An independent research collective studying how biological networks encode memory, reorganize across the lifespan, and evolve — from neural circuits to microbial populations.
BioCommons Lab (BCL) is an open research collective focused on network-level principles that govern memory, aging, and evolution. We study how structure and dynamics shape function in complex biological systems — spanning neural circuits, microbial gene networks, and evolving populations.
Our work bridges neuroscience, aging biology, and microbial evolution to ask a shared question: how do biological systems store information, adapt under pressure, and fail over time? We pursue this through open-access datasets, open-source experimental platforms, and transparent computational frameworks designed to be reproducible and extensible.
Investigating how neural network architecture supports learning and memory, and how aging alters connectivity, stability, and information flow in the brain.
Quantitative analysis of biological networks using whole-brain imaging, population dynamics, and time-series data to characterize connectivity, coordination, and failure modes across scales.
Experimental evolution and genome-scale perturbations to understand how microbial populations adapt, diversify, and acquire antibiotic resistance under selective pressure.
Shared computational and statistical tools for comparing biological systems, including network representations, dynamical analyses, and model-driven interpretation of learning, adaptation, and breakdown.
BioCommons Lab collaborates with academic laboratories, open science communities, and independent researchers worldwide to pursue system-level questions in neuroscience, aging, and evolutionary biology.
All BioCommons Lab projects prioritize open data, open methods, and transparent funding, with the explicit goal of building shared scientific infrastructure for the public good. Wherever possible, datasets, code, and experimental workflows are designed to be reusable beyond a single project or institution.
We welcome partnerships aligned with rigorous, open, and collaborative approaches to biological research—particularly efforts that benefit from cross-system comparison, quantitative analysis, and long-term reuse.
🤝 Support our work via Open Collective
💻 GitHub