Bios
Author
Alper Mete
Polsko-Japońska Akademia Technik Komputerowych (PJATK)
Project Description
BioOS: Continuous Real-Time Biomarker Intelligence for Personalized Health and Longevity
BrainHack Warsaw 2026 — Early-Stage Concept
Abstract: BioOS is a proposed closed-loop biological monitoring system combining an implantable multi-analyte biosensor with a personalized AI layer. It continuously reads the body’s key biological signals, cross-correlates them, and generates actionable daily protocols — while serving as the foundational infrastructure for safe, targeted anti-aging intervention.
Hypothesis: Continuous, simultaneous monitoring of a broad biomarker panel will reveal causal biological relationships invisible to current tools. This enables both day-to-day health optimization and the real-time safety net required to one day attempt selective telomerase reactivation — restoring the cellular repair mechanism evolution switched off to suppress cancer — without triggering uncontrolled cell growth.
Equipment: The core hardware is a subcutaneous implant measuring glucose, insulin, cortisol, testosterone, growth hormone, inflammatory markers, electrolytes, and kidney function markers continuously. An AI layer learns the user’s individual baseline, detects anomalies, and adapts recommendations over time. A one-time genome scan provides each user’s static biological blueprint.
Significance: BioOS moves health monitoring from population-level averages and annual bloodwork to real-time, individual-level biological intelligence. Near-term, it targets sleep, performance, and illness prevention. Long-term, it aims to be the prerequisite safety infrastructure for anti-aging intervention — decrypting human biology well enough to eventually edit it.
Project requirements
All project communication, documentation, and presentations will be conducted in English
Ability to develop or fine-tune Large Language Models (LLM) — mandatory
Experience with biological data processing, genomics, or biomarker analysis
Python proficiency and familiarity with ML frameworks (PyTorch, TensorFlow, or similar)
Genuine interest in longevity science
Ability to think across disciplines — this project bridges AI and biology
Team-oriented mindset and comfort working on early-stage, speculative concepts
Programming languages used in this project
Python
Who are we looking for?
Biologist, Neuroscientist, programmer, data analyst
What can you gain from participating?
The importance of biomarkers, health measurements, medical data analysis, connecting the bridge between biology and information, working in a team environment,
Key resources
- Carrle FP, Hollenbenders Y, Reichenbach A. Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder. Front Neurosci. 2023 Oct 2;17:1219133. doi: 10.3389/fnins.2023.1219133. PMID: 37849893; PMCID: PMC10577178.
https://pmc.ncbi.nlm.nih.gov/articles/PMC10577178/ - Jomaa F, Ebraheem F, Horowitz-Kraus T. Greater Parent-Child Brain Synchronisation During Printed Book Versus Screen Reading Using Hyperscanning Electroencephalograph Data. Acta Paediatr. 2025 Jul;114(7):1633-1641. doi: 10.1111/apa.70007. Epub 2025 Jan 31. PMID: 39891366; PMCID: PMC12147426.
https://pmc.ncbi.nlm.nih.gov/articles/PMC12147426/ - Xu Y, Aubele J, Vigil V, Bustamante AS, Kim YS, Warschauer M. Dialogue with a conversational agent promotes children’s story comprehension via enhancing engagement. Child Dev. 2022 Mar;93(2):e149-e167. doi: 10.1111/cdev.13708. Epub 2021 Nov 8. PMID: 34748214; PMCID: PMC9299009.
https://pmc.ncbi.nlm.nih.gov/articles/PMC9299009/ - Possible Data Set: https://reshare.ukdataservice.ac.uk/853123/
- Context in media: https://neurosciencenews.com/mri-early-reading-brain-activity-1996/
