You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Processing Oscillatory Signals by Incoherent Feedforward Loops}
Article:
Abstract:
From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop( IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose.
Introduction:
From Ca+2 signaling to coordination of cell fates, oscillatory signals are essential to regulation of cellular processes[1–4]. The dynamic properties of such signals are crucial for controlling behaviors of single cells and cell populations[5]. As such, the mechanisms underlying the generation of these signals are well-established[2, 6, 7]. For instance, the network constraints governing the circadian clock elucidate design principles dictating the generation of both natural and synthetic pulses[8–10]. Some general requirements for the generation of oscillations include ‘nonlinear’ reaction rates and negative feedback[9]. A systems-level approach to oscillation characterization examines the topologies in natural systems that give rise to pulse generation[9]. This demonstrates the necessity of ‘nonlinear’ kinetic rate laws for the destabilization of the steady state in the generation of oscillations[9]. While this constraint allows the generation of pulses with a diverse set of network motifs, negative feedback( especially negative feedback with a time delay) is found in all these cases. This component is used to reset the network to its initial state[2, 9]. Engineered systems based on such design constraints demonstrate the capability to generate synthetic oscillators mimicking those found in nature[6]. Even in the absence of any apparent regulation, transient oscillations in gene expression can emerge from cell-size control[11]. Despite the ubiquity of oscillations in biology, much less is known about how cells process these signals. In particular, how do cells distinguish between oscillatory and sustained inputs? For a given oscillatory input, how do cells retrieve encoded information from the frequency and amplitude? For signal processing in the frequency domain, computational methods illustrate one potential mechanism, where a critical frequency defines the bandwidth for high fidelity signal propagation for each network[3]. This capacity can be changed with an increased oscillation amplitude or with increased kinetic rates. Regardless of the strategies that give rise to signal encoding, it is important to further understand how cells process oscillatory signals. Many natural biological networks exhibit the ability to distinguish oscillatory and sustained signals. While several studies describe the contrasting downstream phenotypes, the architectures that give rise to such outcomes remain unclear. One common motif shared by such networks is the Incoherent Feed-Forward Loop( IFFL), in which an input both activates and represses a single output( Fig 1A)[4, 12, 13]. For example, oscillations in the transcription factor Ascl1 play a critical role in driving the proliferation of multipotent neural progenitor cells( NPCs)[14, 15]. In contrast, the sustained expression of Ascl1 promotes neuronal fate differentiation in NPCs[15, 16]. In social amoeba Dictyostelium discoideum, 3’ 5’-cyclic adenosine monophosphate( cAMP) oscillations result in the optimal gene expression for development while continuous stimulation inhibits transcription( Fig 1B)[17–19]. cAMP directly induces the expression of contact site A gene( csaA) while repressing the transcriptional activity of GtaC on csaA[17]. Additionally, the number of cAMP pulses regulates the coordination of development. An IFFL also underlies the regulation of neuronal regeneration by cAMP response element binding protein( CREB). CREB activates regeneration-associated genes( RAGs) and nuclear factor interleukin-3-regulated protein( NFIL3), which negatively regulates RAGs( Fig 1C)[20]. In Drosophila, the constitutive activation of CREB upregulates a single RAG, Arg1, while CREB oscillations induced by neuronal injury increase the transcription of all members[20, 21]. The IFFL motif is also found downstream of p53 in the regulation of cell fate decisions. The tumor suppressor p53 oscillates in response to γ-radiation, and the number of oscillations increase the level of damage. In contrast, UV radiation induction results in a sustained pulse of p53[22]. While p53 oscillations lead to cell cycle arrest, sustained p53 activation induces apoptosis[23, 24]. Here, p53 activates P53 upregulated modulator of apoptosis( PUMA), which plays a role in the induction of apoptosis, and Slug, a transcription factor which represses the activity of PUMA( Fig 1D)[25]. In mammalian cells, pulsatile dynamics of extracellular signal-regulated kinases( ERK) in response to epidermal growth factor( EGF) result in proliferation[26–28]. However, nerve growth factor( NGF) induces sustained ERK, driving differentiation[16]. Here, the transcription factor Myc and the cyclin-dependent kinase inhibitor p21 are both activated by ERK[29, 30]. Myc, an inducer of proliferation, represses the activity of p21, an inhibitor of cell cycle progression( Fig 1E)[31–35]. Tumor necrosis factor-α( TNFα) can stimulate oscillations in the transcription factor NF-κB, which in turn triggers nonspecific inflammatory response genes in immune cells[36]. In addition, persistence of the NF-κB oscillations dictates the resulting transcriptional profile[37, 38]. In contrast, bacterial liposaccharides( LPS) promote sustained NF-κB activation, which induces a LPS-specific expression program[39]. We identify a potential IFFL where NF-κB activates the expression of both IL8, a cytokine, and BCL3, a competitor for NF-κB target promoters including IL8( Fig 1F)[40]. The common occurrence of IFFLs in these networks suggests a role in processing transient or oscillatory signals. This motif is ideal for decoding oscillatory signals due to its ability to react to fold-changes rather than the absolute concentration of an input[12, 13, 41, 42]. In addition, IFFLs are one of the major core topologies that can drive temporal adaptation, the generation of a pulse in response to a sustained input[43]. In essence, this network is able to desensitize a system to a sustained stimulus while maintaining the ability to respond to periodic stimulation. This property is the fundamental principle behind the ability to differentiate pulsatile and sustained signals. Here, we examine a mechanism by which IFFLs process oscillations and the conditions that facilitate counting of pulses. This network exists in two states—one in which the system resets when the counting mechanism fails due to a conflict between the signal requirements and the actual input, and one where pulses result in a stepwise increase of the output. To differentiate between sustained and oscillatory signals, the network must display both states. The system must exhibit a stepwise increase of the output in response to oscillatory signals while having no response to sustained signals. Analogous to the use of radios to transform information from the frequency and amplitude domains of electromagnetic waves, the IFFL acts as a decoder, recognizing pulsatile signals.
Discussion:
Here, we analyze an underappreciated phenomenon in information processing by signaling networks: processing of oscillatory signals. In this context, we illustrate the unique ability of incoherent feedforward loops( IFFL) with proper network parameters to distinguish transient( or oscillatory) and sustained signals. For oscillatory signals, we analyze the extent to which the network is able to count the number of pulses. Additionally, we establish the dynamic constraints of the input signals and network parameters such that counting can occur. With the appropriate parameters, the ability of an IFFL motif to respond to transient signals but not to sustained signals relies upon the property of temporal adaptation in response to a sustained signal. Insights from our model can be used to identify natural information decoding systems and understand the effect of perturbations to such networks. Our results can be applied to GtaC shuttling in social amoeba, which differentiates between pulsing and sustained cAMP and matches optimal signals to the ideal transcript accumulation. Cai et. al. show that when the network regulating CsaA is subjected to high frequency inputs, GtaC is unable to respond to each pulse, resulting in a decreased transcript accumulation. Similarly, a lower than optimal( natural) cAMP frequency results in a decrease in the overall transcript concentration. This phenomenon suggests the presence of a band-pass filter. As in Fig 5, this system illustrates the idea that a decreased frequency( higher period) corresponds to a reduced fraction of the period during which counting can occur. Here, the parameters of the endogenous circuit dictate the range of frequencies that can be decoded resulting in dynamics similar to those explained by our IFFL model. In a similar manner, the frequency of ERK activation has been implicated in cell-fate determination. While previous studies have demonstrated the dependence of differential cell fates( differentiation or proliferation) on specific growth factors, Ryu et. al. illustrate that this decision relies more heavily on the frequency of ERK activity than on the growth-factor identity[49]. Unlike with the sustained application of growth factors, synthetic pulses provide control over the frequency of the ERK activity. This results in differentiation at intermediate frequencies and proliferation at high and low frequencies with a growth factor traditionally known to solely induce proliferation. While this shift in cell fates has been shown experimentally, the mechanisms underlying this frequency decoding are still unknown. Evolution may have selected for an optimal pulse frequency for social amoeba and in mammalian cell differentiation for different purposes. However, despite differences in signaling dynamics, the IFFL is a general motif found downstream of divergent signals in both cases. Generally, IFFLs are known for acting as sign-sensitive accelerators by increasing the response time of gene expression, dictated by the specific combination of kinetic parameters[13]. While some parameter sets allow the IFFL to act as pulse generators or band-pass filters, others speed up an output response[13, 50]. In addition, the IFFL has been shown to generate both time-dependent and dose-dependent biphasic responses[51]. Our analysis defines the quantitative constraints of another property of IFFLs, the capability for counting. In natural systems, motifs do not exist in isolation and the dynamics of a motif in isolation may not persist when placed in the context of a larger network. Despite this caveat, our analysis is valuable for two major reasons. First, we identify a new property of IFFLs—the potential for information processing. The importance of IFFLs in signal processing is implied with its presence in many natural networks that are able to respond differentially to oscillatory and sustained signals( Fig 1). Considering this caveat, we explore the extent by which the counting mechanism, can persist in the presence of different perturbations in S1 Text( e. g. introduction of time delay, alterations to the current architecture, and cellular noise). Second, our analysis is useful for guiding the design and implementation of synthetic gene circuits, which are often intended to operate in relative isolation from other regulatory networks in the host cell. Understanding the constraints of this network for decoding mechanisms is important in the design of synthetic circuits with more complex functions[52, 53]. The conclusions from our study can be used in the design of a synthetic pulse counter[54, 55]. Many synthetic gene circuits respond to the characteristics of static signals. In contrast, circuits adapted from our model would respond to the temporal dynamics of input signals. This would provide a wider range of input attributes, giving rise to a larger spectrum of potential circuit responses. In addition, by matching IFFL characteristics to natural cellular oscillations, synthetic signal decoding circuits would have the capability to respond to natural as well as synthetic stimuli. Natural systems including those involved in cell proliferation, cell death, and neural regeneration convert oscillatory and sustained signals into distinct biological outcomes. In the case of disease, these circuits become deregulated, altering the function of networks required for information encoding or decoding. Insights derived from our model for the pulse counting mechanism will be important for future work in understanding the ways in which these perturbations impact the way cells decode information.
From circadian clocks to ultradian rhythms , oscillatory signals are found ubiquitously in nature . These oscillations are crucial in the regulation of cellular processes . While the fundamental design principles underlying the generation of these oscillations are extensively studied , the mechanisms for decoding these signals are underappreciated . With implications in both the basic understanding of how cells process temporal signals and the design of synthetic systems , we use quantitative modeling to probe one mechanism , the counting of pulses . We demonstrate the capability of an Incoherent Feedforward Loop motif for the differentiation between sustained and oscillatory input signals .