Talk entitled "from the Virtual Human to a Digital Me" presented at the Virtual Physiological Human 2012 Conference held at IET Savoy, Savoy Place, London, 18-20 September 2012.

1. From
the
Virtual
Human
to
a
‘Digital
Me’
Nour
Shublaq,
Peter
Coveney
Centre
for
Computa-onal
Science
University
College
London,
UK
n.shublaq@ucl.ac.uk
VPH 2012 – Infrastructures: Looking Ahead,
Thursday Sept 20, 2012, London

2. Overview
• What
is
a
‘Digital
Me’?
• Ingredients
• Some
challenges
ahead
and
key
to
implementa-on
• Conclusions

3. A
‘Digital
Me’:
Google
maps
of
the
human
body
‘a
coherent
digital
representa-on
that
is
used
as
an
integra-ve
framework
for
the
consolida-on
of
fundamental
and
transla-onal
Integra-ve
Biomedical
Research,
and
the
provision
to
(European)
Ci-zens
of
an
affordable
Personalised,
Predic-ve,
and
Integra-ve
Medicine’
• Interface
to
informaAon
-­‐
having
an
efficient,
effec-ve
and
interac-ve
interface
to
the
combined,
heterogeneous
informa-on
based
on
innova-ve,
interac-ve
visualisa-on
technologies
• Blender
of
informaAon
-­‐
the
ability
to
combine,
integrate,
fuse
informa-on
in
a
synergis-c
way,
and
to
return
such
fusion
to
the
user
visually.
This
involves
knowledge
management,
data
fusion,
image
processing,
mul--­‐
modal
visualisa-on,
and
visualisa-on
of
uncertainty
• PaAent
avatar
-­‐
Modelling
of
physiological
and
pathological
processes
and
their
representa-on
in
a
way
that
fosters
understanding,
explora-on
and
possibly
the
produc-on
of
new
knowledge
from
pa-ent-­‐specific
and
popula-on-­‐specific
informa-on
and
knowledge
Digital
Pa)ent
Roadmap

4. • What
is
a
‘Digital
Me’
?
• Ingredients
• Some
challenges
ahead
and
key
to
implementa-on
• Conclusions

5. Human
Genome
Sequencing
Sixty years ago we barely understood
the genetic basis of heredity. Today,
next generation sequencing has led to
fundamental shifts in our
understanding of biology.
No more than 25,000 protein coding
genes in the human genome and not
more than 100,000 previously thought.
Thousands of DNA variants have now
been associated with traits/diseases.
Physical characteristics and disease
risk are partly determined by small
genetic differences
Structure
Mol.
Profiles
Genomic
2
10
3000
30,000

6. 6
New
Sequencers
1 Human Genome in:
5 years (2001)
2 years (2004)
4 days (Jan 2008)
16 Hours (Oct 2008)
3 Hours (Nov 2009)
6 minutes (Now!)
Cost of whole genome sequencing expected to drop to $100 in a few years

7. Genotype-­phenotype
resources
Molecular-­level
models
(GWAS,
PPI,
…)
Translational
Systems
Biology
Clinical
phenotypes
(EHR,
multi-­scale
physiological
models…)
Exposome
(drugs,
diet,
environmental
chemicals,…)
Developments
1
2
3
System-­level
models
(organ
networks,…)
Text
mining
&
semantic
web
Complex
disease
networks
Pharmacogenomics
Disease
susceptibility
Disease
gene
Oinding
Phenotypic
variation

8. Use
Case:
Cancer
Treatment
8
Drug treatment
recommendation
Genome
and Transcriptome
sequencing
Tumor sampling
Tumor stem cell extraction/
expansion
Modeling
Drug Response
The Cancer Model
X
X
X
Patient Specific Model
Drug Database
Mutation Database

9. Consumer
led
Healthcare
PaAentLikeMe
US-­‐based
social
networking
and
data
sharing
plaZorm
for
people
with
a
range
of
mainly
chronic
and
serious
condi-ons
-­‐
New
security
sengs
23andme
personal
genomics
company
stores
and
analyses
the
genotypes
of
thousands
of
individuals
at
over
500,000
different
posi-ons

11. MobileTech Special (Qualcomm)

12. Informationweek.com

13. RunKeeper
Best
App
for
Exercise
Fooducate
Best
App
for
Healthy
Ea-ng
Sleep
Cycle
Best
App
for
Snoozing
Top
5
Health
Apps
The
Times
Aug
2012

14. Lose
It!
Best
App
for
Weight
Loss
ZocDoc
Best
App
for
Finding
a
Doctor

15. E-­‐infrastructure
and
compuAng
in
the
‘cloud’
Amazon and Microsoft are
providing cloud services
for data storage and
retrieval

17. • What
is
a
‘Digital
Me’
?
• Ingredients
• Some
challenges
ahead
and
key
to
implementa-on
• Conclusions

18. Some
challenges
ahead
Biological
challenges
– Do
we
understand
biology
and
diseases
enough
to
develop
reliable
computa-onal
models?
– How
to
integrate
growing
knowledge
into
models?
ICT
Challenges
– Data
quality
– Data
management
– Data
security
– User
interfaces
Societal
challenges
– Privacy
– How
to
prevent
inequali-es
in
access
to
health
care?
– Health
care
economics
– Implementa-on
in
health
care
– How
to
prevent
adverse
effects/misuse?

19. • Exploit
unprecedented
amounts
of
detailed
biological
data
being
accumulated
for
individual
people
(e.g.
at
GP
surgeries,
labs),
some
of
which
are
already
available
on
EHRs
• Harness
the
latest
developments
in
ICT
– large
scale
data
integra-on
and
mining,
cloud
compu-ng,
high
performance
compu-ng,
advanced
modelling
and
simula-on,
– all
brought
together
in
a
highly
flexible
plaZorm.
• Turn
this
informa-on
into
knowledge
that
assists
in
taking
medical,
clinical
and
lifestyle
decisions
for
the
ci-zen
• Bridge
the
knowledge
gap
in
the
clinical/medical
community
• Pay
acen-on
to
the
ethical,
legal
and
societal
issues
Key
to
ImplementaAon

20. Clinicians
of
Tomorrow
• With
the
rush
of
genomic
data
into
hospitals,
and
an
increased
adop-on
of
electronic
health
records,
the
medical/clinical
community
is
faced
with
a
knowledge
gap.
• Match
the
knowledge
and
training
available
today
for
the
medical
and
clinical
communi-es
with
the
changing
landscape
of
medical
prac-ce
and
personalised
medicine
• Train
clinicians
today
to
be
comfortable
and
familiar
with
the
use
of
genomic
data
in
managing
their
pa-ents.
For
example,
although
it
might
be
more
useful
for
sequencing
and
genomic
research
to
freeze
tumor
samples,
surgeons
and
pathologists
most
oden
store
-ssue
in
formalin,
which
tends
to
make
meaningful
sequencing
more
difficult.

21. E-­‐infrastructure
&
ICT
Layers

22. Ethical,
legal
and
societal
issues
Autonomy
Well-­‐being
JusAce
Scien-sts
Freedom
to
research
Facili-es
and
funding
Appropriate
reward
e.g.
IP
Pa-ents
Right
to
know
or
not
to
know
Improved
treatment
op-ons
Access
to
resources
Vulnerable
groups
Right
to
be
heard
Allevia-on
of
disadvantage
Equality
Professional
groups
Professional
judgment
Increased
burden?
Implica-ons
for
prac-ce
Data
breach
is
the
unauthorised
acquisi-on,
access,
use,
or
disclosure
of
protected
health
informa-on
ownership
of
data,
consent,
compliance,
what
are
the
applicable
laws
and
regula-ons
governing
the
data?
Audi-ng
in
the
cloud?

23. • What
is
a
‘Digital
Me’
?
• Ingredients
• Some
challenges
ahead
and
key
to
implementa-on
• Conclusions

24. • Medicine
today
is
a
driver
of
ICT
innova-on
and
vice
versa
• Advanced
IT
allows
us
to
analyse
pa-ents
all
the
way
up
from
their
own
DNA
sequences
• A
personalised
‘digital
Me‘
approach
is
expected
to
lead
to
improved
– health
outcomes
– drugs/treatments
– disease
preven-on
– evidence-­‐based
decision-­‐making
– lifestyle
choices
for
global
ci-zens
Conclusions

25. Thank
you
for
your
aenAon!
Nour
Shublaq
University
College
London,
UK
n.shublaq@ucl.ac.uk