1. 2008 BioComplex Sean Sedwards
Microsoft Research – University of Trento
Centre for Computational and Systems Biology

2. Biological Complexity
“The Lord God is subtle, but malicious He is not.” – Einstein
But how subtle?
Ballpark: genes with 2 related species each (RNA, protein) gives unique entities: > states.
cf 2266 atoms in the universe!
letter to Max Born in 1926:
“Quantum mechanics is very worthy of regard. But an inner voice tells me that this is not yet the right track. The theory yields much, but it hardly brings us closer to the Old One’s secrets. I, in any case, am convinced the He is not playing dice.”14
It is the same idea that God is not malicious:
“The Lord God is subtle, but malicious he is not”
(“Raffiniert ist der Herr Gott, aber boshaft ist er nicht.”)
In this context, there is a note scribbled in Einstein’s handwriting where God
is replaced by Nature:
“Nature conceals her secrets because she is sublime, not because she is a trickster.”
In 1942, to his student-colleague, Cornelius Lanczos, he still uses this metaphor:
“It is hard to sneak a look at God’s cards. But that he would choose to play dice with the world … is something I cannot believe for a single moment.”

3. Complexity of biology Is biology simpler than meteorology?
weather not predictable on fine scale or long term
Are there significant symmetries (i.e. redundancies) in biology?
If so, why is it less than optimal?
If not, we need to know everything.
How well can we comprehend systems and diseases with high dimensionality?

4. The discrete abstraction
Paradigm of discrete molecules widely accepted
Apparently accurate for current experimental level
Quantum effects not yet found to be essential
Many problems to resolve at this level of abstraction
Leads to notions of states and transitions
Essential components of Turing computation
Continuous approximation (i.e. ODEs) simplify the problem to an extent
continuous dynamical systems are not simple
ODEs cumbersome at describing ‘switches’

5. Biology as computation
Biological Science
Computer Science
Pathways
Cell
Reactions
Molecules
Programs
Computer
Functions
Variables
Formal Analysis
‘Protein molecules as computational elements in living cells’, Denis Bray, Nature, July 1995 …
‘From molecular to modular cell biology’, Hartwell et al., Nature, December 1999 …
‘Life, logic and information’, Paul Nurse, Nature, July 2008.

6. Misconceptions (Some) computer scientists’ naïve view of biology
over-reliance on biological results
using precise methods on approximate data
(Some) biologists’ concept of computer science
“I will only speak to a computer scientist when I have a lot of data to analyse”
it’s not possible to automate because “biology is an art”

7. Biological complexities
Many parts in different individual states
combinatorial explosion of global states
Experimental limitations
Microscopic size of parts and speed of interactions
Inaccurate data
Missing data
Inherent intractability of nonlinearity
Memory: time and history is important
reduces effectiveness of static analysis of high level abstractions
Entanglement of causality caused by feedback
the inherent cyclic nature of life
Lack of modularity caused by evolutionary optimization
Lack of simple genotype – phenotype linkage
Redundancy of genes at species level not necessarily at level of individuals
Epigenetic effects

8. Modelling approaches Top-down is accurate but not complete
reality
Bottom-up is precise but not accurate
Errors multiply when results composed
modelling the phenomenon
modelling the mechanism

9. Non-linearity
Assembling a large model from available data is tractable
Making an assembled model work and giving it meaning is much less tractable
Apparent modularity is misguiding
a human necessity for understanding

10. Limits of knowledge Easy to generate large amounts of data with IT
much more difficult to generate knowledge
How much precise dynamical information can be inferred from experimental snapshots?
We probably know much less than we think
e.g. March 2006: TGN1412 (CD28-SuperMAB)
“… caused catastrophic systemic organ failure … despite being administered at a … dose … 500 times lower than the dose found safe in animals, resulting in the hospitalization of six volunteers … At least four … suffered multiple organ dysfunction and one … signs of developing cancer.”

11. Link between IT and pharma
A 30-year decline in industry productivity as measured by New Molecular Entities (NMEs) per dollar spent in R&D.
May 2004 1
>30000
No more ‘low hanging fruit’?
The more you know the more you screen?
IT generates exponential amounts of data?
FDA / development pipeline?
13 year decline in productivity shown in terms of rising investment against flat approvals.
Relative growth of computational power 1 90

12. Composing systems Electronics designed to be (de)composable
functional blocks
sub-circuits
circuits
electronic systems
components

13. Decomposing biology We would like biology to be the same…
… but it’s not designed to be decomposed

14. Discrete modularity Easy to construct and analyse
small numbers of links allow decomposition

15. Optimized system Distributed function
modularity blurred: difficult to analyze

16. optimization modularity a mutant a model a reality
Femme jouant de la guitare
Pierre Auguste Renoir
Femme à la guitare
Georges Brqaue
Femme à la guitare
Pablo Picasso
La guitariste
Pablo Picasso

17. What do we need? Top-down accurate but not precise
Bottom-up precise but not accurate
Combination improves precision and accuracy
How much precision and accuracy is necessary to be effective?
Are we on track?
Do we need completely different abstractions?