Boolean Network Model Predicts Knockout Mutant Phenotypes of Fission Yeast

Maria I Davidich,Stefan Bornholdt,Peter Csermely

doi:10.1371/journal.pone.0071786

Abstract

Boolean networks (or: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.

Highlights

Our ignorance of the functioning of the genome, despite knowing its complete DNA sequence, illustrates the enormous role of the — far less well characterized — multitude of biochemical interactions between the genes and within the living cell
While deciphering the structure of these control networks of the living cell is a central goal of modern biology, probably the most crucial part in decrypting the full functional role of the genome is the task of reconstructing their computational dynamics with the help of mathematical models [2]
We study the further capabilities of a Boolean network model reproducing the temporal activation pattern sequence of a wild type regulatory network, and ask whether it is capable of predicting the dynamical phenotype of a large set of mutated networks, as well

Summary

Introduction

Our ignorance of the functioning of the genome, despite knowing its complete DNA sequence, illustrates the enormous role of the — far less well characterized — multitude of biochemical interactions between the genes and within the living cell. ODE models are able to reproduce the complex biochemical kinetics of the central genes and proteins that make up the cell cycle control network. As an input, these models are based on the details of the biochemical interaction kinetics [6,7,8]. These models are based on the details of the biochemical interaction kinetics [6,7,8] By construction, this results in a rather complex mathematical model, even for the relatively small yeast cell cycle network. Considering the task of constructing much larger regulatory networks in the future, it is a valid question whether, in practice, the ODE-approach will scale well to much larger networks of hundreds of nodes, or whether ODE models could be accompanied by a class of simpler models

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Conclusion