Reconstruction of Viral Variants via Monte Carlo Clustering.

Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.

Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations.

This article presents a novel scalable character-based phylogeny algorithm for dense viral sequencing data called SPHERE (Scalable PHylogEny with REcurrent mutations). The algorithm is based on an evolutionary model where recurrent mutations are allowed, but backward mutations are prohibited. The algorithm creates rooted character-based phylogeny trees, wherein all leaves and internal nodes are labeled by observed taxa. We show that SPHERE phylogeny is more stable than Nextstrain's, and that it accurately infers known transmission links from the early pandemic. SPHERE is a fast algorithm that can process >200,000 sequences in <2 hours, which offers a compact phylogenetic visualization of Global Initiative on Sharing All Influenza Data (GISAID).

Simulation of the effects of moderate stimulation/inhibition of the ß1-adrenergic signaling system and its components in mouse ventricular myocytes.

The ß1-adrenergic signaling system is one of the most important protein signaling systems in cardiac cells. It regulates cardiac action potential duration, intracellular Ca(2+) concentration ([Ca(2+)]i) transients, and contraction force. In this paper, a comprehensive experimentally based mathematical model of the ß1-adrenergic signaling system for mouse ventricular myocytes is explored to simulate the effects of moderate stimulations of ß1-adrenergic receptors (ß1-ARs) on the action potential, Ca(2+) and Na(+) dynamics, as well as the effects of inhibition of protein kinase A (PKA) and phosphodiesterase of type 4 (PDE4). Simulation results show that the action potential prolongations reach saturating values at relatively small concentrations of isoproterenol (∼0.01 µM), while the [Ca(2+)]i transient amplitude saturates at significantly larger concentrations (∼0.1-1.0 µM). The differences in the response of Ca(2+) and Na(+) fluxes to moderate stimulation of ß1-ARs are also observed. Sensitivity analysis of the mathematical model is performed and the model limitations are discussed. The investigated model reproduces most of the experimentally observed effects of moderate stimulation of ß1-ARs, PKA, and PDE4 inhibition on the L-type Ca(2+) current, [Ca(2+)]i transients, and the sarcoplasmic reticulum Ca(2+) load and makes testable predictions for the action potential duration and [Ca(2+)]i transients as functions of isoproterenol concentration.