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What the IoT should learn from the life sciences

What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.

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WHAT THE IOT SHOULD LEARN 
FROM THE LIFE SCIENCES
Who is 
@BorisAdryan 
• Computational biologist 
• Research group leader 
• Lecturer in genome biology 
• Advisor at 
• 2015 Fellow of the
LIFE AS WE KNOW IT 
DNA = storage of a blueprint 
transcription 
RNA = ‘active copy’ of DNA 
translation 
protein = the building blocks 
of cells and tissues 
Gregor Johann Mendel, 
exhibited in the Library at the NIMR
BIOLOGY THEN AND NOW 
SEQUENCE INFORMATION 
• Reading DNA information 
• Determining “the sequence 
of a gene” was a PhD in the 
early 1980s 
• Data processing was mainly 
transcribing the observation 
into a research paper 
Sanger sequencing 
ca. 1980 
http://www.eplantscience.com
BIOLOGY THEN AND NOW 
SEQUENCE INFORMATION 
181,563,676,918 bases base pairs on 15th October 2014 
(from 165,722,980,375 bases on 24th August 2014) 
• We can sequence a human 
genome in half a day 
• Sequence databases grow 
faster than storage capacity 
• Data processing is the key 
step in scientific 
understanding
BIOLOGY THEN AND NOW 
GENE ACTIVITY INFORMATION 
• When are genes needed? 
• Classical molecular biology 
workflow, taking days… 
• Data is semi-quantitative; 
testing one gene at the time 
Northern blot for d-vhl 
ca. May 1999

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What the IoT should learn from the life sciences

  • 1. WHAT THE IOT SHOULD LEARN FROM THE LIFE SCIENCES
  • 2. Who is @BorisAdryan • Computational biologist • Research group leader • Lecturer in genome biology • Advisor at • 2015 Fellow of the
  • 3. LIFE AS WE KNOW IT DNA = storage of a blueprint transcription RNA = ‘active copy’ of DNA translation protein = the building blocks of cells and tissues Gregor Johann Mendel, exhibited in the Library at the NIMR
  • 4. BIOLOGY THEN AND NOW SEQUENCE INFORMATION • Reading DNA information • Determining “the sequence of a gene” was a PhD in the early 1980s • Data processing was mainly transcribing the observation into a research paper Sanger sequencing ca. 1980 http://www.eplantscience.com
  • 5. BIOLOGY THEN AND NOW SEQUENCE INFORMATION 181,563,676,918 bases base pairs on 15th October 2014 (from 165,722,980,375 bases on 24th August 2014) • We can sequence a human genome in half a day • Sequence databases grow faster than storage capacity • Data processing is the key step in scientific understanding
  • 6. BIOLOGY THEN AND NOW GENE ACTIVITY INFORMATION • When are genes needed? • Classical molecular biology workflow, taking days… • Data is semi-quantitative; testing one gene at the time Northern blot for d-vhl ca. May 1999
  • 7. BIOLOGY THEN AND NOW GENE ACTIVITY INFORMATION • High-throughput gene expression profiling since mid-1990s • Quantitative information for every gene in an organism • Key challenge is the presentation and interpretation of the data
  • 8. BIOLOGY THEN AND NOW 2 6 ATP BIOCHEMISTRY • Signal transduction and metabolic pathways • Characterisation of proteins and substrates that mediate chemical reactions • Nobel prize material
  • 9. BIOLOGY THEN AND NOW BIOCHEMISTRY • We know about 250k metabolites • 100k protein structures • on the order of 10k different chemical reactions
  • 10. ‣We are learning how biological entities depend on each other ‣ Everything is connected ‣ Big, noisy, often unstructured data
  • 11. ‣ Everything is connected ‣ Big, noisy, often unstructured data www.thingslearn.com Analytics, context integration, machine learning and predictive modelling for the IoT.
  • 12. THERE’S NO ANALYTICAL FLEXIBILITY IN M2M/IOT Matt Hatton, Machina Research The BLN IoT ‘14 Internet replaces wire It’s all about the connectedness M2M consumer IoT
  • 13. LIFE SCIENCE STRATEGIES DON’T WORK IN THE IOT - There are no commonly accepted - ‘catalogue’ of things, - ‘ontology’ of things, - ‘data format’ of things, - ‘meta data’ for things. -Most businesses are driven by revenue, not long-term strategic vision - Service providers have no need to publish - Data can be highly personal (cheap excuse) unless they’re
  • 14. WE FIXED OUR KNOWLEDGE REPRESENTATION PROBLEM
  • 16. FORMALISING KNOWLEDGE WITH GENE ONTOLOGY
  • 17. CURRENT GOVERNMENT INVESTMENTS INTO GENE ONTOLOGY NIH alone spent $44,616,906 on the ontology structure since 2001 (no data for UK/EU spendings) ~100 full-time salaries for experts with domain-specific knowledge ~40,000 terms
  • 18. Oct. 1995 TOWARDS MIAMI AND DATA REPOSITORIES cf. IoT Nov. 1993
  • 19. META DATA, SHARING AND DATA REPOSITORIES founded in Nov. 1999 Nature Feb. 2000 But this is a complex and ambitious project, and is one of the biggest challenges that bioinformatics has yet faced. Major difficulties stem from the detail required to describe the conditions of an experiment, and the relative and imprecise nature of measurements of expression levels. The potentially huge volume of data only adds to these difficulties. “ “ Nov. 2000 Oct. 2002 Wide adoption as requirement for publication in scientific journals
  • 20. META DATA, SHARING AND DATA REPOSITORIES cf. IoT 2014 since 2003 Semantic Sensor Network Ontology http://en.wikipedia.org/wiki/Silo
  • 21. PUBLISH OR PERISH story measurements + meta data open, public repositories human curators ontology terms community ok? journal informal exchange - no credit! funders assessment industry! The majority of this infrastructure is paid for by governments and charities
  • 23. PUBLISH OR YOU’RE NOT DOING IOT measurements + meta data storage & provenance human curators ontology terms user ok? Maybe the majority of this infrastructure should be paid for by governments? company cloud device registration “ “ added privileges data value
  • 24. WHAT THE IOT SHOULD LEARN FROM THE LIFE SCIENCES • Given the predicted importance and impact of the IoT, we can and should not leave the development of infrastructure to commercial stakeholders alone. • We need a lot more incentives to participate and targeted investment from the government (“the funders”) into reliable infrastructure. • It took the computational life sciences less than 4 years(!) to grow from a grass roots movement to having industry-scale, expandable infrastructure. • Shared vision, dogmatic implementation, effective lobbying. @BorisAdryan is interested to hear about IoT job opportunities.