## 1 Introduction  
Synthetic biology aims at engineering programmable, higher-order, functional entities by designing, assembling, and characterizing biological parts, devices, and circuits into living cells [1]. Pre-existing organisms are re-engineered into new, purpose-driven ones that present a tremendous potential to address a number of demands ranging from environmental care to personalized medicine. Successful engineering of a new organism containing a predefined function depends on the incorporation of orthogonal synthetic genetic circuits [2]. They are organized into transcription units (TUs) that are usually placed onto a variable number of plasmids and encode for proteins or RNA sequences.  
Proteins are essential subunits for the survival of an individual cell. Their expression is determined by and performed through coordinated molecular interactions. Furthermore, proteins enable cells to cope with internal and external stimuli. For instance, a complex gene regulation occurs in _Escherichia coli_ when the growth medium changes. If the glucose level becomes low, _E. coli_ switches to lactose as an alternative source of energy via the inducible _lac_ operon [3]. This shift in the carbon source prevents any interruptions in protein synthesis and allows the cells to save energy [4]. Thus, transcription networks operate as a global internal processor that decides the nature and the rate at which proteins (and other molecules) shall be produced [5].  
Most biological networks are sparse networks, i.e., their interaction density (or _connectivity_) is low. This could be due to evolutionary selection such that only useful interactions are maintained [6]. _Sparseness_ in networks was first introduced by [PERSON] and [PERSON] [7] in a systematic study of random graphs [8]. Cellular information-processing interactions are complex and form multilayered connections that influence each other. Among multilayered networks of interactions, are transcriptional regulatory networks (TRNs) that play a key role in cellular signaling, gene expression, metabolism, and protein modifications. Ultimately, TRNs dictate what proteins (or other molecules) shall be expressed in the cells per unit of time [9]. [PERSON] and [PERSON] [10] proposed an approach to dissect complex transcriptional networks by analyzing the basic functional patterns they are made of. They discovered that some architectures, referred to as _network motifs_, appear more frequently than predicted if the edges (i.e., the connections between two nodes) were randomly distributed. This result was confirmed by studies on neuronal networks [11].  
Network motifs were first identified in _E. coli_ and later searched for in other organisms, both prokaryotic and eukaryotic [12]. However, studies in high eukaryotes are still in their infancy. Network motifs broadly cluster into two subset of TRNs: sensory and developmental networks [12]. Sensory networks are responsible for reversible decisions in case of (harsh) environmental stimuli such as stress and nutrient insufficiency. In contrast, developmental networks guide differentiation and cell fate events that usually take place over several cell generations. Common motifs in sensory networks are simple regulation [13], feedback loops [14], single-input modules (SIM) [15], and dense overlapping regulons (DOR) [16]. Feedforward loops (FFLs) [17], transcriptional cascade [18], and interlocked FFLs [19] are predominant motifs in developmental networks. They help the cells make the right decisions under precise conditions.  
In the following, we will focus our discussion on FFLs since they are the most abundant motif in natural TRNs. Moreover, _canonical_ FFLs and variants of FFLs have been designed and implemented in vitro and in vivo either independently or as components of larger synthetic gene circuits.