Quantitative Biology

regulation largely unexplored, in part due to methodological limitations. Indeed, we review evidence demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. We review such methods, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. We argue that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.

In the current study, the authors demonstrate the method aimed at analyzing the distribution of acute coronary syndrome (ACS) cases in Saint Petersburg using the synthetic population approach and a statistical model for arterial hypertension prevalence. The cumulative number of emergency services calls in a separate geographical area (a grid cell of a map) associated with ACS is matched with the assessed number of dwellers and individuals with arterial hypertension, which makes it possible to find locations with excessive ACS incidence. The proposed method is implemented in Python programming language, the visualization results are shown using QGIS open software. Three categories of locations are proposed based on the analysis results. The demonstrated method might be applied for using the statistical assessments of hidden health conditions in the population to categorize spatial distributions of their visible consequences.

The life of a cell is governed by highly dynamical microscopic processes. Two notable examples are the diffusion of membrane receptors and the kinetics of transcription factors governing the rates of gene expression. Different fluorescence imaging techniques have emerged to study molecular dynamics. Among them, fluorescence correlation spectroscopy (FCS) and single-particle tracking (SPT) have proven to be instrumental to our understanding of cell dynamics and function. The analysis of SPT and FCS is an ongoing effort, and despite decades of work, much progress remains to be done. In this paper, we give a quick overview of the existing techniques used to analyze anomalous diffusion in cells and propose a collaborative challenge to foster the development of state-of-the-art analysis algorithms. We propose to provide labelled (training) and unlabelled (evaluation) simulated data to competitors all over the world in an open and fair challenge. The goal is to offer unified data benchmarks based on biologically-relevant metrics in order to compare the diffusion analysis software available for the community.

Recently, Antimicrobial peptides (AMPs) have been an area of interest in the researches, as the first line of defense against the bacteria. They are raising attention as an efficient way of fighting multidrug resistance. Discovering and identification of AMPs in the wet labs are challenging, expensive, and time-consuming. Therefore, using computational methods for AMP predictions have grown attention as they are more efficient approaches. In this paper, we developed a promising ensemble learning algorithm that integrates well-known learning models to predict AMPs. First, we extracted the optimal features from the physicochemical, evolutionary, and secondary structure properties of the peptide sequences. Our ensemble algorithm then trains the data using conventional algorithms. Finally, the proposed ensemble algorithm has improved the performance of the prediction by about 10% comparing to the traditional learning algorithms

mRNA vaccines are receiving increased interest as potential alternatives to conventional methods for the prevention of several diseases, including Covid-19. This paper proposes and evaluates three deep learning models (Long Short Term Memory networks, Gated Recurrent Unit networks, and Graph Convolutional Networks) as a method to predict the stability/reactivity and risk of degradation of sequences of RNA. These predictions can be very useful in the development of mRNA vaccines as they can reduce the number of sequences synthesized and tested by helping to identify the most promising candidates. Reasonably accurate results were able to be generated with the Graph Convolutional Network being the best predictor of reactivity (RMSE = 0.249) while the Gated Recurrent Unit Network was the best at predicting risks of degradation under various circumstances (RMSE = 0.266). Overall, combining all target variables, the GRU performed the best with an accuracy value of 76%. Results suggest feasibility of applying such methods in mRNA vaccine research in the near future.

Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of Aging (AIBL). We systematically explore different machine learning models, pre-processing methods and feature selection techniques. The most performant model demonstrates greater than 90% accuracy and recall in predicting AD, and the results generalize across sub-studies of ADNI and to the independent AIBL study. We also demonstrate that these results are robust to reducing the number of clinical visits or tests per visit. Using a metaclassification algorithm and longitudinal data analysis we are able to produce a "lean" diagnostic protocol with only 3 tests and 4 clinical visits that can predict Alzheimer's development with 87% accuracy and 79% recall. This novel work can be adapted into a practical early diagnostic tool for predicting the development of Alzheimer's that maximizes accuracy while minimizing the number of necessary diagnostic tests and clinical visits.

Optimal use and distribution of Covid-19 vaccines involves adjustments of dosing. Due to the rapidly-evolving pandemic, such adjustments often need to be introduced before full efficacy data are available. As demonstrated in other areas of drug development, quantitative systems pharmacology (QSP) is well placed to guide such extrapolation in a rational and timely manner. Here we propose for the first time how QSP can be applied real time in the context of COVID-19 vaccine development.

We have a large number of samples and we want to find the infected ones using as few number of tests as possible. We can use group testing which tells about a small group of people whether at least one of them is infected. Group testing is particularly efficient if the infection rate is low. The goal of this article is to summarize and extend the mathematical knowledge about the most efficient group testing algorithms, focusing on real-life applications instead of pure mathematical motivations and approaches.

We present a dual-stage neural network architecture for analyzing fine shape details from microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification for diagnostic and theragnostic use.

The disease trajectory for clinical sepsis, in terms of temporal cytokine and phenotypic dynamics, can be interpreted as a random dynamical system. The ability to make accurate predictions about patient state from clinical measurements has eluded the biomedical community, primarily due to the paucity of relevant and high-resolution data. We have utilized two distinct neural network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to take a time sequence of five measurements of eleven simulated serum cytokine concentrations as input and to return both the future cytokine trajectories as well as an aggregate metric representing the patient's state of health. The neural networks converged within 50 epochs for cytokine trajectory predictions and health-metric regressions, with the expected amount of error (due to stochasticity in the simulation). The mapping from a specific cytokine profile to a state-of-health is not unique, and increased levels of inflammation result in less accurate predictions. Due to the propagation of machine learning error combined with computational model stochasticity over time, the network should be re-grounded in reality daily as predictions can diverge from the true model trajectory as the system evolves towards a probabilistic basin of attraction. This work serves as a proof-of-concept for the use of artificial neural networks to predict disease progression in sepsis. This work is not intended to replace a trained clinician, rather the goal is to augment intuition with quantifiable statistical information to help them make the best decisions. We note that this relies on a valid computational model of the system in question as there does not exist sufficient data to inform a machine-learning trained, artificially intelligent, controller.