3. Schematic of TIR signalling

4. Cells as computational devices Contains 1 copy of the genome Contains ca 10 10 - 10 11 protein molecules in a volume of ca 1 picolitre Contains ca 10 4 different proteins present at 10 4 - 10 7 copies/cell The proteins plus small molecules and ions form a spatially distributed, dissipative computational network capable of robust self regulation within a narrow range of physical environments. The network is non-linear- binary (?) The network is embodied as a semi-solid state device - most reactions do not occur in free solution

5. The Computational Network is Built from Noisy Components Nuclear Concentration of NF  B Measured by Confocal Imaging of Anti-relA Labelled Fibroblasts Protein concentration in cells is controlled in part by rate of gene transcription transcription is stochastic The concentration of any given protein must therefore vary between different cells in clonal population Since most if not all proteins participate in the control network, this will in turn affect control of gene expression differentially in each individual cell

6. Theoretical Analysis suggests that the Segment Polarity network in Flies is Binary

7. Random Graph Representation of Signal Transduction Network Nodes = Reactants Edges = Reactions Implementation 1.Chemical kinetics -ODEs 2.Cellular Automata 3.Software agents Basic considerations 1.Edges/nodes 2.Edge/node distribution 3.Rules 4.Bit depth 5.Word length

8. Input Promoter Signal Transduction Pathways in a Network ? Input

9. Perturbation of Internal State of Network Can Mimic Agonist Example Mechanisms 1.Titre out inhibitor 2.Increase concentration above Kd for activator

13. Positive pools are detectable by both screen strategies 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 00.20.40.60.8 TK-RL IL8-luc 5 25 45 65 85 105 125 145 50550105015502050 brightness area negative pool 5 25 45 65 85 105 125 145 50550105015502050 positive pool area Dual-Luciferase assay EGFP assay

14. Clone ID Gene nameOrientation Clone ID Proposed mech. of action 2E3N10/NUR77SenseInhibition of survival signals 3E1RelASense Induction of NF  B responsive genes 4D8hTrb-1(hTrb-2, hTrb-3)Sense (3’ UTR)Inhibition of AP-1 activation 2B2 2G1 19-6.2 23-5.8 IFN  SenseInduces TRAIL expression which activates Both apoptosis and NF  B 19-3.7 Prothymosin  4 Antisense (full)Thymosin beta is an anti- inflammatory protein 83-3.8FerritinSense Redox effect on NF  B 91-2.8Mitochondrial NADPH Oxidase AntisenseRedox 53-1.7Homer1BSense 3’ UTRScaffold 126Genomic clone + est Summary of clones isolated from 3% of library

15. Adaptation of Mammalian Expression Screen to High throughput Robotic Platform

16. Output from High Throughput Screen showing Candidate Positive Pools - each Pool from 48 Library Wells 1.1/1000 clones regulate pIL8 2.pIL8 has 4 TF sites 3.1/4000 per site 4.3,000,000 clones in library 5.Each mRNA is represented by ca 10 clones 6.200 unique cDNAs per site 7.The signal transduction network is highly overlapping. 8.Few if any components will be unique to a gene, class of genes or agonist 9.Specifity is likely to be a higher order property - ie “words” rather than letters. 10.What is the word length ?

17. Conclusions /Speculations 1.Signal Transduction networks are likely binary 2.Data suggest ca 200 elements of network “control” a given transcription factor in the IL-8 promoter 3.Since each element is in turn coded by a gene ---- 4.Many elements must be used generically 5.The notion that there are dedicated/specfic pathways and pathway components cannot be correct 6.Programs that are specific may use generic elements by combining them into longer “words”.