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The Biology of Information. not a keynote , but a footnote on molecular biology and computation for Rocky 1. Walter Fontana (SFI) [email protected] www.santafe.edu/~walter. 1. What can computation do for biology?. The computer as…. The computer as…. … theater : simulation, modeling.

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slide1
The Biology of Information

not a keynote, but a footnote

on molecular biology and computation

for Rocky 1

Walter Fontana (SFI)

[email protected]

www.santafe.edu/~walter

slide5
The computer as…

…theater: simulation, modeling

slide6
The computer as…

…theater: simulation, modeling

…library: organization of data

slide7
The computer as…

…theater: simulation, modeling

…library: organization of data

…instrument: component of experiment

slide8
The computer as…

…theater: simulation, modeling

…library: organization of data

…instrument: component of experiment

…mathematical structure: formalism, concept

slide12
1. What can computation do for biology?

2. What can biology do for computation?

slide13
molecular biology and computer science

are in the same conceptual business

…but this business is not well understood on both sides…

slide14
molecular biology and computer science

are in the same conceptual business

at the very minimum,

both are about structure-behavior relations,

i.e. configuring systems to engender specific behaviors

(both are “programming” disciplines)

slide16
a self-printing program in C

now imagine these expressions…

… decaying

… moving around

… combining into imprecise meanings

… acting in parallel & asynchronously

slide17
a self-printing program

now imagine these expressions…

… decaying

… moving around

… combining into imprecise meanings

… acting in parallel & asynchronously

slide18
molecular components…

…turn over (from minutes to days)

…are stochastic (wrt reliability, number, recognition)

…move around (passively or actively) in a structured medium

…communicate through physical contact

…control each other’s state and production

…are often multipurpose

…need (lots of) energy for communication

…operate concurrently

slide19
…which entails a suite of issues, such as:

turn-over of components:

persistence of identity

memory of state

stochasticity (in number and recognition):

error-correction

massive concurrency:

emergence of determinism

coordination & conflicts

communication by contact:

energy transport

control of space

slide20
biological architectures emphasize systemic capacities, e.g.

plasticity

reconfigurability

compressibility

evolvability (neutrality, modularity)

autonomy

self

robustness

all these features are desirable but absent in present day

computer architectures

slide21
+

IS NOT

in biological systems, there is no

“software running on something” !

slide22
in (theoretical) computer science…

…physical hardware is distinct from software.

(in CS, “machine” is a software notion)

in biology…

…physical hardware is software

slide23
analog

digital

physics

  • dynamics
  • stochasticity
  • effective potentials
  • combinatorial trajectories & path-dependency
  • discrete events & concurrency
  • object syntax and action
  • generative interactions

logic

slide24
A few vignettes where the gap between

computation and molecular biology is widest

slide25
Who is the “signal”??

enzyme kinetics 101

slide28
multiple phosphorylation in proteins (phosphobase*)

W.Fontana & D.Krakauer (in progress)

* A. Kreegipuu, N. Blom, S. Brunak. Nucleic Acids Research (1998/1999)

slide33
multiple phosphorylation as pulse filter

W.Fontana & D.Krakauer (in progress)

slide34
multiple phosphorylation as pulse filter

W.Fontana & D.Krakauer (in preparation)

slide40
stochastic treatment of a P-chain with symmetric feedback

large J:

Bose-Einstein

|relative average diff of end states|

small J:

Curie-Weiss

n/signal

S.Krishnamurty,

E.Smith,

D.Krakauer,

W.Fontana

Phys.Rev.Lett., submitted

second order phase-transition

slide41
idea by M.Sasai & P.Wolynes:

stochastic master equation

introduce operator algebra

familiar from many-body physics

obtain equivalent equation,

now approachable by techniques

from many-body physics

effective potentials

slide42
Sasai & Wolynes: “Stochastic gene expression as a many-body problem”,

PNAS, 100, 2374–2379 (2003).

the landscape concept made formally precise

by techniques from statistical mechanics

“programming” becomes sculpting an appropriate landscape.

But how?

(cf. neural networks, spin glasses…)

the landscape metaphor: from energy landscapes in proteins to

epigenetic landscapes a la Waddington

slide44
allosteric

RNA gates

Milan N Stojanovic, Darko Stefanovic. Nature Biotechnology, 21, 1069 - 1074 (2003)

slide45
Why do we need the formalisms of computation and logic?

a pragmatic answer: more tools get us to more places.

a deeper answer: because we need a theory of (molecular) objects.

Why?

Because the pressing (and recalcitrant) question for biology is not

only to describe the behavior of a particular system, but to understand

that system in the context of the possible, i.e. of what is evolutionarily

accessible to it.

Stated differently: we must eventually be able to reason about novelty.

We never can do so within the confines of dynamical systems,

because dynamical systems do not represent the objects they are made of.

(Remember chemistry.)

slide46
we need an abstraction of chemistry

in which

molecules are interacting computational agents

the grand challenge:

describe a system with an expression that is

at the same time

a program to “run” that system

AND

a formula to reason about it abstractly.

slide47
A brief coda where the gap between

computation and molecular biology is closing

(at the formal language end)

slide48
Old notion of computation

no interaction with the “environment”

function

output

input

semantics: input-output relation

slide49
New notion of computation

interaction with the “environment”

process

semantics: potential sequences of interaction events

slide50
computation:

function

process

analogy in physics:

closed system

open system

equilibrium

normal form

main concern:

organization

slide51
Theory of concurrency, Process algebra

Robin Milner, Communicating and Mobile Systems: the p-calculus, Cambridge (1999)

slide52
The p-calculus (Milner, Walker and Parrow 1989)
  • a program specifies a network of interacting processes
  • processes are defined by their potential communication activities
  • communication occurs on complementary channels, identified by names
  • message content: channel name
slide53
Aviv Regev, Ehud Shapiro, Corrado Priami, and others:

application of concurrency / process algebras

to molecular signal transduction

A.Regev & E.Shapiro, Nature, 419, 343 (2000), Concepts

slide54
concurrency theory, what for?

at worst:

  • tool for agent-based simulation

at its most hopeful:

  • tool for agent-based simulation based on a theory of the agents
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