Quantitative Biology

A semiempirical model, based in the logistic map, has been succesfully applied to forecast important quantities along the several phases of the outbreak of the covid-19 for different countries. This paper shows how the model was calibrated and applied to perform predictions of people needing to be hospitalized, needs of ventilators, or the number of deaths which would be produced. It is shown specifically the results obtained in the case of Spain, showing a prediction of diagnosed infected and deaths which will be observed after the ease of the total lockdown produced the 13th of March. Is also shown how this model can provide an insight of what the level of infection in the different regions of Spain is forecasted. The model predicts for Spain for the end of May more than 400,000 diagnosed infected cases, number which will be probably higher due to the change in the possibilities of performing massive number of tests to the general population. The number of forecasted deaths for that date is 46,000 +/- 15,000. The model also predicts the level of infection at the different Spanish regions, providing a counterintuitive result in the cases of Madrid and Catalonia as the result shows a higher the level of infection at Catalonia than the level at Madrid, according with this model. All of these results can be used to guide policy makers in order to optimize resources and to avoid future outbreaks of the covid-19.

Mechano-electric coupling (MEC) in atrial tissue has received sparse investigation to date, despite the well-known association between chronic atrial dilation and atrial fibrillation (AF). Of note, no fewer than six different mechanisms pertaining to stretch-activated channels, cellular capacitance and geometric effects have been identified in the literature as potential players. In this mini review, we briefly survey each of these pathways to MEC. We then perform computational simulations using single cell and tissue models in presence of various stretch regimes and MEC pathways. This allows us to assess the relative significance of each pathway in determining action potential duration, conduction velocity and rotor stability. For chronic atrial stretch, we find that stretch-induced alterations in membrane capacitance decrease conduction velocity and increase action potential duration, in agreement with experimental findings. In the presence of time-dependent passive atrial stretch, stretch-activated channels play the largest role, leading to after-depolarizations and rotor hypermeandering. These findings suggest that physiological atrial stretches, such as passive stretch during the atrial reservoir phase, may play an important part in the mechanisms of atrial arrhythmogenesis.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T therapy, including T-cell activation, T- and malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival analyses. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data lacking in current published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T therapies, as well as with the data collection essential for building such models.

Chimeric antigen receptor T cell therapy has demonstrated innovative therapeutic effectiveness in fighting cancers; however, it is extremely expensive due to the intrinsic patient-to-patient variability in cell manufacturing. We propose in this work a novel calibration-free statistical framework to effectively recover critical quality attributes under the patient-to-patient variability. Specifically, we model this variability via a patient-specific calibration parameter, and use readings from multiple biosensors to construct a patient-invariance statistic, thereby alleviating the effect of the calibration parameter. A carefully formulated optimization problem and an algorithmic framework are presented to find the best patient-invariance statistic and the model parameters. Using the patient-invariance statistic, we can recover the critical quality attribute of interest, free from the calibration parameter. We demonstrate improvements of the proposed calibration-free method in different simulation experiments. In the cell manufacturing case study, our method not only effectively recovers viable cell concentration for monitoring, but also reveals insights for the cell manufacturing process.

Larvae of Hermetia illucens, also commonly known as black soldier fly (BSF) have gained significant importance in the feed industry, primarily used as feed for aquaculture and other livestock farming. Mathematical model such as Von Bertalanffy growth model and dynamic energy budget models are available for modelling the growth of various organisms but have their demerits for their application to the growth and development of BSF. Also, such dynamic models were not yet applied to the growth of the BSF larvae despite models proven to be useful for automation of industrial production process (e.g. feeding, heating/cooling, ventilation, harvesting, etc.). This work primarily focuses on developing a model based on the principles of the afore mentioned models from literature that can provide accurate mathematical description of the dry mass changes throughout the life cycle and the transition of development phases of the larvae. To further improve the accuracy of these models, various factors affecting the growth and development such as temperature, feed quality, feeding rate, moisture content in feed, and airflow rate are developed and integrated into the dynamic growth model. An extensive set of data were aggregated from various literature and used for the model development, parameter estimation and validation. Models describing the environmental factors were individually validated based on the data sets collected. In addition, the dynamic growth model was also validated for dry mass evolution and development stage transition of larvae reared on different substrate feeding rates. The developed models with the estimated parameters performed well highlighting its application in decision-support systems and automation for large scale production.

COVID-19 has fast-paced drug re-positioning for its treatment. This work builds computational models for the same. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel tenofovir prodrug developed in order to improve renal safety when compared to the counterpart tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the selected drugs change as the SARS-Cov-2 mutates over time, suggesting the importance of such a tool in drug prediction. The dataset and software is available publicly at this https URL and the prediction tool with a user-friendly interface is available at this http URL.

State-space models are important tools for quality control of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system aids dynamic ocean management of human activities by informing when animals enter intensive use zones. This capability also facilitates use of ocean observations from animal-borne sensors in operational ocean forecasting models. Such near real-time data provision requires rapid, reliable quality control to deal with error-prone Argos locations. We formulate a continuous-time state-space model for the three types of Argos location data (Least-Squares, Kalman filter, and Kalman smoother), accounting for irregular timing of observations. Our model is deliberately simple to ensure speed and reliability for automated, near real-time quality control of Argos data. We validate the model by fitting to Argos data collected from 61 individuals across 7 marine vertebrates and compare model-estimated locations to GPS locations. Estimation accuracy varied among species with median Root Mean Squared Errors usually < 5 km and decreased with increasing data sampling rate and precision of Argos locations. Including a model parameter to inflate Argos error ellipse sizes resulted in more accurate location estimates. In some cases, the model appreciably improved the accuracy of the Argos Kalman smoother locations, which should not be possible if the smoother uses all available information. Our model provides quality-controlled locations from Argos Least-Squares or Kalman filter data with slightly better accuracy than Argos Kalman smoother data that are only available via reprocessing. Simplicity and ease of use make the model suitable both for automated quality control of near real-time Argos data and for manual use by researchers working with historical Argos data.

The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased alveolar gas exchange. Mechanical ventilation is a potentially lifesaving intervention to improve oxygen exchange but has the potential to cause ventilator-induced lung injury (VILI). A general strategy to reduce VILI is to use low tidal volume and low-pressure ventilation, but optimal ventilator settings for an individual patient are difficult for the bedside physician to determine and mortality from ARDS remains unacceptably high. Motivated by the need to minimize VILI, scientists have developed models of varying complexity to understand diseased pulmonary physiology. However, simple models often fail to capture real-world injury while complex models tend to not be estimable with clinical data, limiting the clinical utility of existing models. To address this gap, we present a physiologically anchored data-driven model to better model lung injury. Our approach relies on using clinically relevant features in the ventilator waveform data that contain information about pulmonary physiology, patients-ventilator interaction and ventilator settings. Our lung model can reproduce essential physiology and pathophysiology dynamics of differently damaged lungs for both controlled mouse model data and uncontrolled human ICU data. The estimated parameters values that are correlated with a known measure of lung physiology agree with the observed lung damage. In future endeavors, this model could be used to phenotype ventilator waveforms and serve as a basis for predicting the course of ARDS and improving patient care.

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.