Perspectives for self-driving labs in synthetic biology

Martín, Héctor García; Radivojević, Tijana; Zucker, Jeremy; Bouchard, Kristofer E.; Sustarich, Jess; Peisert, Sean; Arnold, Dan; Hillson, Nathan J.; Babnigg, G.; Martí, Jose Manuel; Mungall, Christopher J.; Beckham, Gregg T.; Waldburger, Lucas; Carothers, James M.; Sundaram, ShivShankar; Agarwal, D.; Simmons, Blake A.; Backman, Tyler W. H.; Banerjee, Deepanwita; Tanjore, Deepti; Ramakrishnan, Lavanya; Singh, Anup K.

doi:10.1016/j.copbio.2022.102881

Cited by 34 publications

(19 citation statements)

References 50 publications

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“…Automated science in space should be aimed at enabling partially or fully autonomous deep-spaceflight-ready 'self-driving labs' 85,86 that employ AI and ML in a closed-loop system to produce new knowledge and optimize experimental design based on data collected in previous…”

Section: Self-driving Labsmentioning

confidence: 99%

Biological research and self-driving labs in deep space supported by artificial intelligence

et al. 2023

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Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space.Space biology research focuses on answering fundamental mechanistic questions about how molecular, cellular, tissue and whole organismal life responds to the space environment. Biological stressors of spaceflight include ionizing radiation, altered gravitational fields, accelerated day-night cycles, confined isolation, hostile closed environments, distance-duration from Earth 1 , planetary dust regolith 2,3 , and extreme temperatures and atmospheres 4,5 . Moreover, spaceflight stressors are probably compounded and amplified with increasing

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Section: Self-driving Labsmentioning

confidence: 99%

Biological research and self-driving labs in deep space supported by artificial intelligence

et al. 2023

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“…As the sophistication of the tools and machines used increases, greater levels of automation can be achieved, and human responsibility decreases as a result. Previous reviews have also proposed similar classifications of automation based on self-driving vehicle levels of autonomy. , Here we build upon these efforts with greater focus placed on physical automation and its role in the build and test phases of the DBTL cycle. This thorough classification within lab automation aims to provide a deeper understanding of the state and progress of physical automation solutions and their role in achieving truly automated workflows in tandem with more established design and learning tools.…”

Section: Characterizing the Current State Of Lab Automationmentioning

confidence: 99%

Physical Laboratory Automation in Synthetic Biology

Stephenson,

Lastra,

Nguyen

et al. 2023

ACS Synth. Biol.

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Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

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“…There is a major global effort underway to automate, down-scale, and democratize biological research. As a key component of these efforts, both commercial and open-source equipment are being developed for microbial cultivation and execution of biochemical reactions in a miniaturized context with online monitoring, with the intent to greatly accelerate the rate of data generation at lower cost, in reduced physical space, and with lower materials consumption (1)(2)(3)(4)(5)(6). Moreover, to make biological research more accessible, there is a substantial drive for open-source hardware and software in biological research that often follows a do-it-yourself (DIY) model, enabled by the assembly of lowcost, off-the-shelf components (7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Integration of pH control into Chi.Bio reactors and demonstration with small-scale enzymatic poly(ethylene terephthalate) hydrolysis

Denton,

Murphy,

Norton-Baker

et al. 2024

Preprint

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Small-scale bioreactors that are affordable and accessible would be of major benefit to the research community. In previous work, an open-source, automated bioreactor system was designed to operate up to the 30 mL scale with online optical monitoring, stirring, and temperature control, and this system, dubbed Chi.Bio, is now commercially available at a cost that is typically 1-2 orders of magnitude less than commercial bioreactors. In this work, we further expand the capabilities of the Chi.Bio system by enabling continuous pH monitoring and control through hardware and software modifications. For hardware modifications, we sourced low-cost, commercial pH circuits and made straightforward modifications to the Chi.Bio head plate to enable continuous pH monitoring. For software integration, we introduced closed-loop feedback control of the pH measured inside the Chi.Bio reactors and integrated a pH-control module into the existing Chi.Bio user interface. We demonstrated the utility of pH control through the small-scale depolymerization of the synthetic polyester, poly(ethylene terephthalate) (PET), using a benchmark cutinase enzyme, and compared this to 250 mL bioreactor hydrolysis reactions. The results in terms of PET conversion and rate, measured both by base addition and product release profiles, are statistically equivalent, with the Chi.Bio system allowing for a 20-fold reduction of purified enzyme required relative to the 250 mL bioreactor setup. Through inexpensive modifications, the ability to conduct pH control in Chi.Bio reactors widens the potential slate of biochemical reactions and biological cultivations for study in this system, and may also be adapted for use in other bioreactor platforms.

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Perspectives for self-driving labs in synthetic biology

Cited by 34 publications

References 50 publications

Biological research and self-driving labs in deep space supported by artificial intelligence

Biological research and self-driving labs in deep space supported by artificial intelligence

Physical Laboratory Automation in Synthetic Biology

Integration of pH control into Chi.Bio reactors and demonstration with small-scale enzymatic poly(ethylene terephthalate) hydrolysis

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