A gene regulatory network is a central concept in Systems Biology. It links the expression levels of a set of genes via regulatory controls that gene products exert on one another. There have been numerous suggestions for models of gene regulatory networks , with varying degrees of expressivity and ease of analysis. Perhaps the simplest model is the Boolean network, introduced by Kauffman several decades ago: expression levels take a Boolean value, and regulation of expression is expressed by Boolean functions. Even for this simple formulation, the problem of fitting a given  model to an expression dataset is NP-Complete.  In this paper we introduce a novel algorithm for this problem that makes use of sampling in order to handle large datasets. In order to demonstrate its performance we test it on multiple large in-silico datasets with several levels and types of noise. Our results support the notion that network analysis is applicable to large datasets, and that the production of such datasets is desirable for the study of gene regulatory networks .
Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between detail and ease of analysis. However, even for Boolean networks the problem of fitting a given network model to an expression dataset is NP-Complete. Previous methods have addressed this issue heuristically or by focusing on acyclic networks and specific classes of regulation functions. In this paper we introduce a novel algorithm for this problem that makes use of sampling in order to handle large datasets. Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise .
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A gene regulatory network is a central concept in Systems Biology. It
Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN
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There have been numerous suggestions for models of gene regulatory networks , with varying degrees of expressivity
Boolean networks are a common modeling choice since they balance between detail
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Perhaps the simplest model is the Boolean network, introduced by Kauffman several decades ago: expression levels take a Boolean value, and regulation of expression is expressed by Boolean functions. Even for this simple formulation,
However, even for Boolean networks
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network
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Previous methods have addressed this issue heuristically or by focusing on acyclic networks and specific classes of regulation functions.
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In order to demonstrate its performance we test it on multiple large in-silico datasets with several levels and types of noise. Our results support the notion that network analysis is applicable to large datasets, and that the production of such datasets is desirable for the study of gene regulatory networks
Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise