Descriptive vs. Mechanistic Network Models in Plant Development in the Post-Genomic Era

Abstract

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

The flowering transition pathways converge into a complex gene regulatory network that underlies the phase changes of the shoot apical meristem in Arabidopsis thaliana

“…Systems biology approaches are required to provide mechanistic understanding of development ( Albert and Thakar, 2014 ; Davila-Velderrain et al, 2015a ). In this work, we have used a GRN Boolean model to address the system-level mechanisms underlying A. thaliana reproductive development.…”

The impact of Polycomb group (PcG) and Trithorax group (TrxG) epigenetic factors in plant plasticity

“…Such models have mostly considered transcriptional regulators, while the epigenetic components that are the important links between such GRNs and nongenetic components are still to be integrated. Consideration of nongenetic components and epigenetic mechanisms in a mechanistic system‐level approach is required to consolidate studies aimed at understanding the influence of environment during plant development (Davila‐Velderrain et al ., , ). At the same time, such approaches will provide a powerful framework for understanding how epigenetic variation among different populations and under contrasting environmental conditions emerges, and what its role is in plant plasticity, adaptation and evolution.…”

Methods for Characterizing the Epigenetic Attractors Landscape Associated with Boolean Gene Regulatory Networks

“…The postulation of experimentally grounded gene regulatory network (GRN) dynamical models, their qualitative analysis and dynamical characterization in terms of control parameters, and the validation of GRN predictions against experimental observations has become a well-established framework in systems biology -see, for example: [1,2,3,4]. There are multiple tools available for the straightforward implementation and analysis of dynamical models of GRNs [5].…”