About this Research Topic
The emergence of synthetic biological intelligence is leading the development of a new disruptive technology that provides biocomputing functionalities to synthetic tissues as a packaged system. One example is Organoid artificial intelligence (OI), that is a fast-developing field that combines artificial intelligence (AI) technology with organoids, which are tiny organs created in a lab from stem cells. The goal is to produce more intricate and accurate models of human tissues and disorders. Insights into the growth and operation of human organs, as well as the mechanisms underlying various diseases, can be gained by employing AI algorithms to analyze the data produced by organoid studies.
The capacity to model diseases in a controlled laboratory environment without the need for animal or human experiments is one of the main benefits of synthetic biological intelligence and the use of more complex organoid models as proposed by OI are particularly promising. This opens the door for more individualized therapy by enabling researchers to examine the effectiveness of proposed medications or treatments on an organoid specific to a patient. Additionally, unusual diseases and genetic problems that are challenging to mimic in animal models can be studied. Moreover, the interfacing and control of living packed biodevices, i.e. organoids, is unexplored from a bioengineering perspective, which can enhance existing in-vitro models.
Despite its potential, synthetic biological intelligence is still in its early stages of development, and there are challenges that need to be addressed, such as improving the accuracy and efficiency of the AI algorithms used to analyze the data, as well as the reproducibility of the synthetic biological models themselves. However, with continued advancements in both synthetic biology and biocomputing technologies, synthetic biological intelligence has the potential to revolutionize the field of medicine and ultimately improve patient outcomes.
This Research Topic aims to address the unknown relationships between biological and digital organoid artificial intelligence. We aim to attract works that pave the way into investigating in-vitro models inspired by digital solutions, and in-silico solutions inspired by in-vitro experiments. We hope to bring to light novel works that present solutions, methods and materials supporting the fast mutual progression of organoid intelligence in both biological and digital domains.
With the recent attraction in Synthetic Biological Intelligence and Organoid Intelligence, which englobes solutions of artificial intelligence for also cell types that incorporate organoids (neuron, astrocytes, etc.), we will push for the evolution of this new field. In this research topic, we invite stimulating, multidisciplinary, cross-disciplinary, contributions in perspective, review, theoretical and experimental formats for the following themes:
• Intersection between biological and digital technologies, such as synthetic biological intelligence
• Modelling of synthetic biological intelligence or more complex organoid intelligence
•Experimental frameworks for synthetic biological intelligence and organoid intelligence
• Engineering of synthetic biological intelligence and organoid intelligence
• Interfaces for synthetic biological intelligence or organoid intelligence in-vitro models
• Security and ethical aspects of synthetic biological intelligence and especially organoid intelligence.
• Applications of synthetic biological intelligence or organoid intelligence solutions
Topic Editor Brett J. Kagan is employed by, and holds shares in, Cortical Labs Pty Ltd, Melbourne, Australia, and is an inventor on patents for technology related this field of research. All other Topic Editors declare no competing interests with regards to the Research Topic subject
The capacity to model diseases in a controlled laboratory environment without the need for animal or human experiments is one of the main benefits of synthetic biological intelligence and the use of more complex organoid models as proposed by OI are particularly promising. This opens the door for more individualized therapy by enabling researchers to examine the effectiveness of proposed medications or treatments on an organoid specific to a patient. Additionally, unusual diseases and genetic problems that are challenging to mimic in animal models can be studied. Moreover, the interfacing and control of living packed biodevices, i.e. organoids, is unexplored from a bioengineering perspective, which can enhance existing in-vitro models.
Despite its potential, synthetic biological intelligence is still in its early stages of development, and there are challenges that need to be addressed, such as improving the accuracy and efficiency of the AI algorithms used to analyze the data, as well as the reproducibility of the synthetic biological models themselves. However, with continued advancements in both synthetic biology and biocomputing technologies, synthetic biological intelligence has the potential to revolutionize the field of medicine and ultimately improve patient outcomes.
This Research Topic aims to address the unknown relationships between biological and digital organoid artificial intelligence. We aim to attract works that pave the way into investigating in-vitro models inspired by digital solutions, and in-silico solutions inspired by in-vitro experiments. We hope to bring to light novel works that present solutions, methods and materials supporting the fast mutual progression of organoid intelligence in both biological and digital domains.
With the recent attraction in Synthetic Biological Intelligence and Organoid Intelligence, which englobes solutions of artificial intelligence for also cell types that incorporate organoids (neuron, astrocytes, etc.), we will push for the evolution of this new field. In this research topic, we invite stimulating, multidisciplinary, cross-disciplinary, contributions in perspective, review, theoretical and experimental formats for the following themes:
• Intersection between biological and digital technologies, such as synthetic biological intelligence
• Modelling of synthetic biological intelligence or more complex organoid intelligence
•Experimental frameworks for synthetic biological intelligence and organoid intelligence
• Engineering of synthetic biological intelligence and organoid intelligence
• Interfaces for synthetic biological intelligence or organoid intelligence in-vitro models
• Security and ethical aspects of synthetic biological intelligence and especially organoid intelligence.
• Applications of synthetic biological intelligence or organoid intelligence solutions
Topic Editor Brett J. Kagan is employed by, and holds shares in, Cortical Labs Pty Ltd, Melbourne, Australia, and is an inventor on patents for technology related this field of research. All other Topic Editors declare no competing interests with regards to the Research Topic subject
Keywords: Organoid Intelligence Biocomputing Synthetic Biological Intelligence Artificial Intelligence Unconventional Computing
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