AdaLab. Adaptive Automated Scientific Laboratory (AdaLab) Adaptive Machines in Complex Environments. n Start Date:

Transcription

1 AdaLab

2 AdaLab Adaptive Automated Scietific Laboratory (AdaLab) Adaptive Machies i Complex Eviromets Start Date:

3 Scietific Backgroud

4 The Cocept of a Robot Scietist Computer systems capable of origiatig their ow experimets, physically executig them, iterpretig the results, ad the repeatig the cycle. Backgroud Kowledge Hypothesis Formatio Aalysis Fial Theory Experimet selectio Robot Results Iterpretatio

5 Scietific Goals We aim to itegrate the scietific method with 21 st cetury automatio techology. We aim to make scietific discovery more efficiet: cheaper, faster, better. Our visio is that withi 10 years may scietific discoveries will be made by teams of huma ad robot scietists. This collaboratios will produce scietific kowledge more efficietly tha either could aloe.

6 Scietific Goals We propose to develop a framework for semi-automated ad automated kowledge discovery by teams of huma ad robot scietists. This framework will itegrate ad advace: kowledge represetatio, otology egieerig, sematic techologies, machie learig, bioiformatics, ad automated experimetatio. We will evaluate the AdaLab framework o a importat realworld applicatio i cell biology with biomedical relevace to cacer ad ageig.

7 The Diauxic Shift Yeast (S. cerevisiae). First tur sugar ito ethaol. The tur ethaol ito CO 2. Cacer Ageig

8 Eve Eve ruig

9 Parters

10 Bruel Uiversity: Coordiator I Lodo, ear Heathrow airport. Over 15,000 studets ad 1,000 academic staff from 113 differet coutries Coordiates 11 FP7 EU projects, ad a parter i >50 other EU projects Secured > 18 M i Europea fudig for the last two years A wide rage of scietific expertise, from robotic egieerig to the Cetre of Systems ad Sythetic Biology

11 Role i Project: Kowledge represetatio The formal machie processable represetatios of the priciple data ad kowledge etities ivolved to the project, e.g. equipmet, processes, participats, hypotheses, data, results. A declarative laguage for ML ad probabilistic reasoig compoets. A kowledge base about the yeast diauxic shift. A commuicatio mechaism betwee robot ad huma scietists.

12 Uiversity of Machester Ross D. Kig, Professor of Machie Itelligece, Machester Role i Project North of Eglad Head of Research Ala M. Turig Robot Scietists: Eve. Biological applicatio Machester Baby Machie Learig

13 Parter KULeuve (Belgium) PI: Ja Ramo Specificity: Data miig i graphs Active learig Probabilistic models Related projects: MiGraNT (theory for data miig i etworks) ISPECtor (Proteomics experimetal research) Role i ChistERA AdaLab: Modelig ucertai kowledge Hypothesis geeratio Optimisatio of experimet selectio Probabilistic iferece Algorithms for etwork data

14 Laboratoire d'iformatique de Paris-Nord: LIPN Celie Rouveirol LIPN is asssociated with CNRS (UMR 7030). Research groups i Combiatorics, Combiatorial Optimisatio, Algorithmics, Logic, Software Egieerig, Natural Laguage, Machie Learig. The group ivolved i the project is: Machie Learig ad applicatios. Research i this team focuses o three mai topics: Algebraic ad logical models of learig, Collaborative ad trasfer learig, Learig structures from heterogeeous data.

15 Role i the Project Iductive Logic Programmig: icremetal theory revisio, active learig, (determiistic) actio model learig, learig from ambiguous relatioal examples. Complex systems aalysis: commuity extractio, lik predictio i multiplex etworks I collaboratio with UPMC-LIP6, collective learig (multiaget), distributed abductio. I collaboratio with Evry Uiversity, iferece of regulatio etworks from gee expressio datasets.

16 AdaLab

17 AdaLab Adaptive Automated Scietific Laboratory (AdaLab) Adaptive Machies i Complex Eviromets Start Date:

18 Scietific Backgroud

19 The Cocept of a Robot Scietist Computer systems capable of origiatig their ow experimets, physically executig them, iterpretig the results, ad the repeatig the cycle. Backgroud Kowledge Hypothesis Formatio Aalysis Fial Theory Experimet selectio Robot Results Iterpretatio

20 Scietific Goals We aim to itegrate the scietific method with 21 st cetury automatio techology. We aim to make scietific discovery more efficiet: cheaper, faster, better. Our visio is that withi 10 years may scietific discoveries will be made by teams of huma ad robot scietists. This collaboratios will produce scietific kowledge more efficietly tha either could aloe.

21 Scietific Goals We propose to develop a framework for semi-automated ad automated kowledge discovery by teams of huma ad robot scietists. This framework will itegrate ad advace: kowledge represetatio, otology egieerig, sematic techologies, machie learig, bioiformatics, ad automated experimetatio. We will evaluate the AdaLab framework o a importat realworld applicatio i cell biology with biomedical relevace to cacer ad ageig.

22 The Diauxic Shift Yeast (S. cerevisiae). First tur sugar ito ethaol. The tur ethaol ito CO 2. Cacer Ageig

23 Parters

24 Bruel Uiversity: Coordiator I Lodo, ear Heathrow airport. Over 15,000 studets ad 1,000 academic staff from 113 differet coutries Coordiates 11 FP7 EU projects, ad a parter i >50 other EU projects Secured > 18 M i Europea fudig for the last two years A wide rage of scietific expertise, from robotic egieerig to the Cetre of Systems ad Sythetic Biology

25 Role i Project: Kowledge represetatio The formal machie processable represetatios of the priciple data ad kowledge etities ivolved to the project, e.g. equipmet, processes, participats, hypotheses, data, results. A declarative laguage for ML ad probabilistic reasoig compoets. A kowledge base about the yeast diauxic shift. A commuicatio mechaism betwee robot ad huma scietists.

26 Uiversity of Machester Ross D. Kig, Professor of Machie Itelligece, Machester Role i Project North of Eglad Head of Research Ala M. Turig Robot Scietists: Eve. Biological applicatio Machester Baby Machie Learig

27 Parter KULeuve (Belgium) PI: Ja Ramo Specificity: Data miig i graphs Active learig Probabilistic models Related projects: MiGraNT (theory for data miig i etworks) ISPECtor (Proteomics experimetal research) Role i ChistERA AdaLab: Modelig ucertai kowledge Hypothesis geeratio Optimisatio of experimet selectio Probabilistic iferece Algorithms for etwork data

28 Laboratoire d'iformatique de Paris-Nord: LIPN Celie Rouveirol LIPN is asssociated with CNRS (UMR 7030). Research groups i Combiatorics, Combiatorial Optimisatio, Algorithmics, Logic, Software Egieerig, Natural Laguage, Machie Learig. The group ivolved i the project is: Machie Learig ad applicatios. Research i this team focuses o three mai topics: Algebraic ad logical models of learig, Collaborative ad trasfer learig, Learig structures from heterogeeous data.

29 Role i the Project Iductive Logic Programmig: icremetal theory revisio, active learig, (determiistic) actio model learig, learig from ambiguous relatioal examples. Complex systems aalysis: commuity extractio, lik predictio i multiplex etworks I collaboratio with UPMC-LIP6, collective learig (multiaget), distributed abductio. I collaboratio with Evry Uiversity, iferece of regulatio etworks from gee expressio datasets.

30 Istitute of Systems & Sythetic Biology (issb-ue) Mohamed Elati The Istitute of Systems ad Sythetic Biology is a research uit of Uiversity of Evry ad CNRS. The issb is located o the Geopole campus, the leadig BioPark i Frace, ear Paris. Research areas: machie learig, computatioal ad systems biology, bioiformatics. AdaLab: Recostructio of cotext-specific molecular etworks about yeast diauxic shift Itegratio of molecular etwork data, to recostruct active otology ad selectig experimets

31 Key challeges ad potetial impact of the project

32 Key Challeges 1 The proposed AdaLab eeds to be: autoomous ad perceptive to huma requiremets (its scietific collaborators). able to cotiuously lear, adapt ad improve i the real world complex eviromet of scietific research. capable of cotiuous cycles of scietific hypothesis formatio ad experimetatio that will improve its scietific kowledge (models).

33 Key Challeges 2 Itegratig a systems approach, with the research ivolvig collaboratio betwee experts i: robotics, machie learig, logical ad probabilistic iferece, sematic techologies, ad yeast microbiology. Itegratig high-level reasoig about scietific kowledge with the cotrol of low-level robotic movemets to execute experimets. Develop a protocol for commuicatio betwee huma ad robot scietists.

34 Key Challeges 3 Scietific kowledge is iheretly ucertai. Therefore withi the AdaLab framework we eed to develop Bayesia methods that make ifereces ad pla experimets uder ucertaity. Scietific kowledge is best represeted usig logic. To itegrate logic with probabilities we will use statistical relatioal learig, ad develop a otology for represetig ucertai kowledge. The success of the AdaLab framework will be objectively determied by quatitative measuremets of the differet system compoets, ad the scietific kowledge geerated.

35 Key Outputs A AdaLab demostrated to be greater tha 20% more efficiet at discoverig scietific kowledge (withi a limited scietific domai) tha huma scietists aloe. A ovel otology for modellig ucertai kowledge. A efficiet commuicatio mechaism betwee huma ad robot scietists. New machie learig methods for the geeratio ad efficiet testig of complex scietific. Novel biomedical kowledge about cell biology relevat to cacer ad ageig.

36 Potetial Impact Sciece is the greatest geerator of ecoomic wealth (through developmets i techology). Sciece is the greatest driver of better health (through developmet i biomedical sciece). The AdaLab framework will cotribute to realisig Europe s 2020 strategy for smart, sustaiable ad iclusive growth

37 Potetial Impact Itelliget laboratories have the potetial to speed up the techological progress. Such a icrease would lead to more scietific discoveries, better techological solutios, ad ew products. For example ew better drugs could be delivered to the market faster ad cheaper. Curretly, ~25Billio is spet aually withi the EU o pharmaceutical research. Most of this is spet o late-stage trials (which are less ameable to automatio), but we coservatively estimate that ~10% is ameable to the AdaLab framework.

38 Plaig

39 Work packages

40 Work pla Five iterliked WPs. The proposed AdaLab framework will be developed i three cyclic iteratios. The mai software compoets will be icremetally released ad updated. All projects outputs will be made available to the research commuity by the ed of the project.

41 The Ed of the Begiig of AdaLab