Microfluidics × Automated Microscopy × Computer Vision × AI
AI-powered single-cell discovery for microalgal biotechnology.
Cyanoflow is a developing platform designed to isolate, observe, measure, and identify individual microalgal cells using microfluidics, automated imaging, and artificial intelligence.
Section 01The Problem
Microalgal populations contain enormous cellular diversity. Population-level measurements often describe the average behavior of thousands or millions of cells. An unusual cell with a potentially valuable phenotype can therefore remain hidden within the population.
One number for a million cells. The outlier — highlighted above — never shows up.
Every cell becomes its own data point — the unusual ones become visible.
Section 02The Cyanoflow Concept
Cyanoflow combines four technologies into a single chain — from bulk sample to named candidate cells.
Create controlled microscopic compartments for individual-cell observation.
Capture cellular morphology and behavior over time.
Convert images into quantitative cellular measurements.
Identify patterns and phenotypes that warrant further investigation.
Section 03Inside the Chip
Sample enters the chip. Individual cells are captured into tiny aqueous compartments, which travel sealed channels through an imaging zone where computer vision and the AI analysis layer read every cell.
Conceptual schematic. The chip is designed to act like a microscopic conveyor system for biological measurements — each compartment carries one cell's worth of context through the imaging path.
Section 04Single-Cell Observation
Cyanoflow is designed to track individual cells rather than relying only on population averages.
Conceptual interface example. Values shown are illustrative — not claimed experimental results.
Section 05AI Phenotyping
Cyanoflow's AI layer is designed to transform microscopy data into measurable phenotypic features.
"What species is this?"
Taxonomy alone says little about what a cell is doing right now — or whether it is unusual at all.
"What is this cell doing?"
The system focuses on behavior: growth, motion, optical response, and change over time — patterns that can be measured, compared, and searched.
The model can analyze patterns across:
The system can then identify cells that display predefined characteristics of scientific interest — for example, unusually fast growth, distinctive optical signatures, or stable morphologies under stress.
Section 06Candidate Discovery
Cyanoflow is designed to reduce the search space by identifying individual cells that display measurable characteristics of interest. The AI does not replace biological validation. It helps researchers decide where to look next.
Section 07From Discovery to Characterization
Candidate cells identified by the system can be directed toward appropriate downstream characterization under approved laboratory conditions. This turns Cyanoflow from an imaging system into a potential discovery workflow.
Every compartment is imaged continuously as it passes the optical path.
Computer vision converts frames into quantitative single-cell features.
The AI layer scores cells against predefined characteristics of interest.
Candidate cells are flagged and routed for follow-up by position or compartment.
Selected cells move to downstream lab analysis under approved protocols.
Section 08The Data Layer
A long-term objective of Cyanoflow is to build structured datasets that connect cellular appearance and behavior with experimentally validated phenotypes.
AI-assigned phenotypes are hypotheses, not conclusions. A record becomes biology only when an independent measurement confirms it.
Section 09Research Architecture
Data flows one direction through the platform — from physics to biology to computation to the lab. Each layer passes its output to the next.
Section 10Development Roadmap
Cyanoflow is being built in stages. Each milestone is an engineering deliverable, validated before the next begins.
Build the microscopy pipeline that finds individual cells in each frame and follows them across time.
Develop chips that isolate one cell per aqueous compartment with reliable generation and transport.
Turn raw imagery into stable measurements: size, morphology, optical response, growth and division.
Train models on labeled data and validate classifications against independent measurements.
Define the criteria, then let the system flag the cells that meet them — with human review at every step.
Close the loop: computational candidates are confirmed or rejected in the lab.
Parallelize compartments, imaging, and analysis so the search space shrinks from millions of cells to a shortlist worth a researcher's time.
Section 11Why Cyanoflow?
Study biological variation at the level where it actually exists.
Convert microscopy into structured measurements.
Use computational analysis to search large numbers of observations.
The platform represents a developing research concept combining microfluidics, microscopy, computer vision, and machine learning for single-cell microalgal research.
Capabilities shown on this website should be presented as design goals, prototypes, or research objectives unless experimentally validated.
For environmental or unknown microorganisms, experimentation will follow applicable biosafety, institutional, and science-fair requirements.
Cyanoflow · Research
Cyanoflow is being developed to make individual microalgal cells measurable, searchable, and experimentally testable.
Explore Cyanoflow ResearchUnder development · Research objectives · Not validated claims