Project Details
Projekt
Identification of Causal Dependences in Gene Regulatory Networks using Algorithmic Information Theory
Subject Area
Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
Term
from 2010 to 2014
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 150509111
This project aims at analyzing the causal structure of genetic regulatory networks of stem cells of plants using novel causal inference techniques to be developed here. Known methods for causal inference from statistical data usually require a large number of samples. Our preliminary work shows that it is in principle possible to infer causal relations from sample size one if the variables are high-dimensional, since algorithmic information provides additional hints on causal directions. Since algorithmic information is uncomputable, computable non-statistical dependence measures need to be developed. The results achieved during the first period of this projects have shown that free probability theory can successfully be applied to causal inference in high dimensions with small sample size. Recent advances in genomic methods have allowed the simultaneous quantification of all genes in an organism. To identify the causal relation between individual transcripts, we will use inducible expression to analyze the effect of the homeodomain transcription factor WUSCHEL on the regulatory network of plant stem cell control. After appropriate clustering of the genes, we obtain a causal network between extremely high-dimensional variables, to which algorithmic information theory based methods can be applied. The inferred causal relation will then be tested by advanced experiments.
DFG Programme
Priority Programmes
