'Boolean networks' diaporamas de présentation

One Network, Different Models

Networks in Metabolism and Signaling Edda Klipp Humboldt University Berlin Lecture 4 / WS 2007/08 Boolean Networks. gene a. gene b. gene c. gene d. C. A. D. B. A. B. +. +. One Network, Different Models. transcription. translation. repression. activation. gene. protein.

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Hyper computation

Hyper computation. Introduction & Philosophy. Preface. Jeroen Broekhuizen History before Hyper computing Christian Gilissen Introduction & philosophy of Hyper computing Maurice Samulski Hyper computing by examples. Alan Turing. Well known

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Topics

Topics. Logic synthesis. Placement and routing. Logic optimization. Logic synthesis programs transform Boolean expressions into logic gate networks in a particular library. Optimization goals: minimize area, meet delay constraint; some power optimizations. Syntax-directed translation.

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V11 Modelling genetic networks by boolean networks

V11 Modelling genetic networks by boolean networks. Methods to describe genetic networks: (1) boolean networks (today) (2) clustering gene expression data ( ? Bioinformatics II lecture) Clustering is a relatively easy way to extract useful information out of

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ECEN 4703 Switching and Finite Automata Theory

ECEN 4703 Switching and Finite Automata Theory. Sunil P Khatri University of Colorado, Boulder Spring 2002 Modified from lecture notes of Robert K Brayton, University of California, Berkeley. Introduction. Motivation for Logic Synthesis

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Modeling and identification of biological networks

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A Case for Using Signal Transition Graphs for Analysing and Refining Genetic Networks

A Case for Using Signal Transition Graphs for Analysing and Refining Genetic Networks. Richard Banks , Victor Khomenko and Jason Steggles School of Computing Science, Newcastle University, UK. Overview. Modelling genetic regulatory networks using Boolean networks (BNs) .

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A Case for Using Signal Transition Graphs for Analysing and Refining Genetic Networks

A Case for Using Signal Transition Graphs for Analysing and Refining Genetic Networks. Richard Banks , Victor Khomenko and Jason Steggles School of Computing Science, Newcastle University, UK. Overview. Modelling genetic regulatory networks using Boolean networks (BNs) .

383 views • 38 slides

Implication Networks from Large Gene-expression Datasets

Debashis Sahoo PhD Candidate, Electrical Engineering, Stanford University Joint work with David Dill, Andrew Gentles, Rob Tibshirani, Sylvia Plevritis. Implication Networks from Large Gene-expression Datasets. Integrative Cancer Biology Program, Stanford University. Motivation.

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Discrete models of biochemical networks Algebraic Biology 2007 RISC Linz, Austria July 3 , 2007

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Recent Results on Ordering Parameters in Cellular Automata and Boolean Networks

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Improving Boolean Networks to Model Signaling Pathways

Improving Boolean Networks to Model Signaling Pathways. Bree Aldridge Diana Chai BE.400 Term Project December 5, 2002. Outline. Motivation / Project Goals Introduction to Model System Implementation: Boolean network Fuzzy network Results / Conclusions Future Work. Motivation.

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Summary

promoter. gene. p 2. B 1. a 12 , d 12. determine transcription rate. p 3. a 23 , d 23. F k. B 2. p k. a 24 , d 24. p 4. Expression = F k (Q 1 , Q 2 , Q 3 ) , where Q i is state of binding site B i. attach/detach. influence. p 1. B 3. a 31 , d 31. Legend:. p j. B i.

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