Our Partners and Collaborations Validating Our Platform
In-Silico Modeling
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CytoSolve®’s in silico model quantifies vascular endothelial nitric oxide regulation under shear stress, integrating multiple molecular pathways.
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CytoSolve®’s in silico modeling revealed how D-glucaric acid mechanistically enhances liver detoxification through integrated, systems-level molecular pathway analysis.
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CytoSolve®’s in silico modeling demonstrated how ClearLungs® ingredients synergistically alleviate lung congestion through inflammation, mucus regulation, and smooth muscle relaxation.
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CytoSolve®’s in silico modeling revealed how SeaMeal™ ingredients synergistically improve pet coat appearance through skin barrier synthesis and oxidative stress reduction.
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CytoSolve®’s supervised in silico modeling framework unified lupus biology across scales, accelerating hypothesis-driven research, target discovery, and translational therapeutic modeling.
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CytoSolve® integrated validated mechanistic models to simulate hydrogen’s pathway effects, predicting reduced COX-2/PGE2 and TRPV1/CGRP nociceptor signaling.
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CytoSolve® partnered with Wapiti Labs to build an in silico architecture linking Elk Velvet Antler bioactives to biological functions mechanistically.
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CytoSolve® bioinformatics modeling elucidated how oat-derived compounds regulate molecular systems controlling relaxation and fatigue via validated mechanisms at systems level.
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CytoSolve® modeled contact activation and bradykinin pathways to predict siRNA knockdown–response relationships, informing rational strategy design for HAE.
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CytoSolve® transformed NMO pathway blueprints into integrated in silico models, enabling cross-cell immune signaling analysis and cytokine prediction for therapy prioritization.
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CytoSolve® enabled UCLA to analyze anti-AQP4 IgG as a pathogenic ingredient, predicting cytokine-driven immune amplification in neuromyelitis optica.
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CytoSolve® conducted in silico combination screening of an equine joint formula, quantifying synergistic effects across inflammation, oxidative stress, degeneration, and regeneration pathways.
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CytoSolve® applied in silico systems modeling to evaluate individual and combined ingredient efficacy for joint pain, muscle soreness, and headache relief.
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CytoSolve® integrated validated pain-pathway models to quantify apigenin–hesperidin synergy, generating patent-ready evidence across inflammation, nociception, and oxidative stress.
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CytoSolve® applied in silico systems modeling to validate Redoxx® and Bug Check® ingredient synergy, safety alignment, and NASC-ready substantiation outcomes.
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CytoSolve® integrated six molecular relaxation pathways to quantify ingredient effects, enabling in silico screening, mechanistic insight, and formulation optimization strategies.
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CytoSolve® delivered a fully in silico systems modeling framework to simulate testosterone regulation and computationally evaluate plant-based formulation strategies.
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CytoSolve® constructed an in silico systems architecture to model low-grade chronic inflammation and computationally predict synergistic phytonutrient effects.
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CytoSolve® enabled modular in silico systems modeling of endothelial nitric oxide regulation, integrating multiple pathways without monolithic model reconstruction.
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CytoSolve® delivered a modular in silico systems architecture enabling scalable, mechanistic modeling of interferon-driven autoimmune biology across cell types.
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CytoSolve® enabled a fully in silico systems modeling framework to simulate and analyze caffeine–L-arginine interactions impacting cardiovascular-relevant nitric oxide biology.
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CytoSolve® partnered with the Harvard Stem Cell Institute (HSCI) to model the mechanisms of spinal muscular atrophy (SMA) in silico. This collaboration provided systems-level insights into motor neuron degeneration, enabling a deeper understanding of disease mechanisms and potential therapeutic intervention strategies based on peer-reviewed scientific validation.
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CytoSolve® enabled modular in silico systems modeling of endothelial nitric oxide regulation, integrating multiple pathways without monolithic model reconstruction.
