The transition from vegetative to reproductive development is one of the most important phase changes in the plant life cycle. This step is controlled by various environmental signals that are integrated at the molecular level by so-called floral integrators. One such floral integrator in Arabidopsis (Arabidopsis thaliana) is the MADS domain transcription factor SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1). Despite extensive genetic studies, little is known about the transcriptional control of SOC1, and we are just starting to explore the network of genes under the direct control of SOC1 transcription factor complexes. Here, we show that several MADS domain proteins, including SOC1 heterodimers, are able to bind SOC1 regulatory sequences. Genome-wide target gene analysis by ChIP-seq confirmed the binding of SOC1 to its own locus and shows that it also binds to a plethora of flowering-time regulatory and floral homeotic genes. In turn, the encoded floral homeotic MADS domain proteins appear to bind SOC1 regulatory sequences. Subsequent in planta analyses revealed SOC1 repression by several floral homeotic MADS domain proteins, and we show that, mechanistically, this depends on the presence of the SOC1 protein. Together, our data show that SOC1 constitutes a major hub in the regulatory networks underlying floral timing and flower development and that these networks are composed of many positive and negative autoregulatory and feedback loops. The latter seems to be crucial for the generation of a robust flower-inducing signal, followed shortly after by repression of the SOC1 floral integrator.

Successful plant reproduction relies on the perfect orchestration of singular processes that culminate in the product of reproduction: the seed. The floral transition, floral organ development, and fertilization are well-studied processes and the genetic regulation of the various steps is being increasingly unveiled. Initially, based predominantly on genetic studies, the regulatory pathways were considered to be linear, but recent genome-wide analyses, using high-throughput technologies, have begun to reveal a different scenario. Complex gene regulatory networks underlie these processes, including transcription factors, microRNAs, movable factors, hormones, and chromatin-modifying proteins. Here we review recent progress in understanding the networks that control the major steps in plant reproduction, showing how new advances in experimental and computational technologies have been instrumental. As these recent discoveries were obtained using the model species Arabidopsis thaliana, we will restrict this review to regulatory networks in this important model species. However, more fragmentary information obtained from other species reveals that both the developmental processes and the underlying regulatory networks are largely conserved, making this review also of interest to those studying other plant species.

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.

Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible. In other words, a perfect solution for CMC would be a minimal set of motif pairs that describes the interaction behavior perfectly in the sense that two proteins from the network interact if and only if their sequences match a motif pair in the minimal set. In this paper, we introduce and formally define CMC and show that it is closely related to the red-blue set cover (RBSC) problem and its weighted version (WRBSC)--both well-known NP-hard problems for that there exist several algorithms with known approximation factor guarantees. We prove the hardness of approximation of CMC by providing an approximation factor preserving reduction from RBSC to CMC. We show the existence of a theoretical approximation algorithm for CMC by providing an approximation factor preserving reduction from CMC to WRBSC. We adapt the latter algorithm into a functional heuristic for CMC, called CMC-approx, and experimentally assess its performance and biological relevance. The implementation in Java can be found at >http://bioinformatics.uhasselt.be.