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The ongoing coronavirus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has serious influences on human health and economy. The available clinical data suggest that patients infected by SARS-CoV-2 have the possibility of simultaneous infection of bacteria. In this study, we present a data-driven mathematical model for coinfection of SARS-CoV-2 and bacteria to investigate the dynamics of COVID-19 progress. Specifically, based on the statistical analysis of different clinical data from China and some other countries, a system model with ordinary differential equations (ODEs) in four variables, i.e. SARS-CoV-2, bacteria, neutrophils and lymphocytes, is established. We further validate our model through theoretical analysis and fitting of different clinical data. Moreover, through numerical simulations and bifurcation analysis, we find that bacterial infection and immune-related parameters in certain ranges lead to the system transitions among three steady states, i.e. mild, severe and death. We also analyzed the influence of the time it takes for patients to switch from a high-risk area to a low-risk area on the recovery time. These results reveal that the coinfection of viruses and bacteria can explain the changes in neutrophils and lymphocytes, and that initial bacterial infection and immune-related parameters have great influences on the severity degree and mortality in COVID-19 patients. Together, our model and quantitative analysis suggest that preventing bacterial infection and enhancing immune ability during the early phase of infections could be a potential treatment option for high-risk COVID-19 patients. [ABSTRACT FROM AUTHOR]

Single-cell RNA transcriptome data present a tremendous opportunity for studying the cellular heterogeneity. Identifying subpopulations based on scRNA-seq data is a hot topic in recent years, although many researchers have been focused on designing elegant computational methods for identifying new cell types; however, the performance of these methods is still unsatisfactory due to the high dimensionality, sparsity and noise of scRNA-seq data. In this study, we propose a new cell type detection method by learning a robust and accurate similarity matrix, named SCCLRR. The method simultaneously captures both global and local intrinsic properties of data based on a low rank representation (LRR) framework mathematical model. The integrated normalized Euclidean distance and cosine similarity are used to balance the intrinsic linear and nonlinear manifold of data in the local regularization term. To solve the non-convex optimization model, we present an iterative optimization procedure using the alternating direction method of multipliers (ADMM) algorithm. We evaluate the performance of the SCCLRR method on nine real scRNA-seq datasets and compare it with seven state-of-the-art methods. The simulation results show that the SCCLRR outperforms other methods and is robust and effective for clustering scRNA-seq data. (The code of SCCLRR is free available for academic https://github.com/wzhangwhu/SCCLRR). [ABSTRACT FROM AUTHOR]

Abstract: The interferon system provides a powerful and universal intracellular defense mechanism against viruses. As one part of their survival strategies, many viruses have evolved mechanisms to counteract the host type I interferon (IFN-α/β) responses. In this study, we attempt to investigate virus- and double-strand RNA (dsRNA)-triggered type I IFN signaling pathways and understand the inhibition of IFN-α/β induction by viral proteins using mathematical modeling and quantitative analysis. Based on available literature and our experimental data, we develop a mathematical model of virus- and dsRNA-triggered signaling pathways leading to type I IFN gene expression during the primary response, and use the genetic algorithm to optimize all rate constants in the model. The consistency between numerical simulation results and biological experimental data demonstrates that our model is reasonable. Further, we use the model to predict the following phenomena: (1) the dose-dependent inhibition by classical swine fever virus (CSFV) Npro or Erns protein is observed at a low dose and can reach a saturation above a certain dose, not an increase; (2) Erns and Npro have no synergic inhibitory effects on IFN-β induction; (3) the different characters in an important transcription factor, phosphorylated IRF3 (IRF3p), are exhibited because Npro or Erns counteracted dsRNA- and virus-triggered IFN-β induction by targeting the different molecules in the signaling pathways and (4) Npro inhibits the IFN-β expression not only by interacting with IFR3 but also by affecting its complex with MITA. Our approaches help to gain insight into system properties and rational therapy design, as well as to generate hypotheses for further research. [Copyright &y& Elsevier]

Abstract: Cells sense several kinds of stimuli and trigger corresponding responses through signaling pathways. As a result, cells must process and integrate multiple signals in parallel to maintain specificity and avoid erroneous cross-talk. In this study, we focus our theoretical effort on understanding specificity of a model network system in yeast, Saccharomyces cerevisiae, which contains three mitogen-activated protein kinase (MAPK) signal transduction cascades that share multiple signaling components. The cellular response to the pheromone, the filamentous growth and osmotic pressure stimuli in yeast is described and an integrative mathematical model for the three MAPK cascades is developed using available literature and experimental data. The theoretical framework for analyzing the specificity of signaling networks [Bardwell, L., Zou, X.F., Nie, Q., Komarova, N.L., 2007. Mathematical models of specificity in cell signaling. Biophys. J. 92, 3425–3441] is extended to include multiple interacting pathways with shared components. Simulations are also performed with any one stimulus, with any two simultaneous stimuli, and with the simultaneous application of the three stimuli. The interactions between the three pathways are systematically investigated. Moreover, the specificity and fidelity of this model system are calculated using our newly developed concept under different stimuli or with specific mutants. Our simulated and calculated results demonstrate that the yeast MAPK signaling network can achieve specificity and fidelity by filtering out spurious cross-talk between the relevant pathways through different mechanisms, such as scaffolding, cross-inhibiting, and feedback control. Proof that Pbs2 and Hog1 are essential for the maintenance of signaling specificity is presented. Our studies provide novel insights into integration of relevant signaling pathways in a biological system and the mechanisms conferring specificity in cellular signaling networks. [Copyright &y& Elsevier]

Different cellular signal transduction pathways are often interconnected, so that the potential for undesirable crosstalk between pathways exists. Nevertheless, signaling networks have evolved that maintain specificity from signal to cellular response. Here, we develop a framework for the analysis of networks containing two or more interconnected signaling pathways. We define two properties, specificity and fidelity, that all pathways in a network must possess in order to avoid paradoxical situations where one pathway activates another pathway's output, or responds to another pathway's input, more than its own. In unembellished networks that share components, it is impossible for all pathways to have both mutual specificity and mutual fidelity. However, inclusion of either of two related insulating mechanisms—compartmentalization or the action of a scaffold protein—allows both properties to be achieved, provided deactivation rates are fast compared to exchange rates. [ABSTRACT FROM AUTHOR]

• A new framework based on a dynamic optimization problem identifies the adaptive therapeutic schedule for prostate cancer. • A competitive model between drug-sensitive and drug-resistant cells is established and validated using available data. • The stability of equilibrium points and existence of a periodic solution indicate the feasibility of adaptive therapy. • Our therapeutic framework is superior to the current approaches in the total drug dose and the survival time. Adaptive therapy, a new therapeutic strategy, is increasingly applied in the treatment of cancer. According to the evolutionary game theory of cancer cells, adaptive therapy is understood as the dynamic regulation of cancer cells. However, it is a great challenge to identify the therapeutic period and dose in adaptive therapy. In this study, we establish a competitive model between drug-sensitive and drug-resistant cancer cells. Theoretical analysis, including the stability of equilibrium points and the existence of periodic solutions, validates the interpretability of the model. The available data for prostate cancers are used to identify the model parameters. Furthermore, we propose a new dynamic optimization problem with constraints to establish the adaptive therapeutic schedule for prostate cancer, and an optimal set of decisions in this optimization problem represents the therapeutic period and dose in adaptive therapy. Through numerical simulations and quantitative analysis, we compare our therapeutic schedule and current adaptive therapeutic schedules in patients with prostate cancer in three aspects: the total drug dose, the peak number of drug-resistant cancer cells and the survival time. These results demonstrate that our therapeutic framework is superior to the current adaptive therapeutic schedules. The proposed therapeutic framework could provide an identifiable adaptive therapy to achieve personal and precise treatment for prostate cancer. [ABSTRACT FROM AUTHOR]