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

Discrete modularity

• Easy to construct and analyse– small numbers of links allow decomposition

Optimized system

• Distributed function– modularity blurred: difficult to analyze

Femme jouant de la guitarePierre Auguste Renoir

Femme à la guitareGeorges Brqaue

La guitaristePablo Picasso

Femme à la guitarePablo Picasso

a realitya modelmodularitya mutantoptimization

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?