Abstract

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour.

Highlights

  • As molecular biology gradually shifted away from its reductionist framework towards integrative thinking and helped spawn the field of systems biology, network modelling gained more and more thrust as a pivot to formally tackle the complexity of biological systems [1]

  • This study highlights that extra features added to the Boolean framework give rise to a broader scope of model behaviour

  • The extended scope must be balanced with limited availability of quantitative information, as models may keep to behaviour that the Boolean framework can capture as well

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Summary

A Semiquantitative Framework for Gene Regulatory Networks

With the exponentially increasing number of input combinations it becomes progressively limiting to mould all output into all or nothing responses Another issue arising in Boolean modelling is that the updating scheme often disregards qualitative information on time aspects that is intrinsic to the type of interaction between two nodes. Given that this type of information is readily available, it may prove advantageous to incorporate in the gene network model, albeit at the cost of increased complexity.

Introduction
Materials and Methods
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