We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.

A mathematical model of intravascular coagulation is presented; it encompasses the biochemistry of the tissue factor pathway, platelet activation and deposition on the subendothelium, and flow- and diffusion-mediated transport of coagulation proteins and platelets. Simulation experiments carried out with the model indicate the predominant role played by the physical processes of platelet deposition and flow-mediated removal of enzymes in inhibiting coagulation in the vicinity of vascular injury. Sufficiently rapid production of factors IXa and Xa by the TF:VIIa complex can overcome this inhibition and lead to formation of significant amounts of the tenase complex on the surface of activated platelets and, as a consequence, to substantial thrombin production. Chemical inhibitors are seen to play almost no (TFPI) or little (AT-III and APC) role in determining whether substantial thrombin production will occur. The role of APC is limited by the necessity for diffusion of thrombin from the site of injury to nearby endothelial cells to form the thrombomodulin-thrombin complex and for diffusion in the reverse direction of the APC made by this complex. TFPI plays an insignificant part in inhibiting the TF:VIIa complex under the conditions studied whether its action involves sequential binding of TFPI to Xa and then TFPI:Xa to TF:VIIa, or direct binding of TFPI to Xa already bound to the TF:VIIa complex.

Airway hyperresponsiveness is a major characteristic of asthma and is believed to result from the excessive contraction of airway smooth muscle cells (SMCs). However, the identification of the mechanisms responsible for airway hyperresponsiveness is hindered by our limited understanding of how calcium (Ca2+), myosin light chain kinase (MLCK), and myosin light chain phosphatase (MLCP) interact to regulate airway SMC contraction. In this work, we present a modified Hai-Murphy cross-bridge model of SMC contraction that incorporates Ca2+ regulation of MLCK and MLCP. A comparative fit of the model simulations to experimental data predicts 1), that airway and arteriole SMC contraction is initiated by fast activation by Ca2+ of MLCK; 2), that airway SMC, but not arteriole SMC, is inhibited by a slower activation by Ca2+ of MLCP; and 3), that the presence of a contractile agonist inhibits MLCP to enhance the Ca2+ sensitivity of airway and arteriole SMCs. The implication of these findings is that murine airway SMCs exploit a Ca2+-dependent mechanism to favor a default state of relaxation. The rate of SMC relaxation is determined principally by the rate of release of the latch-bridge state, which is predicted to be faster in airway than in arteriole. In addition, the model also predicts that oscillations in calcium concentration, commonly observed during agonist-induced smooth muscle contraction, cause a significantly greater contraction than an elevated steady calcium concentration.

The dynamics of resource patches and species that exploit such patches are of interest to ecologists, conservation biologists, modelers, and mathematicians. Here we consider how social interactions can create unique, evolving patterns in space and time. Whereas simple prey taxis (with consumable prey) promotes spatial uniform distributions, here we show that taxis in producer-scrounger groups can lead to pattern formation. We consider two types of foragers: those that search directly ("producers") and those that exploit other foragers to find food ("scroungers" or exploiters). We show that such groups can sustain fluctuating spatiotemporal patterns, akin to "waves of pursuit." Investigating the relative benefits to the individuals, we observed conditions under which either strategy leads to enhanced success, defined as net food consumption. Foragers that search for food directly have an advantage when food patches are localized. Those that seek aggregations of group mates do better when their ability to track group mates exceeds the foragers' food-sensing acuity. When behavioral switching or reproductive success of the strategies is included, the relative abundance of foragers and exploiters is dynamic over time, in contrast with classic models that predict stable frequencies. Our work shows the importance of considering two-way interactioni.e., how food distribution both influences and is influenced by social foraging and aggregation of predators.pattern formation | foraging strategies | ecological patchiness | chemotaxis | spatial ecology I n this paper, we study the dynamics of social interactions to explore the consequences for spatiotemporal population structure and dynamics. We show that interactions among individuals are key for pattern formation and self-organization when foragers either follow gradients of food or socialize with those that do. Our aim is to demonstrate that social interactions among foragers could have particularly important implications for spatial models of forager-resource dynamics. A comprehensive understanding of the spatial dynamics of social foraging needs to consider the two-way dynamic interaction between forager aggregation and resource patchiness, a problem that remains poorly understood (1, 2).A secondary theme is the discovery of another pattern-forming mechanism. Nature abounds with patterns that the human eye is adept at picking out. Patterns occur in chemical, physical, and biological systems on many scales, from distribution of proteins in a cell, and tissue morphogenesis, to patchy distribution of species in ecology (3-5, 6). There is great interest in finding both universal mechanisms for such patterns (e.g., the balance of repulsion-attraction forces, local activation and long-range inhibition, or motion in an external field; ref. 7), as well as specific examples that have rich pattern-forming features (8).Patterns formed by organisms, and the way they shape their environment, is a rich area with physical (phase transitions), engineering (robotics), sociological (e.g...

Cofilin is an important regulator of actin polymerization, cell migration, and chemotaxis. Recent experimental data on mammary carcinoma cells reveal that stimulation by epidermal growth factor (EGF) generates a pool of active cofilin that results in a peak of actin filament barbed ends on the timescale of 1 min. Here, we present results of a mathematical model for the dynamics of cofilin and its transition between several pools in response to EGF stimulation. We describe the interactions of phospholipase C, membrane lipids (PIP(2)), and cofilin bound to PIP(2) and to F-actin, as well as diffusible cofilin in active G-actin-monomer-bound or phosphorylated states. We consider a simplified representation in which the thin cell edge (lamellipod) and the cell interior are represented by two compartments that are linked by diffusion. We demonstrate that a high basal level of active cofilin stored by binding to PIP(2), as well as the highly enriched local milieu of F-actin at the cell edge, is essential to capture the EGF-induced barbed-end amplification observed experimentally.

Rapid polymerization of actin filament barbed ends generates protrusive forces at the cell edge, leading to cell migration. Two important regulators of free barbed ends, cofilin and Arp2/3, have been shown to work in synergy (net effect greater than additive). To explore this synergy, we model the dynamics of F-actin at the leading edge, motivated by data from EGF-stimulated mammary carcinoma cells. We study how synergy depends on the localized rates and relative timing of cofilin and Arp2/3 activation at the cell edge. The model incorporates diffusion of cofilin, membrane protrusion, F-actin capping, aging, and severing by cofilin and branch nucleation by Arp2/3 (but not G-actin recycling). In a well-mixed system, cofilin and Arp2/3 can each generate a large pulse of barbed ends on their own, but have little synergy; high synergy occurs only at low activation rates, when few barbed ends are produced. In the full spatially distributed model, both synergy and barbed-end production are significant over a range of activation rates. Furthermore, barbed-end production is greatest when Arp2/3 activation is delayed relative to cofilin. Our model supports a direct role for cofilin-mediated actin polymerization in stimulated cell migration, including chemotaxis and cancer invasion.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic requires the rapid development of efficacious treatments for patients with life-threatening coronavirus disease (COVID-19). Quantitative Systems Pharmacology (QSP) models are mathematical representations of pathophysiology for simulating and predicting the effects of existing or putative therapies. The application of model-based approaches, including QSP, have accelerated the development of some novel therapeutics. Nevertheless, the development of disease-scale mechanistic models can be a slow process, often taking years to be validated and considered mature. Furthermore, emerging data may make any QSP model quickly obsolete. We present a prototype QSP model to facilitate further development by the scientific community. The model accounts for the interactions between viral dynamics, the major host immune response mediators, and tissue damage and regeneration. The immune response is determined by viral activation of innate and adaptive immune processes that regulate viral clearance and cell damage. The prototype model captures two physiologically relevant outcomes following infection: a 'healthy' immune response that appropriately defends against the virus, and an uncontrolled alveolar inflammatory response that is characteristic of acute respiratory distress syndrome. We aim to significantly shorten the typical QSP model development and validation timeline by encouraging community use, testing, and refinement of this prototype model. It is our expectation that the model will be further advanced in an open science approach; i.e. by multiple contributions towards a validated quantitative platform in an open forum, with the ultimate goal of informing and accelerating the development of safe and effective treatment options for patients.

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