# Multi-scale model of microbial phenotype modulation by mucins > **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $620,713 ## Abstract Mucus provides a critical protective barrier against infectious agents; its role in the clearance of microorganisms from the lung [1] is long-appreciated. Recent studies by our team [2,3] on the modulation of microbial phenotypes by mucins, glycoproteins that are a primary component of mucus, are creating a whole new appreciation for the complexity of interactions in the mucosal layer. However, to date, our understanding is limited to cataloging the components in this complex milieu with little understanding of the mechanisms underlying the phenotypes that emerge from the interactions of microbes and mucins. Understanding the mechanistic mucin- driven modulation of microbial phenotypes is of paramount importance in multiple diseases including cystic fibrosis, a disease characterized by defective clearance of mucus [4]. There is emerging evidence that mucin alters the transport of secreted factors and elicits changes in gene regulation in microbes [1,5]. Recently developed metabolic models by our team of P. aeruginosa (a key pathogen in cystic fibrosis) can explicitly account for the connection between these changes in gene regulation and the metabolic functionality of the bacterium in these complex environments [6]. The underlying central hypothesis to the proposed work is that microbial phenotypes are a function of mucin-modulated transport- and metabolism-related properties. An integrative, multi-scale computational model will be constructed to guide experimental design and facilitate understanding of emergent microbe-mucin phenomena. We will develop a framework for integrating metabolic network models with continuum models of transport phenomena using agent-based models that can serve as a template for similar multi-scale modeling challenges. Specifically, we will address the following questions: (1) How do mucins modulate the metabolism of microbes? (2) How do mucins alter transport of microbes and metabolites? (3) What are the key metabolic- and transport-related modulators of clinically-relevant phenotypes of a microbe in mucus? The importance of a mechanistic understanding of the underlying complex interactions of microbes, mucins, metabolites, and transport phenomena cannot be overstated; for example, acute lower respiratory tract infections, driven by the interaction between mucus and microbes, are a critical global health problem with a greater burden of disease than cancer, heart disease, malaria, and HIV [7]. Our team of experts in computational modeling, microbial physiology, and mucus biology is well poised to tackle this complex problem. We will establish a framework for computational modeling of metabolism and transport in mucosal environments and identify key modulators of Pseudomonas phenotypes. We will be able to predict and control biofilm dispersion through modulation of the mucosal environment, resulting in the potential for more effective antibiotic targeting and ultimately strategies to treat P. aeruginosa infections and o... ## Key facts - **NIH application ID:** 10850772 - **Project number:** 5R01AI154242-05 - **Recipient organization:** UNIVERSITY OF VIRGINIA - **Principal Investigator:** Roseanne Ford - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $620,713 - **Award type:** 5 - **Project period:** 2020-06-01 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10850772 ## Citation > US National Institutes of Health, RePORTER application 10850772, Multi-scale model of microbial phenotype modulation by mucins (5R01AI154242-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10850772. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*