Continuous N-of-1 Computational Feasibility Study of a Whole Genome Sequence

Overview

This is an observational, computational N-of-1 study that uses previously collected Whole Genome Sequencing (WGS) data from a single adult participant to evaluate the feasibility of a deterministic bioenergetic simulation model. The study does not involve clinical interventions, treatments, or prospective specimen collection. All analyses are performed on existing genomic data in a secure computational environment. The purpose of this study is to determine whether a physics-based metabolic model can successfully integrate individual genomic constraints to generate personalized, hypothesis-driven insights about metabolic pathways. The study focuses on model feasibility and computational performance, not clinical outcomes. No medications, diets, or behavioral interventions are administered or evaluated.

This protocol (B-2026-N1) describes a continuous, observational N-of-1 computational feasibility study conducted by Bioactify LLC and What Ifs Tech Inc. The study uses secondary, commercially obtained 30x Whole Genome Sequencing (WGS) data from a single adult participant. No new data are collected for research purposes, and no clinical interventions are performed. The primary objective is to assess the feasibility of integrating static genomic constraints into a deterministic simulation engine that incorporates Flux Balance Analysis (FBA) and dynamic physiologically based modeling. The computational framework is designed to explore how individual genetic variants may influence metabolic pathway behavior. All analyses occur within an isolated cloud environment using pre-existing data. This study is observational and exploratory. It does not evaluate the safety, efficacy, or clinical utility of any treatment, diet, medication, or device. The research aims to generate methodological insights about individualized computational modeling and hypothesis generation. This study received an Exempt Research Determination from Pearl IRB (Protocol ID: 2026-0119) under 45 CFR 46.104(d)(4), as it involves only secondary use of existing data with no interaction or intervention with human subjects.

Conditions