# Network-based Framework to Decode Novel ‘Gain-of-Function’ Mutations and their Mechanistic Roles in General Human Diseases > **NIH NIH R35** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $393,169 ## Abstract Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more and more clear that many deleterious mutations could exhibit a 'gain-of-function' behavior. Systematic investigation of such mutations has been lacking and largely overlooked. In the last few years it has become more clear that the efficacy and specificity of signal transduction in a cell is, at heart, a problem of molecular recognition and protein interaction. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to gain-of- function mutations, it would give rise to various disease types. Research in my laboratory is focused on developing and utilizing quantitative and molecular technologies to understand protein interaction networks and their perturbations by genomic mutations, bridging genotype and phenotype in health and disease. Our overall goal is to contribute to the understanding of disease mechanisms and of more open ended questions about explanations for 'missing heritability' in genome-wide association studies. We envision that It will be instrumental to push current human genetics research paradigm towards a thorough functional and quantitative modeling of all genomic mutations and their mechanistic molecular interaction events involved in disease development and progression. Therefore, gaining a systems-level understanding of gain-of-function mutations requires to resolve the plastic nature of molecular interactions, and to integrate experimental and computational strategies at the genome scale. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, how do interaction networks undergo rewiring upon gain-of- function mutations? Which mutations are key for gene regulation and cellular decisions? Do mutagtions exhit allel-specific behaviors or how do the allelic combinations work to coordinate cellular phenotypes? Is it possible to leverage molecular interaction networks to engineer signal transduction in cells, aiming to cure disease? To begin to address these questions, in this proposal, we will systematically interrogate of gain-of-function disease mutations using a novel network-based systems biology framework. We will then decipher condition-dependent protein-protein interaction perturbations induced by gain-of-function mutations in disorder regions and phosphorylation sites. Finally, we will determine allele-specific and allele-combinatorial effect of gain-of-function mutations on protein interaction network rewiring. Together, this integrative proposal is innovative because it will provide insights in prioritizing driver functional gain-of-function disease mutations, and uncovering individualized molecular mechanisms at a base resolution. Furthermore, it is significant because it will greatly facilitate the funct... ## Key facts - **NIH application ID:** 10247013 - **Project number:** 5R35GM133658-03 - **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN - **Principal Investigator:** S. Stephen Yi - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $393,169 - **Award type:** 5 - **Project period:** 2019-09-15 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10247013 ## Citation > US National Institutes of Health, RePORTER application 10247013, Network-based Framework to Decode Novel ‘Gain-of-Function’ Mutations and their Mechanistic Roles in General Human Diseases (5R35GM133658-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10247013. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*