Role of Information in Physical Sciences

Information and the Sciences – a Brief History

Some Examples from Life Sciences • Connections between information and the real world have

intruigued researchers for centuries.

• Maxwell’s thought experiment (circa 1867) in relation to the Second Law of Thermodynamics provides an elegant nexus.

Maxwell’s demon was one of the first observations relating energy to information.

• Szilard and Brillouin subsequently used this to equate one bit of information with K_{B}T ln 2 joules of energy.

Maxwell’s Demon Realized [David Leigh, Nature, 2007]

Irradiation of rotaxane 1 at 350 nm in CD_{3}OD at 298 K interconverts the three diastereomers of 1 and, in the presence

of benzil, drives the ring distribution away from the thermodynamic minimum, increasing the free energy of the

molecular system without ever changing the binding strengths of the macrocycle or ammonium binding sites.

• As the science of information developed, so did interest in correspondence between information theoretic and scientific measures.

• Among the more intuitive and well studied is the correspondence between thermodynamic (Boltzmann-Gibbs) entropy and information theoretic (Shannon-Hartley) entropy.

• Drawing on these, two questions arise:

– Can we draw on information theoretic formalisms to address foundational questions in scientific disciplines?

– Can we draw on physical principles to address basic questions in computing?

• This interplay between information and science is the focus of the proposed institute.

Role of Information in Scientific Discovery: Examples

What is the “informative” component of XRCC1, BRCA-1, and H. sapiens DNA ligase III?

BRCT domain, from T.Thermophilus DNA Ligase.

These are inferred using a variety of information correlation and extraction techniques (sequence analysis, Markov models), and experimentally validated.

What is the “infomative” component of a given set of PPI networks?

• PPI networks for 9 eukaryotic organisms derived from BIND and DIP

– A. thaliania, O. sativa, S. cerevisiae, C. elegans, D. melanogaster, H. sapiens, B. taurus, M. musculus, R. norvegicus

– # of proteins ranges from 288 (Arabidopsis) to 8577 (fruit fly)

– # of interactions ranges from 340 (rice) to 28829 (fruit fly)

• Ortholog contraction

– Group proteins according to existing COG ortholog clusters

– Merge Homologene groups into COG clusters

– Cluster remaining proteins via BLASTCLUST

– Ortholog-contracted fruit fly network contains 11088 interactions between 2849 ortholog groups

• Mule is available at

http://www.cs.purdue.edu/pdsl/

Conserved Protein Interaction Patterns

PSfrag replacements

Pattern C. elegans S. cerevisiae D. melanogaster

Small nuclear ribonucleoprotein complex (p < 2e − 43)

Conserved Protein Interaction Patterns

PSfrag replacements

Pattern H. sapiens S. cerevisiae

B. taurus

Actin-related protein Arp2/3 complex (p < 9e − 11)

– Typeset by FoilTEX – 7

Conserved Protein Interaction Patterns

PSfrag replacements

Pattern H. sapiens S. cerevisiae D. melanogaster

Endosomal sorting (p < 1e − 78)

– Typeset by FoilTEX – 8

What is the ”informative” component shared by two given PPI networks (Yeast and Fruit Fly)?

Rank Score z-score # Proteins # Matches  # Mismatches  # Dups.
1  15.97  6.6  18 (16, 5)  28  6  (4, 0)

protein amino acid phosphorylation (69%) JAK-STAT cascade (40%)

2 13.93 3.7 13 (8, 7) 25 7 (3, 1)

endocytosis (50%) / calcium-mediated signaling (50%)

5 8.22 13.5 9 (5, 3) 19 11 (1, 0)

invasive growth (sensu Saccharomyces) (100%) oxygen and reactive oxygen species metabolism (33%)

6 8.05 7.6 8 (5, 3) 12 2 (0, 1)

ubiquitin-dependent protein catabolism (100%) mitosis (67%)

21 4.36 6.2 9 (5, 4) 18 13 (0, 5)

cytokinesis (100%, 50%)

30 3.76 39.6 6 (3, 5) 5 1 (0, 6)

DNA replication initiation (100%, 80%)

Subnets Conserved in Yeast and Fruit Fly

Proteosome regulatory particle subnet

PSfrag replacements

S.Cerevisiae D.Melanogaster

Calcium-dependent stress-activated signaling pathway

PSfrag replacements

S.Cerevisiae D.Melanogaster

Role of Information in Scientific Discovery: Examples

What are statistically significant functional pathways in gene regulatory networks?

NARADA DEMO

Frequency  p-value  Pathway
276  5E-94  metabolic process ⊣ flagellum biogenesis → transport
136  3.1E-71 regulation of translation ⊣ DNA recombination → transport
38  4.9E-47 response to stimulus ⊣ transcription → cell motility
36  6.6E-35 flagellum biogenesis → ciliary or flagellar motility
56  1.4E-24 regulation of translation ⊣ transcription → carboxylic acid metabolism
178  8.3E-21 signal transduction ⊣ transcription → transport
14  8.6E-20 phosphate transport → transcription → phosphonate transport
16 2E-16 SOS response ⊣ regulation of transcription ⊣ DNA repair
501  1.2E-13 regulation of transcription, DNA-dependent → transport
12  3.6E-10 proteolysis ⊣ regulation of transcription ⊣ response to external stimulus
15  3.8E-7  nitrate assimilation ⊣ cytochrome complex assembly
10  1.4E-6  cell morphogenesis ⊣ protein secretion
178  3.8E-4  transcription → carbohydrate metabolic process

Information Sciences in Life Sciences, Ongoing Work

Characterizing phenotype and disease in networks.

Detecting “informative” parts of networks is an essential aspect of understanding disease and remediation (Alzheimers).