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Related Experiment Video

Updated: Jun 12, 2025

Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials

Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials

Published on: March 9, 2017

Starting a synthetic biological intelligence lab from scratch.

Md Sayed Tanveer^1,2, Dhruvik Patel^1,2,3, Hunter E Schweiger⁴

¹Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Patterns (New York, N.Y.)

|June 9, 2025

Summary

Synthetic biological intelligence (SBI) trains in vitro neurons for tasks, offering a low-energy alternative to artificial intelligence (AI). This guide helps computational and biological labs bridge the knowledge gap to advance SBI research efficiently.

Keywords:

artificial intelligence biocomputing electrophysiology neuroAI neuroscience organoid intelligence synthetic biological intelligence

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Last Updated: Jun 12, 2025

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Area of Science:

Neuroscience
Artificial Intelligence
Biotechnology

Background:

Gigantic artificial intelligence (AI) models require substantial energy and data.
The human brain achieves similar outputs with significantly less data and energy.
Synthetic biological intelligence (SBI), using in vitro neurons, is an emerging field.

Purpose of the Study:

To provide a roadmap for computational labs entering SBI research by detailing biological aspects.
To outline computational considerations for biological labs interested in SBI.
To offer strategies, processes, risks, and precautions for efficient SBI research initiation.

Main Methods:

Literature review and synthesis of interdisciplinary knowledge (tissue engineering, biomaterials, signal processing, AI, neuroscience).
Development of general strategies and step-by-step processes for SBI research.
Identification of potential risks and necessary precautions for cost and time mitigation.

Main Results:

A structured approach for computational labs to acquire biological SBI knowledge.
Guidance for biological labs on computational SBI aspects.
A framework for minimizing delays and costs in SBI research.

Conclusions:

SBI presents a promising, energy-efficient alternative to conventional AI.
Interdisciplinary collaboration and knowledge sharing are crucial for SBI advancement.
This work facilitates broader participation and accelerates progress in the field of synthetic biological intelligence.