Modeling the Molecular Impact of SARS-CoV-2 Infection on the Renin-Angiotensin System.

SARS-CoV-2 infection is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The study of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding of the pathogenic mechanisms and risk factors associated with COVID-19 coronavirus disease and to design effective therapeutic strategies. In this context, we developed a mathematical model of RAS based on data regarding protein and peptide concentrations; the model was tested on clinical data from healthy normotensive and hypertensive individuals. We used our model to analyze the impact of SARS-CoV-2 infection on RAS, which we modeled through a downregulation of ACE2 as a function of viral load. We also used it to predict the effect of RAS-targeting drugs, such as RAS-blockers, human recombinant ACE2, and angiotensin 1-7 peptide, on COVID-19 patients; the model predicted an improvement of the clinical outcome for some drugs and a worsening for others. Our model and its predictions constitute a valuable framework for in silico testing of hypotheses about the COVID-19 pathogenic mechanisms and the effect of drugs aiming to restore RAS functionality.

Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0.

MOTIVATION: The rational design of proteins with modified properties, through amino acid substitutions, is of crucial importance in a large variety of applications. Given the huge number of possible substitutions, every protein engineering project would benefit strongly from the guidance of in silico methods able to predict rapidly, and with reasonable accuracy, the stability changes resulting from all possible mutations in a protein. RESULTS: We exploit newly developed statistical potentials, based on a formalism that highlights the coupling between four protein sequence and structure descriptors, and take into account the amino acid volume variation upon mutation. The stability change is expressed as a linear combination of these energy functions, whose proportionality coefficients vary with the solvent accessibility of the mutated residue and are identified with the help of a neural network. A correlation coefficient of R = 0.63 and a root mean square error of sigma(c) = 1.15 kcal/mol between measured and predicted stability changes are obtained upon cross-validation. These scores reach R = 0.79, and sigma(c) = 0.86 kcal/mol after exclusion of 10% outliers. The predictive power of our method is shown to be significantly higher than that of other programs described in the literature. AVAILABILITY: http://babylone.ulb.ac.be/popmusic

Modeling the temporal evolution of the Drosophila gene expression from DNA microarray time series.

The time evolution of gene expression across the developmental stages of the host organism can be inferred from appropriate DNA microarray time series. Modeling this evolution aims eventually at improving the understanding and prediction of the complex phenomena that are the basis of life. We focus on the embryonic-to-adult development phases of Drosophila melanogaster, and chose to model the expression network with the help of a system of differential equations with constant coefficients, which are nonlinear in the transcript concentrations but linear in their logarithms. To reduce the dimensionality of the problem, genes having similar expression profiles are grouped into 17 clusters. We show that a simple linear model is able to reproduce the experimental data with very good precision, owing to the large number of parameters that represent the connections between the clusters. Remarkably, the parameter reduction allowed elimination of up to 80-85% of these connections while keeping fairly good precision. This result supports the low-connectivity hypothesis of gene expression networks, with about three connections per cluster, without introducing a priori hypotheses. The core of the network shows a few gene clusters with negative self-regulation, and some highly connected clusters involving proteins with crucial functions.

Modeling slow-wave activity dynamics: does an exponentially dampened periodic function really fit a single night of normal human sleep?

OBJECTIVE: Slow-wave activity (SWA) is believed to be a fundamental measure of sleep homeostasis and is frequently characterized as an exponentially declining periodic dynamical system. The objective of this study is to carry out the first rigorous statistical test of this hypothesized dynamical behavior. METHODS: Delta power (DP) was computed for each epoch and artifacts were visually scored for 18 randomly selected nights from 18 healthy young men. Non-linear least-squares (LS) combined with the simplex algorithm were used to fit a 7-parameter confirmatory model of DP separately for each individual night of data. Individual night testing was employed because the model must apply to individual night data to be of research or clinical utility. RESULTS: Visually, results appeared satisfactory in half of the cases, though the model was never statistically verified. Validation using simulated data suggested that if the exponentially declining sinusoidal model were correct, satisfactory model fit would be expected on 17/18 nights. CONCLUSIONS: An exponentially dampened periodic function does not fit a single night of sleep amongst healthy young men. Historically, averaging across nights was the primary method used to develop such hypothesized model in order to reduce variability in the data. Our validation with simulated data established that this model does not fit individual night data because the data in an individual night do not conform to an exponentially dampened periodic function and not because of variability. SIGNIFICANCE: Further exploratory work is needed to determine how to optimally model single night SWA data.

Dynamic modeling of complex biological systems: a link between metabolic and macroscopic description.

In this study, a class of dynamic models based on metabolic reaction pathways is analyzed, showing that systems with complex intracellular reaction networks can be represented by macroscopic reactions relating extracellular components only. Based on rigorous assumptions, the model reduction procedure is systematic and allows an equivalent 'input-output' representation of the system to be derived. The procedure is illustrated with a few examples.