Abstract
BackgroundGene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis.ResultsBoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package.ConclusionsWe introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays.
Highlights
Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more
The Boolean Network (BN) model was introduced by Stuart Kauffman in a series of seminal papers [1,2,3]; see [4]
This is the p53-MDM2 negative feedback loop transcriptional circuit that is involved in DNA repair in the cell, and is an important tumor suppression agent [8]
Summary
Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. This is the p53-MDM2 negative feedback loop transcriptional circuit that is involved in DNA repair in the cell, and is an important tumor suppression agent [8]. From the pathway diagram, is is clear that MDM2 has an inhibiting effect on p53, which in turn activates it This p53-MDM2 negative-feedback regulatory loop keeps p53 at small expression levels under no stress, in which case all four proteins are inactivated in the steady state [8]. MDM2 is inhibited by ATM, which in turn is activated by the DNA damage signal, so that p53 is expected to display an oscillatory behavior under DNA damage [10] These behaviors are captured nicely by the BN model, as can be seen in the state transition diagram under no stress and under DNA damage, at the bottom of Fig. 1
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