Monomer

Monomer Bio is a San Francisco-based laboratory automation software company specializing in cell culture and broader biology research workflows. The platform enables life sciences teams to develop and optimize high-quality in vitro disease models by combining AI-driven scientific agents, fully automated workcells, and comprehensive data tools — all designed to help researchers make discoveries at unprecedented speed and scale.

Built by a team of biologists, roboticists, and engineers with deep hands-on lab experience, Monomer guides clients through every stage of lab automation, from initial consultation and instrument integration to data analysis and experimental design. The platform connects to standard, readily available lab instruments, making it accessible to a wide range of research organizations.

Core Platform: The Monomer Stack

• Biology: Helps teams maintain cells at scale so researchers can focus on designing and running mission-critical experiments, with cells as the foundational layer of the platform.
• Automation: Software connects to standard lab instruments to create automated protocols that multiply throughput and unlock lab capacity.
• Data: Continuously monitors cell cultures, tracks changes over time, and makes historical well-level data easy to retrieve and reuse.
• Insights: Delivers a real-time feed of analyzed culture data to simplify cell maintenance and enable faster, more impactful decision-making.

Key Capabilities

• Monitor: An integrated system tracks changes in plates via imaging, providing real-time insight into hundreds of live samples simultaneously.
• Record: Generates, organizes, and shares cell line and culture data directly from lab instruments.
• Interpret: Plots metrics of interest to identify trends and structures imaging data for deep learning applications.
• Act: Enables researchers to take action and update protocols in response to changing growth conditions or experimental readouts.

Autonomous Cell Biology Features

• Scientific Agents: AI-powered agents grow and annotate complex tissues through a responsive UI that learns from every experiment.
• Autonomous Workcells: Execute workflows on fully automated workcells using a multi-day scheduler with well-level control and consumable tracking.
• Reproducible Tissues: Increases consistency, reduces biological noise, and eliminates single-point-of-failure risk on key capabilities through digitized production workflows.

Application Areas

• Cell Culture: Develop and optimize protocols for adherent, suspension, and 3D culture workflows, including media composition and feeding schedule optimization.
• Stem Cell Differentiation: Consistently develop organoids using 2D or 3D culture models by growing dozens of stem cell and tissue types in parallel and at scale.
• Cell Engineering: Streamline the generation of novel cell lines with tools that track critical characteristics such as transfection efficiency and monoclonality.

Team and Background

• Co-founder and CEO Jimmy Sastra, PhD brings 18 years of experience in robotics, applied optimization, and lab automation, including serving as VP of Engineering at Strateos, the world's first biology cloud lab, where he deployed over 20 automated workcells.
• CTO Yang Choo has 10+ years of experience at the intersection of in vitro and in silico engineering, with foundational technical leadership at Transcriptic.
• Applications Engineer Carter Allen has 10 years of experience designing and deploying lab automation systems, including "lights-out" automation at Counsyl.
• Co-founder and Advisor Mark Zhang has 10 years of experience in AI analytics and bioinformatics, and was an early engineer at Kensho, an MIT/Harvard-based startup that became the largest AI acquisition in history.
• The broader engineering team includes software engineers with backgrounds in SaaS analytics, enterprise security, infrastructure management, and full-stack product development.

Monomer Bio is headquartered at 1338 Mission St, San Francisco, CA, and is actively working with research organizations to bring autonomous cell biology infrastructure to modern life sciences labs.