Computational Modeling of Individual Metabolic Pathways
Conditions
Keywords
Whole Genome Sequencing, N-of-1, Computational Modeling, Metabolic Pathways, Flux Balance Analysis, Bioenergetics, Genomic Simulation, Deterministic Modeling, Secondary Data Analysis
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Bioactify LLC
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Healthy volunteers
Yes
Inclusion criteria
* Adult female participant, age 45 * Possesses previously collected 30x Whole Genome Sequencing (WGS) data * Able to provide self-reported phenotypic and lifestyle information as needed for computational analysis * Willing to participate in a self-directed, observational N-of-1 study
Exclusion criteria
* None
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of Deterministic In Silico Metabolic Flux Simulation | 3 months | Assesses whether secondary 30x Whole Genome Sequencing (WGS) data can be successfully ingested and processed by a deterministic computational framework that incorporates Flux Balance Analysis (FBA) and dynamic physiologically based modeling. Feasibility is defined by the ability to run simulations without system failure, data corruption, or computational instability. This outcome evaluates computational performance only and does not assess clinical endpoints. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Generation of a Jacobian Sensitivity Atlas | 3 months | Evaluates whether the simulation engine can successfully generate a differentiable sensitivity atlas that identifies potential enzymatic or transport constraints based solely on static genomic inputs. This outcome assesses algorithmic output quality and computational stability, not clinical or diagnostic interpretation. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATOREsther Cashbaugh
Bioactify LLC
Outcome results
None listed