Abstract
The goal of systems biology is to generate models for predicting how a system will react under untested conditions or in response to genetic perturbations. This paper discusses experimental and analytical approaches to deriving causal relationships in gene regulatory networks.
Highlights
Owing to their sessile mode of life, plants are subject to drastic variations in their environment that lead to rapid adaptation of their gene expression states resulting from their complex gene-regulatory networks
The ultimate goal in plant systems biology is to infer, for both scientific and practical gain, how such regulatory networks will respond under untested conditions
We use the term generegulatory network (GRN) to refer to the set of transcriptional interactions between transcription factors (TFs) and their targets, as opposed to a multimodal set of gene-to-gene or gene-to-metabolite interactions
Summary
Owing to their sessile mode of life, plants are subject to drastic variations in their environment that lead to rapid adaptation of their gene expression states resulting from their complex gene-regulatory networks. More recent studies have taken advantage of the wealth of interaction and expression data available in public databases to construct extensive [13] and con densed [14] models of GRNs involved in floral develop ment, resulting in time-evolving molecular regulatory networks for the development of sepal primordia [13] as well as for floral transition [14] These few examples of successful Strong Prior approaches demonstrate that GRNs confer robust emer gent properties supporting developmental or environ mental adaptations. State-space modeling is a modern machine-learning technique devoted to detecting causality in networks by inferring ordinary differential equations specifying the relation ships among genes in those networks while avoiding over-fitting In plants, this technique has been applied to probe GRNs involved in leaf senescence [31] and GRNs involved in regulating early, time-dependent transcrip tional responses to NO3- [32].
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