A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

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.

Use of Computational Modeling to Study Joint Degeneration: A Review

“…Since these regulatory network graphs are sometimes constructed on the basis of experimental results published in literature (knowledge-based models), conflicting opinions in literature can lead to problems in decision making from the perspective of the model developer. Additionally, most of these (G) RN models are either qualitative or semi-quantitative in nature (Kerkhofs and Geris, 2015;Kerkhofs et al, 2016), which makes it difficult to be integrated with quantitative methods like the FEM. Also, the ability for quantitative model predictions is limited.…”

Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine

“…To date, very few in silico skeletal models have focused on the growth plate—especially at the intracellular level. Kerkhofs and coworkers implemented a series of models on the genetic switch between the chondrocyte's proliferative and hypertrophic state within the growth plate (Kerkhofs et al, 2012 , 2016 ; Kerkhofs, 2015 ; Kerkhofs and Geris, 2015 ), The control of this switch was studied both in the context of regenerative medicine and tissue engineering, and in the context of degenerative cartilage diseases (Melas et al, 2014 ). The model that we originally developed (Kerkhofs et al, 2012 ) was an additive, multi-valued, Boolean model representing the genetic switch from a SOX9 positive stable state to a RUNX2 positive stable state, being the hallmark of the proliferative and the hypertrophic state of the chondrocyte, respectively (see section Qualitative Models for formal definition of stable states).…”

In silico methods – Computational alternatives to animal testing

“…Liesbet Geris (University of Liège, KU Leuven, Belgium) showed translational applications of in silico methods when combined with different aspects of tissue engineering. She presented the modelling of differentiation processes of mesenchymal stem cells or chondrocytes based on gene regulation by using Boolean models (Kerkhofs and Geris, 2015;Kerkhofs et al, 2016) and explained that prediction models can support the development or validation of new carriers for bone formation (Kerckhofs et al, 2016) or support the design and optimization of cell culture bioreactors Papantoniou et al, 2014). The ongoing work and importance of the Virtual Physiological Human Initiative 1 towards individualized physiology-based computer simulations that aims to revolutionize human medicine was also presented.…”