Optimising the flow of experiments to a robot scientist with multi-objective evolutionary algorithms

Byrne, Emma

doi:10.1145/1274000.1274006

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Each test also has a number of ''test instances,'' which is the requested number of replicates to be run to give statistically meaningful results. An evolutionary multiobjective optimizer 19 can then be used to determine which trials can be run together, without changing the metabolite solutions (the system can dispense no more than eight different metabolites or medium components), using factors, such as trial priority, number and Pick and Volume File Creation. The final stage before running the system is creation of yeast library ''picking'' files for the pregrowth plates and ''volume'' files for the i1000 liquid handler.…”

Section: Major Software Componentsmentioning

confidence: 99%

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

Sparkes

King

Aubrey

et al. 2010

JALA: Journal of the Association for Laboratory Automation

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P rogress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of ''Adam,'' which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge. ( JALA 2010;15:33-40) INTRODUCTIONWe wish to automate all aspects of laboratory science, not just the physical experimentation, but also the intellectual aspects of hypothesis formation, experiment planning, and results analysis. A ''Robot Scientist'' is a physically implemented robotic system that applies techniques from artificial intelligence (AI) to execute cycles of automated scientific experimentation. 1 This contrasts with standard laboratory automation that normally focuses on just the physical aspects of experimentation. Our Robot Scientist ''Adam'' executes, with minimal human intervention, a complex combination of operations on yeast cell cultures at medium to high throughput and, moreover, is capable of modifying those operations according to the behavior of the organisms. 2 Again, this contrasts with standard laboratory automation that is normally characterized by medium-or highthroughput execution of a linear sequence of a relatively small number of different operations. 3e5 Automating Scientific DiscoveryAutomation has been integral to many of the changes in human society since the 19th century. The advent of computer science in the mid-20th century has made practical the idea of automating aspects of scientific discovery. Computers were initially used to automate simple linear processes, for example, to collect and process laboratory instrument data, perform astronomical calculations, and create ballistic tables.Later, AI began to be used to automate aspects of planning experiments and analyzing results. Meta-DENDRAL, developed in the 1960s, was the first automated system for scientific hypothesis generation. 9 and IDS 10 were all impressive examples of automated data-driven discovery systems that could discover scientific laws as algebraic equations. A more recent example uses iterative cycles of algorithmic correlation to distil natural laws of geometric and momentum conservation, using data captured from the motion-tracking studies of a range of simple and complex oscillators and pendula. 11 However, none of the systems described fully ''closes the loop;'' they either do not collect their own data, or do not use analyzed res...

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Section: Major Software Componentsmentioning

confidence: 99%

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

Sparkes

King

Aubrey

et al. 2010

JALA: Journal of the Association for Laboratory Automation

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AutoLabDB: a substantial open source database schema to support a high-throughput automated laboratory

Sparkes

Clare

2012

Bioinformatics

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We describe the design of the database schema (AutoLabDB), detailing the main features and describing why we believe it will be relevant to LIMS manufacturers or custom builders. This database has been developed to support two large automated Robot Scientist systems over the last 5 years, where it has been used as the basis of an LIMS that helps to manage both the laboratory and all the experiment data produced.

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The jMetal framework for multi-objective optimization: Design and architecture

Durillo

Nebro

Alba

2010

IEEE Congress on Evolutionary Computation

236

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Optimising the flow of experiments to a robot scientist with multi-objective evolutionary algorithms

Cited by 4 publications

References 10 publications

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

AutoLabDB: a substantial open source database schema to support a high-throughput automated laboratory

The jMetal framework for multi-objective optimization: Design and architecture

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