Multistep Signaling Research Articles

Reviewing mathematical models of sperm signaling networks.

Dave Garbers' work significantly contributed to our understanding of sperm's regulated motility, capacitation, and the acrosome reaction. These key sperm functions involve complex multistep signaling pathways engaging numerous finely orchestrated elements. Despite significant progress, many parameters and interactions among these elements remain elusive. Mathematical modeling emerges as a potent tool to study sperm physiology, providing a framework to integrate experimental results and capture functional dynamics considering biochemical, biophysical, and cellular elements. Depending on research objectives, different modeling strategies, broadly categorized into continuous and discrete approaches, reveal valuable insights into cell function. These models allow the exploration of hypotheses regarding molecules, conditions, and pathways, whenever they become challenging to evaluate experimentally. This review presents an overview of current theoretical and experimental efforts to understand sperm motility regulation, capacitation, and the acrosome reaction. We discuss the strengths and weaknesses of different modeling strategies and highlight key findings and unresolved questions. Notable discoveries include the importance of specific ion channels, the role of intracellular molecular heterogeneity in capacitation and the acrosome reaction, and the impact of pH changes on acrosomal exocytosis. Ultimately, this review underscores the crucial importance of mathematical frameworks in advancing our understanding of sperm physiology and guiding future experimental investigations.

Pneumatic servo position control optimization using adaptive-domain prescribed performance control with evolutionary mating algorithm

Pneumatic servo systems face challenges such as friction, compressibility, and nonlinear dynamics, necessitating advanced control techniques. Research suggests model-based, model-free, hybrid, and optimization-based methods have their strengths. Therefore, this study presents an optimal control strategy using Adaptive Domain Prescribed Performance Control (AD-PPC) cascaded with PID and optimized using the Evolutionary Mating Algorithm (EMA) for a pneumatic servo system (PSS). The goal is to achieve faster transient control and stable rod-piston positioning with minimal friction through the hysteresis phenomenon of the targeted proportional valve-controlled double-acting pneumatic cylinder (PPVDC) representing the PSS. The novel EMA optimizes the cascaded controller based on the tracking error as its objective function. Simulation studies verify the proposed AD-PPC-PID controller with the PPVDC model plant, iteratively optimized by the EMA. The analytical study compares this setup's control system and optimization model with the same control system model using alternative optimization methods. The testing employs step and multi-step signals for PPVDC's rod-piston position input. Results show that the EMA-tuned AD-PPC-PID outperforms AD-PPC-PID controller with other optimizers. For both input trajectory tests, EMA-tuned AD-PPC-PID shows faster response times, with average improvements of 30 % in settling times and 70 % in tracking performance metrics compared to other optimizers, making it robust for nonlinear system applications like PPVDC rod-piston positioning.

Intelligent Cell Profiling and Precision Release: Multimolecular Marker-Activated Transmembrane DNA Computing Nanosystem.

Accurate classification of tumor cells is of importance for cancer diagnosis and further therapy. In this study, we develop multimolecular marker-activated transmembrane DNA computing systems (MTD). Employing the cell membrane as a native gate, the MTD system enables direct signal output following simple spatial events of "transmembrane" and "in-cell target encounter", bypassing the need of multistep signal conversion. The MTD system comprises two intelligent nanorobots capable of independently sensing three molecular markers (MUC1, EpCAM, and miR-21), resulting in comprehensive analysis. Our AND-AND logic-gated system (MTDAND-AND) demonstrates exceptional specificity, allowing targeted release of drug-DNA specifically in MCF-7 cells. Furthermore, the transformed OR-AND logic-gated system (MTDOR-AND) exhibits broader adaptability, facilitating the release of drug-DNA in three positive cancer cell lines (MCF-7, HeLa, and HepG2). Importantly, MTDAND-AND and MTDOR-AND, while possessing distinct personalized therapeutic potential, share the ability of outputting three imaging signals without any intermediate conversion steps. This feature ensures precise classification cross diverse cells (MCF-7, HeLa, HepG2, and MCF-10A), even in mixed populations. This study provides a straightforward yet effective solution to augment the versatility and precision of DNA computing systems, advancing their potential applications in biomedical diagnostic and therapeutic research.

Development of antibody-aptamer sandwich-like immunosensor based on RCA and Nicked-PAM CRISPR/Cas12a system for the ultra-sensitive detection of a biomarker

Biomarkers are the most sensitive reactants and early indicators of many kinds of diseases. The development of highly sensitive and simple techniques to quantify them is challenging. In this study, based on rolling cycle amplification (RCA) and the Nicked PAM/CRISPR-Cas12a system (RNPC) as a signal reporter, a sandwich-type method was developed using antibody@magnetic beads and aptamer for the high-sensitive detection of the C-reactive protein (CRP). The antibody-antigen (target)-aptamer sandwich-like reaction was coupled to RCA, which can produce hundreds of similar binding sites and are discriminated by CRISPR/Cas12a for signal amplification. The ultrasensitivity is achieved based on the dual-signal enhancing strategy, which involves the special recognition of aptamers, RCA, and trans-cleavage of CRISPR/Cas12a. By incorporating the CRISPR/Cas12a system with cleaved PAM, the nonspecific amplification of the RCA reaction alone was greatly reduced, and the dual signal output of RCA and Cas12a improved the detection sensitivity. Our assay can be performed only in two steps. The first step takes only 20 min of target capture, followed by a one-pot reaction, where the target concentration can be obtained by fluorescence values as long as there are 37 °C reaction conditions. Under optimal conditions, this system detected CRP with high sensitivity. The fabricated biosensor showed detection limits of 0.40 pg/mL in phosphate-buffered saline and 0.73 pg/mL in diluted human serum and a broad linear dynamic range of 1.28 pg/mL to 100 ng/mL within a total readout time of 90 min. The method could be used to perform multi-step signal amplification, which can help in the ultrasensitive detection of other proteins. Overall, the proposed biosensor might be used as an immunosensor biosensor platform.

The Potential Role of Epigallocatechin-3-Gallate (EGCG) in Breast Cancer Treatment

Breast cancer is one of the most diagnosed cancers worldwide, with an incidence of 47.8%. Its treatment includes surgery, radiotherapy, chemotherapy, and antibodies giving a mortality of 13.6%. Breast tumor development is driven by a variety of signaling pathways with high heterogeneity of surface receptors, which makes treatment difficult. Epigallocatechin-3-gallate (EGCG) is a natural polyphenol isolated as the main component in green tea; it has shown multiple beneficial effects in breast cancer, controlling proliferation, invasion, apoptosis, inflammation, and demethylation of DNA. These properties were proved in vitro and in vivo together with synergistic effects in combination with traditional chemotherapy, increasing the effectiveness of the treatment. This review focuses on the effects of EGCG on the functional capabilities acquired by breast tumor cells during its multistep development, the molecular and signal pathways involved, the synergistic effects in combination with current drugs, and how nanomaterials can improve its bioavailability on breast cancer treatment.

Autophagy modulation in breast cancer utilizing nanomaterials and nanoparticles.

Autophagy regenerates cellular nutrients, recycles metabolites, and maintains hemostasis through multistep signaling pathways, in conjunction with lysosomal degradation mechanisms. In tumor cells, autophagy has been shown to play a dual role as both tumor suppressor and tumor promoter, leading to the discovery of new therapeutic strategies for cancer. Therefore, regulation of autophagy is essential during cancer progression. In this regard, the use of nanoparticles (NPs) is a promising technique in the clinic to modulate autophagy pathways. Here, we summarized the importance of breast cancer worldwide, and we discussed its classification, current treatment strategies, and the strengths and weaknesses of available treatments. We have also described the application of NPs and nanocarriers (NCs) in breast cancer treatment and their capability to modulate autophagy. Then the advantages and disadvantaged of NPs in cancer therapy along with future applications will be disscussed. The purpose of this review is to provide up-to-date information on NPs used in breast cancer treatment and their impacts on autophagy pathways for researchers.

Deep Learning for Reliable Communication Optimization on Autonomous Vehicles

Recent breakthroughs in the autonomous vehicle industry have brought this technology closer to consumers. However, the cost of self-driving solutions still constitutes an entry barrier to many potential users due to its reliance on powerful onboard computers. As an alternative, autonomous driving algorithm processing may be offloaded to remote machines, which requires a reliable connection to the cloud servers. However, despite significant 5G coverage in many countries, mobile network reliability and latency are still inadequate for this purpose. This work explores deep learning concepts to forecast signal quality as a vehicle moves, predicting when periods of degraded network quality will occur. We develop a Long Short-Term Memory (LSTM)-based neural network, trained on multivariate time series containing historical data on several mobile network parameters, and evaluate the results of multi-step Reference Signal Received Power (RSRP) prediction. Results show that our model achieves a rapidly increasing Root-Mean-Square Error (RMSE), reaching over 8 dBm after 25-time steps. This error does not allow for the accurate prediction of future signal quality.

Yogurt classification using an electronic tongue system and machine learning techniques

• The development of a pattern recognition methodology for the classification of yogurts. • The use of a dataset of yogurts from the region of Boyacá-Colombia. • The development of an electronic tongue system for the classification of yogurt Electronic tongue sensor arrays allow the analysis of liquid substances and provide information for classification tasks to artificially emulate a taste recognition system. These kinds of devices are needed for the alimentary industry because they ensure their products’ quality; however, they are still in the development phase because of the components required for their robustness. This paper presents an electronic tongue system for yogurt classification to contribute to developing automatic systems for this industry. The developed system uses a sensor array with Screen-Printed Electrode sensors, a potentiostat, and a multiplexer to handle eight different sensors. A pattern recognition methodology for the processing of multi-step amperometry signals is defined. It comprises the following stages: First, a data unfolding process to build a two-dimensional matrix with the information from the sensors is applied. Next, raw data is normalized using the mean-centered group scaling method. Then, a dimensionality reduction stage is applied. Two methods are evaluated, the t-distributed stochastic neighbor embedding ( t -SNE) and Principal Component Analysis (PCA). Subsequently, a supervised machine learning classification algorithm is used. Five methods were evaluated to determine the best classifier: LDA, classification trees(C4.5), naive Bayes, k -NN, and SVM. A dataset of 151 samples of 8 different yogurts is used for the methodology validation. This validation is executed through a Leave One Out Cross Validation (LOOCV) method. Final classification accuracy of 100% was obtained When PCA and LDA or t -SNE and k -NN were used; however, from the computational point of view, a shorter execution time was evidenced for PCA and LDA, showing the convenience in its use in the methodology.

The histidine phosphotransfer AHP4 plays a negative role in Arabidopsis plant response to drought

Cytokinin plays an important role in plant stress responses via a multistep signaling pathway, involving the histidine phosphotransfer proteins (HPs). In Arabidopsis thaliana, the AHP2, AHP3 and AHP5 proteins are known to affect drought responses; however, the role of AHP4 in drought adaptation remains undetermined. In the present study, using a loss-of-function approach we showed that AHP4 possesses an important role in the response of Arabidopsis to drought. This is evidenced by the higher survival rates of ahp4 than wild-type (WT) plants under drought conditions, which is accompanied by the downregulated AHP4 expression in WT during periods of dehydration. Comparative transcriptome analysis of ahp4 and WT plants revealed AHP4-mediated expression of several dehydration- and/or abscisic acid-responsive genes involved in modulation of various physiological and biochemical processes important for plant drought acclimation. In comparison with WT, ahp4 plants showed increased wax crystal accumulation in stems, thicker cuticles in leaves, greater sensitivity to exogenous abscisic acid at germination, narrow stomatal apertures, heightened leaf temperatures during dehydration, and longer root length under osmotic stress. In addition, ahp4 plants showed greater photosynthetic efficiency, lower levels of reactive oxygen species, reduced electrolyte leakage and lipid peroxidation, and increased anthocyanin contents under drought, when compared with WT. These differences displayed in ahp4 plants are likely due to upregulation of genes that encode enzymes involved in reactive oxygen species scavenging and non-enzymatic antioxidant metabolism. Overall, our findings suggest that AHP4 plays a crucial role in plant drought adaptation.

Delay Compensation Control Strategy for Electric Vehicle Participating in Frequency Regulation Based on MPC Algorithm

With the continuous increase of renewable energy, the power system will face the problem of insufficient frequency response capability. Electric vehicles (EVs) combine the characteristics of mobile energy storage and controllable loads and can provide capacity support in frequency control. However, as a distributed frequency regulation resource, there is inevitably a communication delay in the process of electric vehicles participating in frequency regulation, which will affect the stability of the system frequency. Therefore, this paper proposes a frequency regulation delay compensation control strategy based on model predictive control (MPC). Firstly, the model predictive control is utilized to design an electric vehicle frequency response model controller and establish a frequency regulation model of the power system containing electric vehicles; then, by establishing the selection rules of the control signal, the multi-step control signal is dynamically adjusted during the rolling optimization of the predictive model to form a delay compensation mechanism considering delay and packet loss; finally, multi-scenario simulation is conducted, and the results show that the proposed strategy can roll-optimize the auxiliary frequency regulation function of electric vehicles, significantly reduce the influence of communication time delay, and improve the system frequency stability.

A DNA-Micropatterned Surface for Propagating Biomolecular Signals by Positional on-off Assembly of Catalytic Nanocompartments.

Signal transduction is pivotal for the transfer of information between and within living cells. The composition and spatial organization of specified compartments are key to propagating soluble signals. Here, a high-throughput platform mimicking multistep signal transduction which is based on a geometrically defined array of immobilized catalytic nanocompartments (CNCs) that consist of distinct polymeric nanoassemblies encapsulating enzymes and DNA or enzymes alone is presented. The dual role of single entities or tandem CNCs in providing confined but communicating spaces for complex metabolic reactions and in protecting encapsulated compounds from denaturation is explored. To support a controlled spatial organization of CNCs, CNCs are patterned by means of DNA hybridization to a microprinted glass surface. Specifically, CNC-functionalized DNA microarrays are produced where individual reaction compartments are kept in close proximity by a distinct geometrical arrangement to promote effective communication. Besides a remarkable versatility and robustness, the most prominent feature of this platform is the reversibility of DNA-mediated CNC-anchoring which renders it reusable. Micropatterns of polymer-based nanocompartment assemblies offer an ideal scaffold for the development of the next generation responsive and communicative soft-matter analytical devices for applications in catalysis and medicine.

A High-Performance Multistep Phase-Shifting Interferometric Fiber Optic Sensing Method With Modulation Error Correction Capability

Modulation error is a main error source of phase-shifting technique in the field of interferometric fiber optic sensing. To solve this problem, a multistep phase-shifting method with modulation error correction capability using rectangular-pulse binary phase modulation is proposed in this paper. In this method, a pair of three-step interference signals are obtained with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> /2 phase modulation to generate multistep phase-shifting interference signals. The cross-correlation operation of the two signals with the same phase shift is calculated. Then, precise control of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> /2 modulation can be realized using simulated annealing algorithm to correct the modulation error. In addition, compared with the three-step phase-shifting algorithm, the extracted five-step phase-shifting algorithm itself has a higher ability to suppress the modulation error. Simulation and experimental results demonstrate that this method can be used to demodulate the phase result with high effectiveness and stability, improve the time domain signal and reduce the total harmonic distortion (THD), which provides a novel perspective for high-performance fiber-optic sensing and phase-shifting technique. In the actual experiment, after the calibration of modulation error, THD is effectively reduced from 7.5% to 4.5%. In addition, the method is verified by the demodulation of 200 Hz and 1000 Hz signals. The method also shows excellent repeatability and linearity in demodulation of sensing signals.

Young's Modulus Calculus Using Split Hopkinson Bar Tests on Long and Thin Material Samples.

Young’s modulus is a key parameter of materials. The method of its calculation in the current paper is concerned with the mismatch of the mechanical impedance at the bar/specimen interface for a compression SHPB (split Hopkinson pressure bar) test. By using long and thin specimens, the signal recorded in the transmission bar presents itself as a multistep signal. The ratio between the heights of two successive steps represents the experimental data that are considered in the formula of the elastic modulus this article is devoted to. The oscillatory nature of the real signals on the horizontal or quasi-horizontal segments prevents a precise determination of the two successive step heights ratio. A fine tuning of this value is made based on the characteristic time necessary for the signal to rise from one level to the next one. The FEM (Finite Element Method) simulations are also used in calculation of the Poisson coefficient of the tested complex concentrated alloy.

Dynamic nuclear polarization-enhanced, double-quantum filtered 13C-13C dipolar correlation spectroscopy of natural 13C abundant bone-tissue biomaterial

Here, we describe a method for obtaining a dynamic nuclear polarization (DNP)-enhanced double-quantum filtered (DQF) two-dimensional (2D) dipolar 13C-13C correlation spectra of bone-tissue material at natural 13C abundance. DNP-enhanced DQF 2D dipolar 13C-13C spectra were obtained using a few different mixing times of the dipolar-assisted rotational resonance (DARR) scheme and these spectra were compared to a conventional 2D through-space double-quantum (DQ)-single-quantum (SQ) correlation spectrum. While this scheme can only be used for an assignment purpose to reveal the carbon-carbon connectivity within a residue, the DQF 13C-13C dipolar correlation scheme introduced here can be used to obtain longer distance carbon-carbon constraints. A DQF pulse block is placed before the DARR mixing scheme for removing dominant 13C single-quantum (SQ) signals because these SQ 13C signals are overwhelmingly large compared to those 13C-13C dipolar cross-peaks generated and therefore saturate the dynamic range of the NMR detection. This approach exhibits strong enough 2D cross-peaks in a dipolar 13C-13C correlation spectrum and potentially provides pairwise 13C-13C dipolar constraints because the dipolar truncation effect as well as multi-step signal propagations involving a spin cluster that contains more than two spins can be ignored probabilistically. To obtain fast signal averaging, AsymPolPOK was used to provide a short 1H DNP signal build-up time (1.3 s) and to expedite our MAS DNP NMR acquisitions while still maintaining a satisfactory DNP enhancement factor (ε = 50). Under long DARR mixing, a t1-noise-like artifact was observed at a site that possesses a large chemical shift anisotropy (CSA) and a few different strategies to address this problem were discussed.

The residence of synaptically released dopamine on D2 autoreceptors.

SUMMARYNeuromodulation mediated by synaptically released endogenous transmitters acting in G-protein-coupled receptors (GPCRs) is slow primarily because of multistep downstream signaling. What is less well understood is the spatial and temporal kinetics of transmitter and receptor interaction. The present work uses the combination of the dopamine sensor, dLight, to detect the spatial release and diffusion of dopamine and a caged form of a D2-dopamine receptor antagonist, CyHQ-sulpiride, to rapidly block the D2 autoreceptors. Photoactivation of the CyHQ-sulpiride blocks receptors in milliseconds such that the time course of dopamine/receptor interaction is mapped onto the downstream signaling. The results show that highly localized release, but not dopamine diffusion, defines the time course of the functional interaction between dopamine and D2 autoreceptors, which determines downstream inhibition.

Predicting Metastasis from Cues of Metastatic Cancer Stem-like Cells-3D-Ultrasensitive Metasensor at a Single-Cell Level.

Metastasis is the primary reason for treatment failure and cancer-related deaths. Hence forecasting the disease in its primary state can advance the prognosis. However, existing techniques fail to reveal the tumor heterogeneity or its evolutionary cascades; hence they are not feasible to predict the onset of metastatic cancer. The key to metastasis originates from the primary tumor cells, evolving by inheriting multistep sequential cue signals. We have identified this specific population, termed metastatic cancer stem-like cells (MCSCs), to foresee cancer's ability to metastasize. An invasive property renders MCSCs nonadherent, summoning a powerful technique to forecast metastasis. Thus, we have generated an ultrasensitive 3D-metasensor to efficiently capture and investigate MCSCs and magnify the vital premetastatic signals from a single cell. We developed 3D-metasensor by an ultrafast laser ionization technique, consisting of self-assembled three-dimensionally organized nanoprobes incorporated with dopant functionalities. This distinct methodology establishes attachment with nonadherent MCSCs, elevates Raman activity, and enables probing of consequent signals (metabolic, proliferation, and metastatic) specifically altered in MCSCs. Extensive analysis using prediction tools-the area under the curve (AUC) and principal component analysis (PCA)-revealed high sensitivity (100%) and specificity (80%) to differentiate the MCSCs from other populations. Further, investigation reveals that the cue signal level from MCSCs of primary cancer is analogous to MCSCs from higher-level tumors, disclosing the relative dependence to estimate the primary tumor's capacity to metastasize. Multiple spectrum evaluation using the metasensor pinpoint the dynamic cues in MCSCs predict the onset of metastasis; thus, exploring these metastasis hallmarks can enhance prognosis and revolutionize therapy strategies.

Based on ADRC Strategy for Tracking Trajectory of Undamped Plants

This paper mainly actualizes the precise trajectory tracking for the direct-drive servo system with undamped plant. Using the nominal model of the undamped object in this paper is to realize the precise control and obtain the better tracking effects by adopting the ADRC strategy which is introduced to avoid the overshoot causing by design of typical PID control law and improve the robustness of servo system. Simultaneously, it can also achieve the fast tracking and obtain the better convergence effects. Extensive simulations indicate that it practically validates the superiority of adopting the ADRC strategy to control these typical undamped plant. Both average placement tracking error and velocity tracking error for the sinusoidal waves and multi-step signal are converging to the value fluctuating around zero or gradually approaching to zero. This all-around control strategy to achieve the precise and fast trajectory tracing for the direct-drive servo system with undamped object is effective and feasible.

Multi-cylinder electrohydraulic digital loading technology for reproduction of large load

To solve the problem of the control accuracy in electrohydraulic loading systems caused by load increment, this paper proposes a multi-cylinder electrohydraulic digital loading (MEDL) technology for accurate reproduction of large load. A traditionally used single cylinder loading (SCL) is replaced by a new hydraulic cylinders group that includes N hydraulic cylinders at each point, in which one is controlled by the electrohydraulic servo valve and the others (N-1) are controlled by the on-off valve. The areas of the on-off valve controlled (OVC) cylinders form an increasing geometric sequence with a common ratio of 2. In addition, the force of the servo valve controlled (SVC) cylinder can be regulated continuously, and the OVC cylinders have only two states of no force or maximum force. There should be no force tracking error caused by nonlinear factors for the OVC cylinders. Thus, a continuous accurate large loading can be achieved by changing the working area of the cylinders group. Moreover, an improved full closed-loop (FCL) control strategy is proposed to solve the load reverse sudden change caused by the asynchronous opening and closing of the servo valve and on-off valve. With a case of N = 4 for MEDL, AMESim simulation results illustrated that the tracking error of the 4-cylinder group was about 1/6 of the single cylinder under a case of 40 kN. Furthermore, extensive experiments conducted on a real full loading bench under the FCL control method indicated that compared with SCL, the tracking error of the 4-cylinder group with a multi-step signal and various-frequency sinusoidal signals were reduced by 73% and 46%, respectively. Both simulation and experimental results proved that the proposed MEDL technology improved the loading accuracy and optimized the dynamic performance of the system.

Signal Integration in Plant Abiotic Stress Responses via Multistep Phosphorelay Signaling

Plants growing in any particular geographical location are exposed to variable and diverse environmental conditions throughout their lifespan. The multifactorial environmental pressure resulted into evolution of plant adaptation and survival strategies requiring ability to integrate multiple signals that combine to yield specific responses. These adaptive responses enable plants to maintain their growth and development while acquiring tolerance to a variety of environmental conditions. An essential signaling cascade that incorporates a wide range of exogenous as well as endogenous stimuli is multistep phosphorelay (MSP). MSP mediates the signaling of essential plant hormones that balance growth, development, and environmental adaptation. Nevertheless, the mechanisms by which specific signals are recognized by a commonly-occurring pathway are not yet clearly understood. Here we summarize our knowledge on the latest model of multistep phosphorelay signaling in plants and the molecular mechanisms underlying the integration of multiple inputs including both hormonal (cytokinins, ethylene and abscisic acid) and environmental (light and temperature) signals into a common pathway. We provide an overview of abiotic stress responses mediated via MSP signaling that are both hormone-dependent and independent. We highlight the mutual interactions of key players such as sensor kinases of various substrate specificities including their downstream targets. These constitute a tightly interconnected signaling network, enabling timely adaptation by the plant to an ever-changing environment. Finally, we propose possible future directions in stress-oriented research on MSP signaling and highlight its potential importance for targeted crop breeding.

A Microphysiological Approach to Evaluate Effectors of Intercellular Hedgehog Signaling in Development.

Paracrine signaling in the tissue microenvironment is a central mediator of morphogenesis, and modeling this dynamic intercellular activity in vitro is critical to understanding normal and abnormal development. For example, Sonic Hedgehog (Shh) signaling is a conserved mechanism involved in multiple developmental processes and strongly linked to human birth defects including orofacial clefts of the lip and palate. SHH ligand produced, processed, and secreted from the epithelial ectoderm is shuttled through the extracellular matrix where it binds mesenchymal receptors, establishing a gradient of transcriptional response that drives orofacial morphogenesis. In humans, complex interactions of genetic predispositions and environmental insults acting on diverse molecular targets are thought to underlie orofacial cleft etiology. Consequently, there is a need for tractable in vitro approaches that model this complex cellular and environmental interplay and are sensitive to disruption across the multistep signaling cascade. We developed a microplate-based device that supports an epithelium directly overlaid onto an extracellular matrix-embedded mesenchyme, mimicking the basic tissue architecture of developing orofacial tissues. SHH ligand produced from the epithelium generated a gradient of SHH-driven transcription in the adjacent mesenchyme, recapitulating the gradient of pathway activity observed in vivo. Shh pathway activation was antagonized by small molecule inhibitors of epithelial secretory, extracellular matrix transport, and mesenchymal sensing targets, supporting the use of this approach in high-content chemical screening of the complete Shh pathway. Together, these findings demonstrate a novel and practical microphysiological model with broad utility for investigating epithelial-mesenchymal interactions and environmental signaling disruptions in development.