Presentation is loading. Please wait.

Presentation is loading. Please wait.

Boolean Networks and Experiment Design B-Cell Single Ligand Screen Stuart Johnson Bioinformatics and Data Analysis Lab UCSD.

Published byGabriel Randall Modified over 8 years ago

Similar presentations

Presentation on theme: "Boolean Networks and Experiment Design B-Cell Single Ligand Screen Stuart Johnson Bioinformatics and Data Analysis Lab UCSD."— Presentation transcript:

1 Boolean Networks and Experiment Design B-Cell Single Ligand Screen Stuart Johnson Bioinformatics and Data Analysis Lab UCSD

2 Outline Why Boolean networks? Building/Displaying Boolean Networks Experiment design Procedure Some competing (sub)networks from the B-Cell data Conclusions

3 Why try Boolean Networks? Data noisy partial sampling Model Biochemical system lots of complexity predictive lots of meaning doable forward problem very difficult inverse problem

4 Why try Boolean Networks? Boolean data Boolean networks some complexity predictive (exp. design) data-like meaning? consistency = causality; should tell us about connectivity easy

5 Boolean data Experimental conditions TIME P-P, 2 nd Msg red=1 at 99% confidence: P(d=NC)<.01 blue=0 everything else 2 nd msg / co-sampled Ca

6 Boolean data Experimental conditions TIME P-P, 2 nd Msg red=1 at 99% confidence: P(d=NC)<.01 blue=0 everything else Phosphoproteins

7 Boolean data Experimental conditions TIME P-P, 2 nd Msg late resp. Ca -> PP early resp. Ca,cAMP -> No PP groups of siml. resp.

8 Boolean data Experimental conditions TIME P-P, 2 nd Msg Node = Full column of data; all exp. cond.

9 Known ligand/ receptor interactions from AfCS ligand descriptions Inputs, etc. Experimental Conditions Gq Single ligand screen inputs

10 ? Extracting patterns Ca (.5 min) ELC LPA AIG Experimental Conditions consistent?

11 Graph Time Experimental Conditions displaying and encoding patterns LPA 0 Ca 0.5 ER1 2.5 000 10? 011 110 Truth Table ERK1 (2.5 min)

12 all hypotheses: ER1(2.5 min) H1,H2 & H3: Early calcium is associated with ER1 H1: LPA is special (causes an early Ca signal but no ER1) H2: M3A is special (0.5 min Ca, no 1 min Ca, but ER1) H3: no special ligands, ER1 consistent with Ca & cAMP H1 H2 H3

13 Constructing complete networks I1I2I3 N1N2N3 573= 105 networks maximum xx Input nodes nodes with truth tables

14 Constructing complete networks I1I2I3 N1N2N3

15 Constructing complete networks I1I2I3 N1N2N3

16 Constructing complete networks I1I2I3 N1N2N3 “Feedback” not allowed! a completely determined network can have multiple output states; forward and inverse problems no longer “easy”

17 Experiment Design: networks reproduce results of completed experiments 1 output state All networks: 1 possible output state: For known inputs, every network simply reproduces results of completed experiments (Information) Entropy = score = 0

18 Experiment Design: networks are predictive 3 output states All networks: multiple possible output states: these multiple states correspond to unknown entries (?) in truth tables and the different connectivity of the networks Entropy = score > 0

19 Dual-ligand experiment design ligand 2 ligand 1 entropy score

20 Dual-ligand experiment design ligand 2 ligand 1 entropy score ELC + LPA

21 Procedure Build Boolean Networks Do Experiments Display Boolean Networks Score class of experiments pick highest scoring exp.

22 Controlling Complexity: Constraint Graphs Graphs specify allowable inputs and hops RCP LIG 2M PP LIG 2M PP LIG 2M PP 1 RCP LIG 2M PP 1

23 Graphs specify allowable inputs and hops RCP LIG 2M PP LIG 2M PP LIG 2M PP 1 RCP LIG 2M PP 1 Controlling Complexity: Constraint Graphs

24 RCP LIG 2M PP 1 Network display All node rules Can filter/cluster/display these rules to see: ligand classification (chemokines, cytokines, etc) clusters of similar control patterns etc. - “pathways”

25 LIG 2M PP Early Calcium vs... Early Calcium + cAMP

26 ER1 -> ER2,P90 LIG 2M PP 1 P90 -> AKT ST6 -> ST3

27 RCP LIG 2M PP Early Ca & Gq control vs... Early Ca & G12

28 Conclusions This is a general method/implementation and will extend to the RAW screens and FXM in some form Boolean network analysis has many interesting features: –learns from experiments/proposes new exp. –formalizes inclusion of known information as either constraint graphs or hidden nodes –caveat 1: the BN have encoded any real meaning –caveat 2: you can control complexity and digest the networks inferred http://dev.afcs.org:12057/ for the latest results, navigable/clickable networks and more backgroundhttp://dev.afcs.org:12057/

Download ppt "Boolean Networks and Experiment Design B-Cell Single Ligand Screen Stuart Johnson Bioinformatics and Data Analysis Lab UCSD."

Similar presentations

5 min for questions Alliance for Cellular Signaling Pasadena, CA May 19, 2003Monday Morning Single and Double Ligand Screens 9:00 Rama Ranganathan Single.

5 min for questions Alliance for Cellular Signaling Pasadena, CA May 19, 2003Monday Morning Single and Double Ligand Screens 9:00 Rama Ranganathan Single.
Slides from: Doug Gray, David Poole

Slides from: Doug Gray, David Poole
Feature Selection as Relevant Information Encoding Naftali Tishby School of Computer Science and Engineering The Hebrew University, Jerusalem, Israel NIPS.

Feature Selection as Relevant Information Encoding Naftali Tishby School of Computer Science and Engineering The Hebrew University, Jerusalem, Israel NIPS.
BAYESIAN NETWORKS. Bayesian Network Motivation  We want a representation and reasoning system that is based on conditional independence  Compact yet.

BAYESIAN NETWORKS. Bayesian Network Motivation  We want a representation and reasoning system that is based on conditional independence  Compact yet.
CSCI 347 / CS 4206: Data Mining Module 07: Implementations Topic 03: Linear Models.

CSCI 347 / CS 4206: Data Mining Module 07: Implementations Topic 03: Linear Models.
D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April 3 2013 Seminar.

D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April Seminar.
1 Fast Asymmetric Learning for Cascade Face Detection Jiaxin Wu, and Charles Brubaker IEEE PAMI, 2008 Chun-Hao Chang 張峻豪 2009/12/01.

1 Fast Asymmetric Learning for Cascade Face Detection Jiaxin Wu, and Charles Brubaker IEEE PAMI, 2008 Chun-Hao Chang 張峻豪 2009/12/01.
Decomposition of overlapping protein complexes: A graph theoretical method for analyzing static and dynamic protein associations Algorithms for Molecular.

Decomposition of overlapping protein complexes: A graph theoretical method for analyzing static and dynamic protein associations Algorithms for Molecular.
衛資所 生物資訊組 陳俊宇 April 07, 03. graph nodeedge Chromosomegenepositional correlations Pathwayenzymefunctional correlations Gene expression genecoexpressed.

衛資所 生物資訊組 陳俊宇 April 07, 03. graph nodeedge Chromosomegenepositional correlations Pathwayenzymefunctional correlations Gene expression genecoexpressed.
An Illustrative Example

An Illustrative Example
Before we start ADALINE

Before we start ADALINE
Inverses of Trigonometric Functions. The Sine Function Graph Domain: Range: All Reals -1≤y≤1 The Sine graph is a function (one output for each input).

Inverses of Trigonometric Functions. The Sine Function Graph Domain: Range: All Reals -1≤y≤1 The Sine graph is a function (one output for each input).
Www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms Clustering and Microarray Analysis.

Introduction to Bioinformatics Algorithms Clustering and Microarray Analysis.
Unit Four - Functions 8.F.1 Understand that a function is a rule that assigns exactly one output to each input. The graph of a function is the set of ordered.

Unit Four - Functions 8.F.1 Understand that a function is a rule that assigns exactly one output to each input. The graph of a function is the set of ordered.
Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data Ioannis N. Melas, Alexander Mitsos, Dimitris.

Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data Ioannis N. Melas, Alexander Mitsos, Dimitris.
IP Addressing & Subnetting Made Easy. Part 1: Working with IP Addresses.

IP Addressing & Subnetting Made Easy. Part 1: Working with IP Addresses.
Outline Decoder Encoder Mux. Decoder Accepts a value and decodes it Output corresponds to value of n inputs Consists of: Inputs (n) Outputs (2 n, numbered.

Outline Decoder Encoder Mux. Decoder Accepts a value and decodes it Output corresponds to value of n inputs Consists of: Inputs (n) Outputs (2 n, numbered.
Identify an Unknown Substance Presented by:. Introduction Physical Properties are Chemical properties are Picture of your substance.

Identify an Unknown Substance Presented by:. Introduction Physical Properties are Chemical properties are Picture of your substance.
Genetic network inference: from co-expression clustering to reverse engineering Patrik D’haeseleer,Shoudan Liang and Roland Somogyi.

Genetic network inference: from co-expression clustering to reverse engineering Patrik D’haeseleer,Shoudan Liang and Roland Somogyi.
Genetic Regulatory Network Inference Russell Schwartz Department of Biological Sciences Carnegie Mellon University.

Genetic Regulatory Network Inference Russell Schwartz Department of Biological Sciences Carnegie Mellon University.

Similar presentations

Ads by Google