bio complex 2008 bio complex 2008 microsoft research – university of trento centre for...
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BioComplex2008
Microsoft Research – University of TrentoCentre for Computational and Systems Biology
Sean Sedwards
Biological Complexity
• “The Lord God is subtle, but malicious He is not.” – Einstein
• But how subtle?– Ballpark: 25000 genes with 2 related species each
(RNA, protein) gives 50000 unique entities: >250000 states.
– cf 2266 atoms in the universe!
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?
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’
Biological Science Computer Science
Formal Analysis
Pathways
Cell
Reactions
Molecules
Programs
Computer
Functions
Variables
‘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.
Biology as computation
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”
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
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
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
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.”
• No more ‘low hanging fruit’?• The more you know the more you screen?• IT generates exponential amounts of data?• FDA / development pipeline?
A 30-year decline in industry productivity as measured by New Molecular Entities (NMEs) per dollar spent in R&D.
May 2004
1
>30000
13 year decline in productivity shown in terms of rising investment against flat approvals.
Relative growth of computational power
1
90
Link between IT and pharma
Composing systems
• Electronics designed to be (de)composable
components functional blockscircuits electronic systemssub-circuits
Decomposing biology
• We would like biology to be the same…
… but it’s not designed to be decomposed
Femme jouant de la guitarePierre Auguste Renoir
Femme à la guitareGeorges Brqaue
La guitaristePablo Picasso
Femme à la guitarePablo Picasso
a realitya modelmodularitya mutantoptimization