protein turnover considered to be high. In turn, in the second scenario the transcriptional regulation was assumed to maintain dened levels of inactive pro{enzymes. For both scenarios, we were able to obtain closed{form formulas for growth signal progression in time and space and found that a localised initial signal maintains its Gaussian shape, but gets delocalised and exponentially degraded. Importantly, mathematical treatment of the reaction diusion system revealed that diusion ltered out high frequencies of spatially periodic initiator signals suggesting that the muscle cell is robust against uctuations in spatial receptor expression or activation. However, neither scenario was consistent with the presence of stably travelling signal waves. Our study highlights the role of feedback loops in spatiotemporal signal progression and results can be applied to studies in cell proliferation, cell dierentiation and cell death in other spatially extended cells

A Slow Axon Antidromic Blockade Hypothesis for Tremor Reduction via Deep Brain Stimulation

Parkinsonian and essential tremor can often be effectively treated by deep brain stimulation. We propose a novel explanation for the mechanism by which this technique ameliorates tremor: a reduction of the delay in the relevant motor control loops via preferential antidromic blockade of slow axons. The antidromic blockade is preferential because the pulses more rapidly clear fast axons, and the distribution of axonal diameters, and therefore velocities, in the involved tracts, is sufficiently long-tailed to make this effect quite significant. The preferential blockade of slow axons, combined with gain adaptation, results in a reduction of the mean delay in the motor control loop, which serves to stabilize the feedback system, thus ameliorating tremor. This theory, without any tuning, accounts for several previously perplexing phenomena, and makes a variety of novel predictions

Control Predictivo basado en Modelo Neuroborroso de un Autoclave Industrial

XXVIII JORNADAS DE AUTOMÁTICA. 05/09/2007. HuelvaEn este artículo se presenta un modelo neuroborroso de la temperatura de un autoclave industrial, usado para estrategias basadas en Control Predictivo No-Lineal, permitiendo un bajo coste computacional, las cuales son aptas para implementarse en un autómata programable (PLC) de gama media, muy común en la industria. El modelo se ha validado con datos experimentales obtenidos en una planta real.Ministerio de Eduación y Ciencia DPI2004-07444-C04-0

Modelling and simulation of pathogen inactivation with sanitizers in a food washing tank operated continuously and with turbulent flow

[Resumen] Las autoridades de seguridad alimentaria en los últimos años están demandando avances en el modelado y simulación de la lavado de frutas y verduras que permitan diseñar modos de operación sostenible (mínimo consumo de agua) garantizando la seguridad final del alimento Se requiere, además, evitar que los residuos y patógenos se trasladen del agua de lavado al alimento (contaminación cruzada). En este estudio, se simula el proceso de lavado y desinfección en un tanque industrial en régimen turbulento. Para modelar el transporte de especies (desinfectante, patógenos y materia orgánica), se considera la ecuación de advección-difusión. Para describir la inactivación de los patógenos en el agua, se incluye el modelo de reacción describiendo las interacciones entre las distintas especies. Los resultados revelan que, aunque un incremento en las velocidades del flujo (aumento de la turbulencia) genera mayor homogeneidad en la distribución de desinfectante, el tiempo característico del proceso resulta insuficiente para inactivar eficazmente los patógenos en el agua.[Abstract] Food safety authorities in recent years are demanding advances in modeling and simulating the washing of fruits and vegetables to support the development of sustainable operation designs (minimum water use) and guaranteeing final product safety. It is required, moreover, to avoid the transference of residues and pathogens from the wash water to the food (cross-contamination). In this study, we simulate an industrial washing and disinfection process in a tank in a turbulent regime. We consider the advectiondiffusion equation to model the species (disinfectant, pathogens, and organic matter) transport, and we include the reaction model between the different species to describe pathogen inactivation in the wash water. The results reveal that, although increasing flow velocities (increased turbulence) generates greater homogeneity in the disinfectant distribution, the characteristic time of the process becomes insufficient to inactivate pathogens in the water.Consejo Superior de Investigaciones Científicas; 20213AT001Consejo Superior de Investigaciones Científicas; 202270I19

Combination of mechanism-based and data-based models for the prediction of quality in fresh fish

[Resumen] El desperdicio alimentario supone un gran problema de sostenibilidad. El pescado, en concreto, es un producto especialmente perecedero, degradándose con mucha rapidez cuando la temperatura de almacenamiento no se controla adecuadamente. Los modelos matemáticos son herramientas útiles para predecir la evolución de la calidad del pescado durante el transporte y almacenamiento. Conocer la dinámica de la degradación con antelación también nos permitirá definir estrategias para reducir el desperdicio alimentario y aumentar el valor añadido del pescado. En este trabajo se presenta una nueva metodología para la descripción de la evolución de calidad de filetes de merluza (Merluccius merluccius) envasada en atmósferas modificadas durante el transporte y almacenamiento. Dicha metodología consta de dos partes: un modelo basado en los mecanismos del proceso que describe la evolución de dos de los indicadores de calidad más utilizados (contenido bacteriano y bases volátiles), y un modelo de aprendizaje automático que permite correlacionar dichos indicadores con un indicador sensorial, el QIM (del inglés, Quality Index Method). Para la evolución de las bases volátiles se propone un nuevo modelo basado en la ley de potencias. Además, en este trabajo, los parámetros desconocidos del modelo se estiman utilizando datos experimentales. En el caso de los modelos de aprendizaje automático, se comparan técnicas de redes neuronales y árboles de decisión utilizando diferentes indicadores del error. El modelo combinado supone un avance en la predicción de calidad, ya que permite predecir el QIM a partir, exclusivamente, de datos de temperatura y obtener resultados satisfactorios.[Abstract] Food waste is a significant sustainability problem. Fish, in particular, is a very perishable product. In fact, spoilage increases rapidly when the storage temperature is not adequately controlled. Mathematical models are useful tools to predict fish quality evolution during transport and storage. This knowledge will help to develop strategies to reduce food waste and to increase the product value. In this work, we propose a new methodology to predict the evolution of quality, during transport and storage, of hake fillets packed in modified atmospheres. It consists of two parts: a model based on the process mechanisms to describe the evolution of two highly used quality indicators (bacterial content and volatile bases), and a machine learning model to correlate such indicators with one of the most employed sensory indexes: the Quality Index Method (QIM). Moreover, we propose a Power Law model to describe volatile bases evolution. We estimate the unknown parameters using experimental data. In the machine learning part, we compare random forest models and neural networks using several statistical techniques. Overall, this methodology is an improvement in fish quality prediction because it makes QIM prediction using just temperature data with promising results.Ministerio de Agricultura, Pesca y Alimentación; 202178002Ministerio de Ciencia e Innovación; PRTRC17.I

Cumulative signal transmission in nonlinear reaction-diffusion networks

Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network’s ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically. The derived time-integrals do not require knowledge of the solution of the reaction-diffusion equation, and we provide a simple graphical test to check if a given network belongs to the proposed class. The formulae for the time-integrals reveal how the kinetic parameters shape signal transmission in a network under spatiotemporal stimuli. We use these to show that a canonical complex-formation mechanism behaves as a spatial low-pass filter, the bandwidth of which is inversely proportional to the diffusion length of the ligand

Measurement of the shape of the Bs0→Ds∗−μ+νμ differential decay rate

The shape of the B0s→D∗−sμ+νμ differential decay rate is obtained as a function of the hadron recoil parameter using proton-proton collision data at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.7 fb−1 collected by the LHCb detector. The B0s→D∗−sμ+νμ decay is reconstructed through the decays D∗−s→D−sγ and D−s→K−K+π−. The differential decay rate is fitted with the Caprini-Lellouch-Neubert (CLN) and Boyd-Grinstein-Lebed (BGL) parametrisations of the form factors, and the relevant quantities for both are extracted

Observation of the semileptonic decay B+→ pp¯ μ+νμ

The Cabibbo-suppressed semileptonic decay B+→pp¯¯¯μ+νμ is observed for the first time using a sample of pp collisions corresponding to an integrated luminosity of 1.0, 2.0 and 1.7 fb−1 at centre-of-mass energies of 7, 8 and 13 TeV, respectively. The differential branching fraction is measured as a function of the pp¯¯¯ invariant mass using the decay mode B+ → J/ψK+ for normalisation. The total branching fraction is measured to be B(B+→pp¯¯¯μ+νμ)= (5.27+0.23−0.24±0.21±0.15)×10−6, where the first uncertainty is statistical, the second systematic and the third is from the uncertainty on the branching fraction of the normalisation channel