An agent-based modeling approach for lung fibrosis in response to COVID-19.

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-ß). The TGF-ß-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-ß sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-ß, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-ß, and the duration of TGF-ß sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-ß sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-ß, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-ß sources changed the fibrotic patterns; in the presence of persistent TGF-ß sources, partial removal of TGF-ß from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-ß sources may find applications in the current clinical trials targeting TGF-ß for the resolution of lung fibrosis.

High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx.

We performed a systematic study involving simulation and experimental techniques to develop induced-junction silicon photodetectors passivated with thermally grown SiO2 and plasma-enhanced chemical vapor deposited (PECVD) SiNx thin films that show a record high quantum efficiency. We investigated PECVD SiNx passivation and optimized the film deposition conditions to minimize the recombination losses at the silicon-dielectric interface as well as optical losses. Depositions with varied process parameters were carried out on test samples, followed by measurements of minority carrier lifetime, fixed charge density, and optical absorbance and reflectance. Subsequently, the surface recombination velocity, which is the limiting factor for internal quantum deficiency (IQD), was obtained for different film depositions via 2D simulations where the measured effective lifetime, fixed charge density, and substrate parameters were used as input. The quantum deficiency of induced-junction photodiodes that would be fabricated with a surface passivation of given characteristics was then estimated using improved 3D simulation models. A batch of induced-junction photodiodes was fabricated based on the passivation optimizations performed on test samples and predictions of simulations. Photodiodes passivated with PECVD SiNx film as well as with a stack of thermally grown SiO2 and PECVD SiNx films were fabricated. The photodiodes were assembled as light-trap detector with 7-reflections and their efficiency was tested with respect to a reference Predictable Quantum Efficient Detector (PQED) of known external quantum deficiency. The preliminary measurement results show that PQEDs based on our improved photodiodes passivated with stack of SiO2/SiNx have negligible quantum deficiencies with IQDs down to 1 ppm within 30 ppm measurement uncertainty.

Stability Analysis of a Bulk-Surface Reaction Model for Membrane Protein Clustering.

Protein aggregation on the plasma membrane (PM) is of critical importance to many cellular processes such as cell adhesion, endocytosis, fibrillar conformation, and vesicle transport. Lateral diffusion of protein aggregates or clusters on the surface of the PM plays an important role in governing their heterogeneous surface distribution. However, the stability behavior of the surface distribution of protein aggregates remains poorly understood. Therefore, understanding the spatial patterns that can emerge on the PM solely through protein-protein interaction, lateral diffusion, and feedback is an important step toward a complete description of the mechanisms behind protein clustering on the cell surface. In this work, we investigate the pattern formation of a reaction-diffusion model that describes the dynamics of a system of ligand-receptor complexes. The purely diffusive ligand in the cytosol can bind receptors in the PM and the resultant ligand-receptor complexes not only diffuse laterally but can also form clusters resulting in different oligomers. Finally, the largest oligomers recruit ligands from the cytosol using positive feedback. From a methodological viewpoint, we provide theoretical estimates for diffusion-driven instabilities of the protein aggregates based on the Turing mechanism. Our main result is a threshold phenomenon, in which a sufficiently high recruitment of ligands promotes the input of new monomeric components and consequently drives the formation of a single-patch spatially heterogeneous steady state.

On the application of a single-crystal κ-diffractometer and a CCD area detector for studies of thin films.

A multipurpose six-axis κ-diffractometer, together with the brilliance of the ESRF light source and a CCD area detector, has been explored for studying epitaxial relations and crystallinity in thin film systems. The geometrical flexibility of the six-axis goniometer allows measurement of a large volume in reciprocal space, providing an in-depth understanding of sample crystal relationships. By a set of examples of LaAlO3 thin films deposited by the atomic layer deposition technique, the possibilities of the set-up are presented. A fast panoramic scan provides determination of the crystal orientation matrices, prior to more thorough inspection of single Bragg nodes. Such information, in addition to a broadening analysis of families of single reflections, is shown to correlate well with the crystallinity, crystallite size, strain and epitaxial relationships in the thin films. The proposed set-up offers fast and easy sample mounting and alignment, along with crucial information on key features of the thin film structures.

An agent-based modeling approach for lung fibrosis in response to COVID-19.

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-ß). The TGF-ß-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-ß sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-ß, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-ß, and the duration of TGF-ß sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-ß sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-ß, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-ß sources changed the fibrotic patterns; in the presence of persistent TGF-ß sources, partial removal of TGF-ß from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-ß sources may find applications in the current clinical trials targeting TGF-ß for the resolution of lung fibrosis. Author summary: COVID-19 survivors are at risk of lung fibrosis as a long-term effect. Lung fibrosis is the excess deposition of tissue materials in the lung that hinder gas exchange and can collapse the whole organ. We identified TGF-ß as a critical regulator of fibrosis. We built a model to investigate the mechanisms of TGF-ß sources in the process of fibrosis. Our results showed spatial behavior of sources (stationary or mobile) and their activity (activation rate of TGF-ß, longer activation of sources) could lead to lung fibrosis. Current clinical trials for fibrosis that target TGF-ß need to consider TGF-ß sources' spatial properties and activity to develop better treatment strategies.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Maraviroc inhibits SARS-CoV-2 multiplication and s-protein mediated cell fusion in cell culture.

In an effort to identify therapeutic intervention strategies for the treatment of COVID-19, we have investigated a selection of FDA-approved small molecules and biologics that are commonly used to treat other human diseases. A investigation into 18 small molecules and 3 biologics was conducted in cell culture and the impact of treatment on viral titer was quantified by plaque assay. The investigation identified 4 FDA-approved small molecules, Maraviroc, FTY720 (Fingolimod), Atorvastatin and Nitazoxanide that were able to inhibit SARS-CoV-2 infection. Confocal microscopy with over expressed S-protein demonstrated that Maraviroc reduced the extent of S-protein mediated cell fusion as observed by fewer multinucleate cells in the context of drug-treatment. Mathematical modeling of drug-dependent viral multiplication dynamics revealed that prolonged drug treatment will exert an exponential decrease in viral load in a multicellular/tissue environment. Taken together, the data demonstrate that Maraviroc, Fingolimod, Atorvastatin and Nitazoxanide inhibit SARS-CoV-2 in cell culture.

Iterative community-driven development of a SARS-CoV-2 tissue simulator.

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Regulating cellular cyclic adenosine monophosphate: "Sources," "sinks," and now, "tunable valves".

A number of hormones and growth factors stimulate target cells via the second messenger pathways, which in turn regulate cellular phenotypes. Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger that facilitates numerous signal transduction pathways; its production in cells is tightly balanced by ligand-stimulated receptors that activate adenylate cyclases (ACs), that is, "source" and by phosphodiesterases (PDEs) that hydrolyze it, that is, "sinks." Because it regulates various cellular functions, including cell growth and differentiation, gene transcription and protein expression, the cAMP signaling pathway has been exploited for the treatment of numerous human diseases. Reduction in cAMP is achieved by blocking "sources"; however, elevation in cAMP is achieved by either stimulating "source" or blocking "sinks." Here we discuss an alternative paradigm for the regulation of cellular cAMP via GIV/Girdin, the prototypical member of a family of modulators of trimeric GTPases, Guanine nucleotide Exchange Modulators (GEMs). Cells upregulate or downregulate cellular levels of GIV-GEM, which modulates cellular cAMP via spatiotemporal mechanisms distinct from the two most often targeted classes of cAMP modulators, "sources" and "sinks." A network-based compartmental model for the paradigm of GEM-facilitated cAMP signaling has recently revealed that GEMs such as GIV serve much like a "tunable valve" that cells may employ to finetune cellular levels of cAMP. Because dysregulated signaling via GIV and other GEMs has been implicated in multiple disease states, GEMs constitute a hitherto untapped class of targets that could be exploited for modulating aberrant cAMP signaling in disease states. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling.

Spatially compartmentalized phase regulation of a Ca2+-cAMP-PKA oscillatory circuit.

Signaling networks are spatiotemporally organized to sense diverse inputs, process information, and carry out specific cellular tasks. In ß cells, Ca2+, cyclic adenosine monophosphate (cAMP), and Protein Kinase A (PKA) exist in an oscillatory circuit characterized by a high degree of feedback. Here, we describe a mode of regulation within this circuit involving a spatial dependence of the relative phase between cAMP, PKA, and Ca2+. We show that in mouse MIN6 ß cells, nanodomain clustering of Ca2+-sensitive adenylyl cyclases (ACs) drives oscillations of local cAMP levels to be precisely in-phase with Ca2+ oscillations, whereas Ca2+-sensitive phosphodiesterases maintain out-of-phase oscillations outside of the nanodomain. Disruption of this precise phase relationship perturbs Ca2+ oscillations, suggesting the relative phase within an oscillatory circuit can encode specific functional information. This work unveils a novel mechanism of cAMP compartmentation utilized for localized tuning of an oscillatory circuit and has broad implications for the spatiotemporal regulation of signaling networks.

Sensors for optical thermometry based on luminescence from layered YVO4: Ln3+ (Ln = Nd, Sm, Eu, Dy, Ho, Er, Tm, Yb) thin films made by atomic layer deposition.

Below the Earth's crust, temperatures may reach beyond 600 K, impeding the batteries used to power conventional thermometers. Fluorescence intensity ratio based temperature probes can be used with optical fibers that can withstand these conditions. However, the probes tend to exhibit narrow operating ranges and poor sensitivity above 400 K. In this study, we have investigated single and dual layered YVO4: Ln3+ (Ln = Nd, Sm, Eu, Dy, Ho, Er, Tm, Yb) thin films (100-150 nm) for use in fluorescence intensity ratio based temperature sensors in the 300-850 K range. The type of lanthanide emission can be fine-tuned by adjusting the thickness of each layer, and the layered structure allows for emission from otherwise incompatible lanthanide pairs. This novel multi-layered approach enables high sensitivity over a broad temperature range. The highest relative sensitivity was achieved for a dual layered YVO4: Eu3+/YVO4: Dy3+ sample, exhibiting a maximum sensitivity of 3.6% K-1 at 640 K. The films were successfully deposited on all tested substrates (silicon, iron, aluminum, glass, quartz, and steel), and can be applied homogenously to most surfaces without the use of binders. The films are unaffected by water, enabling non-contact temperature sensing in water, where IR thermometers are not an option.

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals.

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gαs, and Gαi proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs.

User attitudes towards physical activity websites in a randomized controlled trial.

OBJECTIVE: To better understand the mixed findings regarding the efficacy of Internet-based physical activity interventions, we examined the use and usefulness of particular website components that may lead to improvements in intervention efficacy. METHOD: Participants were sedentary individuals from a 12-month randomized controlled physical activity trial conducted in Providence, Rhode Island and Pittsburgh, Pennsylvania from 2003-2006. The present study included participants from the Tailored Internet arm (n=81; instantaneous web-based tailored feedback to participants) or the Standard Internet arm (n=82; websites currently available to the public). We obtained objective data via the intervention websites and subjective usefulness data via questionnaires. RESULTS: The Tailored Internet arm logged onto their website significantly more times than the Standard Internet arm (median 50 vs. 38; p<.05). Among participants in the Tailored Internet arm, the self-monitoring feature (i.e., logging) followed by goal setting were rated as the most useful website components. CONCLUSION: Logins in the current study were substantially higher compared to previous studies. Participants endorsed goal setting and self-monitoring as being most useful, which are critical components for health behavior change. Future studies should continue to examine these features and improve the perceived usefulness of other theory-based strategies.

Stability analysis in spatial modeling of cell signaling.

Advances in high-resolution microscopy and other techniques have emphasized the spatio-temporal nature of information transfer through signal transduction pathways. The compartmentalization of signaling molecules and the existence of microdomains are now widely acknowledged as key features in biochemical signaling. To complement experimental observations of spatio-temporal dynamics, mathematical modeling has emerged as a powerful tool. Using modeling, one can not only recapitulate experimentally observed dynamics of signaling molecules, but also gain an understanding of the underlying mechanisms in order to generate experimentally testable predictions. Reaction-diffusion systems are commonly used to this end; however, the analysis of coupled nonlinear systems of partial differential equations, generated by considering large reaction networks is often challenging. Here, we aim to provide an introductory tutorial for the application of reaction-diffusion models to the spatio-temporal dynamics of signaling pathways. In particular, we outline the steps for stability analysis of such models, with a focus on biochemical signal transduction. WIREs Syst Biol Med 2018, 10:e1395. doi: 10.1002/wsbm.1395 This article is categorized under: Biological Mechanisms > Cell Signaling Analytical and Computational Methods > Dynamical Methods Models of Systems Properties and Processes > Mechanistic Models.

Luminescent YbVO4 by atomic layer deposition.

UV to visible and near-infrared converting thin films of YbVO4 have been deposited by atomic layer deposition, using the precursor combinations Yb(thd)3 (thd = 2,2,6,6-tetramethyl-3,5-heptanedione) and O3, and VO(thd)2 and O3 at a deposition temperature of 240 °C, followed by post deposition annealing at 400-1000 °C. The UV absorption and the visible and near-infrared emission have been investigated in detail. The structure, thickness and composition of the deposited films have been studied by X-ray diffraction, ellipsometry, and X-ray fluorescence, respectively. The optimal pulse ratio of Yb(thd)3 and VO(thd)2 with respect to near-infrared emission was found to be 1 : 3, which also yielded the most crystalline sample after annealing. Crystallization of the films is accelerated when an excess of V2O5 is present, enabling crystallization at temperatures as low as 500 °C, probably through a flux aided process.

Cryptic MCAT enhancer regulation in fibroblasts and smooth muscle cells. Suppression of TEF-1 mediated activation by the single-stranded DNA-binding proteins, Pur alpha, Pur beta, and MSY1.

An asymmetric polypurine-polypyrimidine cis-element located in the 5' region of the mouse vascular smooth muscle alpha-actin gene serves as a binding site for multiple proteins with specific affinity for either single- or double-stranded DNA. Here, we test the hypothesis that single-stranded DNA-binding proteins are responsible for preventing a cryptic MCAT enhancer centered within this element from cooperating with a nearby serum response factor-interacting CArG motif to trans-activate the minimal promoter in fibroblasts and smooth muscle cells. DNA binding studies revealed that the core MCAT sequence mediates binding of transcription enhancer factor-1 to the double-stranded polypurine-polypyrimidine element while flanking nucleotides account for interaction of Pur alpha and Pur beta with the purine-rich strand and MSY1 with the complementary pyrimidine-rich strand. Mutations that selectively impaired high affinity single-stranded DNA binding by fibroblast or smooth muscle cell-derived Pur alpha, Pur beta, and MSY1 in vitro, released the cryptic MCAT enhancer from repression in transfected cells. Additional experiments indicated that Pur alpha, Pur beta, and MSY1 also interact specifically, albeit weakly, with double-stranded DNA and with transcription enhancer factor-1. These results are consistent with two plausible models of cryptic MCAT enhancer regulation by Pur alpha, Pur beta, and MSY1 involving either competitive single-stranded DNA binding or masking of MCAT-bound transcription enhancer factor-1.

Suppression of tissue factor expression, cofactor activity, and metastatic potential of murine melanoma cells by the N-terminal domain of adenovirus E1A 12S protein.

Tissue factor, the cellular initiator of blood coagulation, has been implicated as a determinant of metastatic potential in human melanoma cells. Here, we report that differential expression of tissue factor in murine melanoma cell lines of known metastatic behavior is mediated by AP-1-dependent and 12S E1A oncoprotein-repressible gene transcription. When compared to weakly metastatic C10 cells, highly metastatic M4 cells possessed elevated levels of tissue factor cofactor activity, transfected promoter activity, and heterodimeric AP-1 DNA-binding complexes containing Fra-1. Transient co-expression of the adenovirus E1A 12S oncoprotein strongly repressed transcription of an AP-1-driven tissue factor reporter gene indicating the additional requirement of N-terminal E1A-interacting coactivators. Stable expression of E1A mutants defective in CBP/p300-binding failed to suppress tissue factor expression and experimental metastasis by M4 cells while clones expressing wild type E1A exhibited greatly reduced tissue factor cofactor activity and metastatic potential in vivo. Overexpression of functional tissue factor in cells containing wild type E1A failed to restore the highly metastatic M4 phenotype suggesting that additional E1A-responsive and CBP/p300-dependent genes are required to facilitate metastasis of murine melanoma cells demonstrating high tissue factor expression and cofactor activity.