1 / 17

Schematic of TIR signalling

Schematic of TIR signalling. 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

Download Presentation

Schematic of TIR signalling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Schematic of TIR signalling

  2. Cells as computational devices • Contains 1 copy of the genome • Contains ca 1010- 1011 protein molecules in • a volume of ca 1 picolitre • Contains ca 104 different proteins • present at104 - 107 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

  3. The Computational Network is Built from Noisy Components Nuclear Concentration of NFkB 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

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

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

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

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

  8. Positive pools are detectable by both screen strategies 0.9 0.8 0.7 TK-RL 0.6 Dual-Luciferase assay 0.5 0.4 0.3 0.2 0.1 0 0 0.2 0.4 0.6 0.8 IL8-luc positive pool negative pool 1 4 5 1 4 5 1 2 5 1 2 5 1 0 5 1 0 5 EGFP assay brightness 8 5 8 5 6 5 6 5 4 5 4 5 2 5 2 5 5 5 5 0 5 5 0 1 0 5 0 1 5 5 0 2 0 5 0 5 0 5 5 0 1 0 5 0 1 5 5 0 2 0 5 0 area area

  9. Summary of clones isolated from 3% of library

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

  11. Output from High Throughput Screen showing Candidate Positive Pools - each Pool from 48 Library Wells • 1/1000 clones regulate pIL8 • pIL8 has 4 TF sites • 1/4000 per site • 3,000,000 clones in library • Each mRNA is represented by ca 10 clones • 200 unique cDNAs per site • The signal transduction network is highly overlapping. • Few if any components will be unique to a gene , class of genes or agonist • Specifity is likely to be a higher order property - ie “words” rather than letters. • What is the word length ?

  12. Conclusions/Speculations • Signal Transduction networks are likely binary • 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 • The notion that there are dedicated/specfic pathways and pathway components cannot be correct • Programs that are specific may use generic elements by combining them into longer “words”.

More Related