Improving Boolean Networks to Model Signaling Pathways

Bree AldridgeDiana Chai

BE.400 Term ProjectDecember 5, 2002

OutlineMotivation / Project GoalsIntroduction to Model SystemImplementation:

Boolean networkFuzzy network

Results / ConclusionsFuture Work

MotivationCellular states control behaviorQuantitative signaling and state data difficult to obtain Boolean-like networks:

Representative of how signaling networks process and transmit information“Simpler” than solving a huge system of ODEsTool to explore subnetwork interactions (crosstalk)Missing data holes may be filled in with intuition

Project GoalsExplore the use of Boolean-like networks to model signaling eventsDetermine level of abstraction to which Boolean-like networks are usefulMake qualitative predictions about important nodes in signaling pathways

Insulin

Insulin Receptor

Fibronectin

1

IRS1

FAK/Src

Grb2

Sos

Ras

Raf

Mek

Erk

P13K

Akt/PKB

DNA Synthesis

Model System

Asthagiri and Lauffenburger, 2001Anabi et al., 2001

Transient Behavior

Asthagiri and Lauffenburger, 2001

DNA Synthesis

Asthagiri et.al., 2000

Pure Boolean Model

Pure Boolean Output

Fuzzified ModelGo to Simulink:

Introduction to fuzzy logicMembership functionsRule based logicShow working model

Results : Time course

Results: DNA Synthesis

Take-home ResultsFuzzy logic networks are capable of capturing qualitative features of signaling networks (e.g. crosstalk) Easy to build despite lack of quantitative informationGood for testing hypotheses at higher level of abstraction than ODE-based models

Crosstalk Example

ConclusionsBoolean Networks are NOT sufficient to capture complex behaviors of signalling networks where behavior is not ALL or NONEFuzzy Logic Networks are best used at the qualitative prediction levelAlso good for exploring how subnetworks interactEspecially good for when data is lacking

Future WorkExplore the insulin signaling pathwayExplore different levels of crosstalkExplore sensitivity by changing membership functions and weighting rules

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