Dr. James Sharpe

1. James Sharpe - ICREA, Coordinator of EMBL-CRG Systems Biology Unit - Barcelona
Saunders 1948

2. . . .
James Sharpe
Coordinator of the EMBL-CRG Systems Biology Unit
ICREA Research Professor
Barcelona

3. • Going beyond correlations
• But very challenging
• For the moment, we have more success with correlations than systems modelling
• Biology = “more than the sum of its parts”
• This is due to COMPLEXITY: non-linearities, feedbacks, dynamics
• Requires dynamical models, which make predictions about dynamical behaviour
• Requires testing these predictions with experimental perturbations
• Therefore, requires experimentally accessible model systems (mice, flies, worms, cells)
• Cannot experiment on people
• Patient data can be used for correlations, mapping, extrapolations, pharmacogenomics …
. . .

4. Page 11
. . .

5. “Why is it so difficult to come up with a concise
definition of systems biology?”
“FROM SYSTEMS BIOLOGY TO PERSONALIZED MEDICINE”
“Systems biology means different things to
different people”
Nat. Rev. Mol. Cell Bio. 7:188 (2006)
“Although there is a growing consensus about
the importance of systems biology, there are
almost as many definitions as there are
practitioners.”
The Biochemist, April 2005
“… it is therefore in danger of losing it’s
meaning.”
Williamson (2005)

6. “the whole is something over and above its parts and not
just the sum of them all”
Aristotle (384–322 B.C.)
‘‘Every object that biology studies is a system of systems.’’
Francois Jacob (1974).

7. Systems biology is an approach by which biological questions are addressed
through integrating data collection activities with
computational/mathematical modelling activities to produce a better
understanding of biological systems (or sub-systems).
BBSRC (2013)
Systems Biology is a rapidly evolving discipline that seeks to determine how
complex biological systems function by integrating experimentally derived
information through mathematical and computing solutions. Through an
iterative process of experimentation and modelling, systems biology aims to
understand how individual components interact to govern the functioning of the
system as a whole. Imperial College London (2013)
More recent definitions…
Systems biology describes the study of complex systems, with emphasis on how
interactions between components of biological systems underlie the behaviour of
the system as a whole. It is typified by the generation and testing of models of
complex processes (generally quantitative and computational) to explain and
predict biological phenomena, and combines iterative cycles of theory, modelling
and experiments. MRC (2012)

8. Whole is greater than the sum of the parts
Iterative cycles:
• making predictions
• testing predictions
Experimental, quantitative data
Computer modelling

9. Whole is greater than the sum of the parts
“the whole is something over and above its parts and not
just the sum of them all”
Aristotle (384–322 B.C.)
“EMERGENCE”

10. 0.560.63
Cell cycle Spatial patterning
Whole is greater than the sum of the parts
Emergence comes largely from the dynamic behaviour over time

11. Whole-genome
“Statistical” SB
Model-driven
“Mechanistic” SB
Emergence comes largely from the dynamic behaviour over time

12. Completeness:
Emergence:
Whole-genome
“Statistical” SB
Model-driven
“Mechanistic” SB
General or abstract
principles of organisation
Data-mining
Whole genome,
Whole transcriptome,
Whole proteome,
etc...
Maybe emergent
levels of organisation?
Scale-free, hubs, etc...
Whole temporal sequence of
events (and all points in space).
All components relevant to
defined biological process.
Trajectories through phase
space: Oscilations, bi-stability,
homeostasis, canalisation...
• Pick a defined problem
• Focus on how it works
• Causative relationships
• Temporal Dynamics
TIME – PHASE SPACE
2 types of Systems Biology

13. 2 types of Systems Biology
Whole-genome
“Statistical” SB
Model-driven
“Mechanistic” SB
• Pick a defined problem
• Focus on how it works
• Causative relationships
• Temporal Dynamics
TIME – PHASE SPACE
General or abstract
principles of organisation
Data-mining
SUBSETS

14. Westerhoff & Palsson, Nature Biotechnology 22:1249 (2004)
“The evolution of molecular biology into systems biology”
Where did it come from? A history of systems biology...
DESCRIPTIONS / DATA
MODELS / UNDERSTANDING / DYNAMICS

15. Westerhoff & Palsson, Nature Biotechnology 22:1249 (2004)
“The evolution of molecular biology into systems biology”
Where did it come from? A history of systems biology...
DESCRIPTIONS / DATA
MODELS / UNDERSTANDING / DYNAMICS

16. The mechanistic/dynamical side of understanding systems
has been a defined science for quite a long time
Dynamical Systems Theory
Systems Theory
Cybernetics
Distributed Neural Networking
Systems
Biology

17. Whole-genome
“Statistical” SB
Model-driven
“Mechanistic” SB
• Pick a defined problem
• Focus on how it works
• Causative relationships
• Temporal Dynamics
TIME – PHASE SPACE
General or abstract
principles of organisation
Data-mining
Dynamical Systems Theory
Systems Theory
Cybernetics
Distributed Neural Networking
The mechanistic/dynamical side of understanding systems
has been a defined science for quite a long time

18. Whole-genome
“Statistical” SB
Model-driven
“Mechanistic” SB
• Pick a defined problem
• Focus on how it works
• Causative relationships
• Temporal Dynamics
TIME – PHASE SPACE
General or abstract
principles of organisation
Data-mining
Natural extension of:
bioinformatics, genomics
CORRELATIONS
INTERPOLATIONS
EXTRAPOLATIONS
Greater than the sum of the parts

19. Whole is greater than the sum of the parts
Biological system is (just) the sum of its parts?
or

20. G PGENETICS / GENOMICS
“…fallen short of expectations…”
correlation = causality/
CORRELATIONS / STATISTICS

21. G PGENETICS / GENOMICS
GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
HEIGHT
CANCER A
COPD II
CANCER B
COPD I

22. GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G PGENETICS / GENOMICS
PLEIOTROPIC
HEIGHT
CANCER A
COPD II
CANCER B
COPD I

23. GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G PGENETICS / GENOMICS
MULTIGENIC
HEIGHT
CANCER A
COPD II
CANCER B
COPD I

24. GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G PGENETICS / GENOMICS
DIFFICULT, BUT THEORETICALLY STATISTICALLY POSSIBLE . . .
HEIGHT
CANCER A
COPD II
CANCER B
COPD I
One of the PREDICTIVE aspects
But limited success

25. GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G PGENETICS / GENOMICS
MULTIGENIC
HEIGHT
CANCER A
COPD II
CANCER B
COPD I
One of the PREDICTIVE aspects
Limited success, because it assumes:
Biological system is (just) the sum of its parts

26. GENES:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G PGENETICS / GENOMICS
f(x)
EPISTASIS
The BLACK BOX of biology
HEIGHT
CANCER A
COPD II
CANCER B
COPD I

28. GENOTYPE PHENOTYPE
dV
dt
= f(V,P)
+ INITIAL CONDITIONS
+ BOUNDARY CONDITIONS
DYNAMICAL MODELS
Emergent, complex behaviour

29. GENOTYPE PHENOTYPE

30. GENOTYPE PHENOTYPE
dV
dt
= f(V,P)
VARIABLES PARAMETERS

31. GENOTYPE PHENOTYPE
BIOLOGY
• CONCEPTS
• DYNAMICS
• FEEDBACK
• THEORY
• QUESTIONS
TOOLS
• IMAGING
• MICROSCOPY
• SIMULATIONS
• MOL. BIOL.
• QUANTITATIVE
DATA
• 4D IMAGES
• EXPRESSION
• PROTEIN STATES
• SHAPES
• RATES

32. GENOTYPE PHENOTYPE

33. FGF8 Wnt3a
MULTISCALE
INTEGRATION
ORGAN
TISSUE
CELLS
NETWORKS
MOLECULES

37. • Developmental Diseases
• Wound healing
• Regeneration
• Tissue engineering
• Tumour growth

38. Gorski & Misteli, Journal of Cell Science (2005)
Sounds like: “This is how you should do biology”…
Iterative cycles:
• making predictions
• testing predictions
Whole is greater than the sum of the parts
Experimental, quantitative data
Computer modelling

39. Gorski & Misteli, Journal of Cell Science (2005)
Sounds like: “This is how you should do biology”…
REINVENTING THE WHEEL?

40. EXPERIMENTS & OBSERVATIONS
FUNCTIONAL PERTURBATIONS
DATA
THEORY / HYPOTHESES
EXPLORING IDEAS
PREDICTIONS
“THE SCIENTIFIC METHOD”
QUANTITATIVE
COMPREHENSIVE
REINVENTING THE WHEEL?
A B C
REQUIRES A LOT OF EXPERIMENTAL TESTING
DYNAMIC MODELLING

41. Which protein should we try to drug?
The Mammalian MAPK/ERK Pathway Exhibits
Properties of a Negative Feedback Amplifier. Oliver E.
Sturm, Richard Orton, Joan Grindlay, Marc Birtwistle,
Vladislav Vyshemirsky, David Gilbert, Muffy Calder,
Andrew Pitt, Boris Kholodenko and Walter Kolch (21
December 2010) Science Signaling 3 (153), ra90
Two examples of medically-relevant systems modelling
How do drugs affect dynamical heart behaviour?
Helped understand the impacts of:
• Ivabradine
• Ranolazine
Dennis Noble et al.
Years of work with experimentally-accessible model systems.
Many, many experimental perturbations.
Many iterations of testing model predictions.
Personalised models may (one day) be tailored from (limited) patient data,
but the basic models cannot be built from patient data.
✔
✘

42. • Going beyond correlations
• But very challenging
• For the moment, we have more success with correlations than systems modelling
• Biology = “more than the sum of its parts”
• This is due to COMPLEXITY: non-linearities, feedbacks, dynamics
• Requires dynamical models, which make predictions about dynamical behaviour
• Requires testing these predictions with experimental perturbations
• Therefore, requires experimentally accessible model systems (mice, flies, worms, cells)
• Cannot experiment on people
• Patient data can be used for correlations, mapping, extrapolations, pharmacogenomics …
. . .
Multicellular Lung Systems Biology?
THANKYOU