Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges
This is an incremental review and proposal for using biological mechanisms to enable machine learning at a molecular scale, potentially for low-powered or biological computing applications.
The paper explores molecular machine learning (MML) by investigating how biological systems, such as gene regulatory networks and calcium signaling, can be used to perform machine learning functions, including building an Analog to Digital Converter (ADC).
Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.