Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of regulatory control. Gene regulation, however, is a process that depends on many different components such as transcription factors (TFs), cis-regulatory motifs and their temporal and spatial coordination. Accordingly, a promising new direction for computational analysis is the incorporation of multiple data types to discover, for instance, cluster membership, the spatial organization of cis-regulatory motifs and TFs that bind to these motifs. Here, we present such a data-driven framework, comprising four stages, to infer gene regulatory networks (GRNs) by modeling: 1. motif presence in the promoter, 2. spatial motif arrangement in co-regulated genes, 3. TFs that bind the respective motifs, and 4. dynamic properties of the GRN. A novel method is presented in stage 2, where we optimize for the spatial motif properties: orientation, occurrence of multiple motifs, relative distance between two motifs and distance to the Transcription Start Site (TSS). To find optimal distance based properties in efficient time we describe a dynamic programming approach. To combine multiple motif properties that are shared by genes with similar expression profiles a Hill-climber is employed. Subsequently, in stage 3 and 4, we infer GRNs by assigning TFs to the derived motifs and model time-dependent regulatory relationships between them with the Inferelator approach. None of the stages require the user to manually adjust any parameter, and thus derived properties can be analyzed without the bias introduced by parametrization. We applied this approach to 5. cerevisiae data and obtained insight into individual and general properties of the spatial assembly of regulatory elements and inferred the corresponding GRN.