A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis.We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis.Biologists have long been fascinated by the green plant leaf and have tried to understand how leaves are born, live and die. In the last decades, several new approaches to study the structure and function of leaves have emerged: Molecular biology and molecular genetics have, for example, enabled identification of genes that regulate the primary function of the leaf - photosynthesis - and leaf development has been understood in much greater detail; high through-put transcriptomics has identi