Prior Experiments Research Articles

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm’s performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies.We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.

Scorpion α-toxin LqhαIT specifically interacts with a glycan at the pore domain of voltage-gated sodium channels

Voltage-gated sodium (Nav) channels sense membrane potential and drive cellular electrical activity. The deathstalker scorpion α-toxin LqhαIT exerts a strong action potential prolonging effect on Nav channels. To elucidate the mechanism of action of LqhαIT, we determined a 3.9Å cryoelectron microscopy (cryo-EM) structure of LqhαIT in complex with the Nav channel from Periplaneta americana (NavPas). We found that LqhαIT binds to voltage sensor domain 4 and traps it in an "S4 down" conformation. The functionally essential C-terminal epitope of LqhαIT forms an extensive interface with the glycan scaffold linked to Asn330 of NavPas that augments a small protein-protein interface between NavPas and LqhαIT. A combination of molecular dynamics simulations, structural comparisons, and prior mutagenesis experiments demonstrates the functional importance of this toxin-glycan interaction. These findings establish a structural basis for the specificity achieved by scorpion α-toxins and reveal the conserved glycan as an essential component of the toxin-binding epitope.

Motion Planning for Dynamic Three-Dimensional Manipulation for Unknown Flexible Linear Object

Generally, deformable objects have large and nonlinear deformations. Because of these characteristics, recognition and estimation of their movement are difficult. Many studies have been conducted aimed at manipulating deformable objects at will. However, they have been focused on situations wherein a rope’s properties are already known from prior experiments. In our previous work, we proposed a motion planning algorithm to manipulate unknown ropes using a robot arm. Our approach considered three steps: motion generation, manipulation, and parameter estimation. By repeating these three steps, a parameterized flexible linear object model that can express the actual rope movements was estimated, and manipulation was realized. However, our previous work was limited to 2D space manipulation. In this paper, we extend our previously proposed method to address casting manipulation in a 3D space. Casting manipulation involves targeting the flexible linear object tips at the desired object. While our previous studies focused solely on two-dimensional manipulation, this work examines the applicability of the same approach in 3D space. Moreover, 3D manipulation using an unknown flexible linear object has never been reported for dynamic manipulation with flexible linear objects. In this work, we show that our proposed method can be used for 3D manipulation.

Dynamic response of a transmission conductor following delayed ice shedding by reduced-scale model test

Ice shedding on conductors of transmission lines can induce severe vertical vibrations and abrupt tension changes, potentially causing structural damage and power outages. Prior experiments and numerical simulations assumed that ice sheds instantaneously from transmission lines, neglecting the practical delays in the ice shedding process, which resulted in unrealistic conductor dynamic responses. This study introduces a reduced-scale modeling system designed to simulate delayed ice shedding on conductors. The jump height and dynamic tension of an isolated-span transmission line following delayed ice shedding are analyzed, and various factors such as the two-dimensional delay duration, shedding sequence, and weight of ice accretion are examined through reduced-scale model tests. Based on the experimental data, simplified formulas are proposed to calculate the conductor's jump height and maximum tension after ice shedding by taking the time delay of the shedding process into account.

Diffraction of polar molecules at nanomasks with low charge density

The wave nature of matter is a cornerstone of modern physics and has been demonstrated for a wide range of fundamental and composite particles. While diffraction at nanomechanical masks is usually regarded to be independent of internal atomic or molecular states, the particles' polarizabilities and dipole moments lead to dispersive interactions with the grating surface. In prior experiments, such forces largely prevented coherent diffraction of polar molecules as they induce dephasing of the matter wave in the presence of randomly distributed charges inside the grating. Here, we show that surface milling using neon ions facilitates the fabrication of lowly charged nanomasks in gold-capped silicon nitride membranes. This allows us to observe diffraction of polar molecules with over four times larger electric dipole moment than in previous experiments, opening a path towards distinction of structural conformers in matter-wave experiments. Published by the American Physical Society 2024

The invariant measure of a walking droplet in hydrodynamic pilot–wave theory

We study the long time statistics of a walker in a hydrodynamic pilot-wave system, which is a stochastic Langevin dynamics with an external potential and memory kernel. While prior experiments and numerical simulations have indicated that the system may reach a statistically steady state, its long-time behavior has not been studied rigorously. For a broad class of external potentials and pilot-wave forces, we construct the solutions as a dynamics evolving on suitable path spaces. Then, under the assumption that the pilot-wave force is dominated by the potential, we demonstrate that the walker possesses a unique statistical steady state. We conclude by presenting an example of such an invariant measure, as obtained from a numerical simulation of a walker in a harmonic potential.

Research on the Influencing Factors of Pulsed Hydraulic Fracturing Effects in Hot Dry Rock Based on Numerical Simulation

Geothermal energy, representing one of the five principal non-carbon-based renewable resources, is crucial in the global energy transition and in mitigating greenhouse gas emissions. Hot Dry Rock (HDR) reservoirs, rich in geothermal resources, predominantly utilize hydraulic fracturing, the most extensively researched and applied technology. However, fracturing these robust HDR formations poses significant challenges compared to traditional hydraulic fracturing in sedimentary rocks in the oil and gas industry. Cyclic pulse fracturing, an innovative mode of fracturing, has received limited academic attention, particularly regarding its permeability enhancement mechanism under varying thermal and pressure conditions, which remains largely unexplored. Building on prior laboratory experiments, this study employs the pore pressure cohesive zone model within ABAQUS software to elucidate the initiation and expansion of hydraulic fractures. Numerical simulations investigated the influence of critical parameters such as pulse pattern, temperature, geostress differential, and confining pressure on fracture propagation. The findings indicate that cyclic pulse hydraulic fracturing can significantly reduce the breakdown pressure in HDR, with reductions up to 37.68% under certain conditions. Notably, at constant amplitude, increased frequency exacerbates damage to hot dry rocks; conversely, at constant frequency, higher amplitudes result in greater damage. Additionally, pulse hydraulic fracturing improves the fracturing process, yielding a more intricate fracture network. In HDR pulse hydraulic fracturing, the temperature disparity between the fracturing fluid and the rock markedly impacts the breakdown pressure. Cold water interacting with a high-temperature geo-thermal reservoir triggers a thermal shock effect, diminishing initiation pressure and expediting fracture development and expansion. The impact of geostress differential and confining pressure on pulse hydraulic fracturing is complex, influencing not only the breakdown pressure but also the direction and extent of fracture expansion.

Accurate first-principles simulation for the response of 2D chemiresistive gas sensors

The realm of chemiresistive gas sensors has witnessed a notable surge in interest in two-dimensional (2D) materials. The advancement of high-performance 2D gas sensing materials necessitates a quantitative theoretical method capable of accurately predicting their response. In this context, we present our first-principles framework for calculating the response of 2D materials, incorporating both carrier concentration and mobility. We showcase our method by applying it to prototype NH3 sensing on 2D MoS2 and comparing the results with prior experiments in the literature. Our approach offers a thorough solution for carrier concentration, taking into account the electronic structure around the Fermi level. In conjunction with the mobility calculation, this enables us to provide a quantitative prediction of the response profile and limit of detection (LOD), yielding a notably improved alignment with prior experimental findings. Further analysis quantifies the contributions of carrier concentration and mobility to the overall response of 2D MoS2 to NH3. We identify that discrepancies in the charge-transfer-based method primarily stem from overestimating carrier concentrations. Our method opens exciting opportunities to explore carrier mobility-dominated sensing materials, facilitates efficient screening of promising gas sensing materials, and quantitative understanding of the sensing mechanism.

Direct numerical simulation of the 7-7-7 film cooling at a range of compound angles

Film cooling is essential for protecting substrate materials from the hot gases in turbomachines. Recent experiments have explored the effects of compound angles for the 7-7-7 film cooling hole, a baseline shaped film cooling hole developed at the START lab. However, differences in facility conditions, manufacturing process uncertainties, uncertainties in flow conditions, and the absence of internal cooling hole measurements necessitate further investigation to fully understand the impact of compound angles and the associated flow physics. This study presents direct numerical simulations (DNS) for the baseline 7-7-7 shaped film cooling hole, examining a range of compound angles and flow conditions paralleling those in prior lab experiments. The data gleaned offers insights into the flow dynamics within and in the vicinity of the cooling hole, particularly in areas proximate to the wall. Our findings indicate that increasing the compound angle adversely affects adiabatic cooling efficiency. A detailed analysis reveals that this decline in efficiency is primarily due to hot gas intrusion into the cooling hole, the elevation of the cooling jet from the wall, and intensified secondary flows at higher compound angles. Additionally, the DNS data reveals a logarithmic scaling in the laterally averaged mean velocity profile and a self-similarity in temperature profiles beyond a certain distance from the cooling hole.

Experimental Characterization of Hydronic Air Coil Performance with Aluminum Oxide Nanofluids of Three Concentrations

This paper presents the continuation of experimental investigations conducted by the present authors to measure and compare the thermal and fluid dynamic performance of a residential hydronic air coil using nanofluids. The prior experiments were limited to testing only one volumetric concentration (1%) of aluminum oxide (Al2O3) nanofluid. They compared it with the base fluid, a 60% ethylene glycol/40% water mixture by mass (60% EG). The original tests revealed some deficiencies in the experimental setup, which was subsequently revised and improved. This paper summarizes the results of experiments from the improved test bed using three concentrations of Al2O3 nanofluids: 1, 2, and 3% volumetric concentrations prepared with an average particle size of 45 nm in a 60% EG dispersion. The test bed in these experiments simulates a small air handling system typical of heating, ventilation, and air conditioning (HVAC) applications in cold regions. Entering conditions for the air and liquid were selected to emulate typical commercial air handling systems operating in cold climates. Contrary to previous findings, our test results revealed that nanofluids did not perform as well as expected. Prior predictions from many analytical and numerical studies had promised significant performance gain. The performance of the 1% nanofluid was generally equal to that of the base fluid under identical inlet conditions. However, the performance of the 2% and 3% nanofluids was considerably lower than that of the base fluid. The higher concentration nanofluids exhibited heat rates up to 14.6% lower than the 60% EG and up to 44.3% lower heat transfer coefficient. The 1% Al2O3/60% EG exhibited a 100% higher pressure drop across the coil than the base fluid, considering equal heat output. This performance degradation was attributed to the inability to maintain nanofluid dispersion stability, agglomeration, and subsequent decline in the thermophysical properties.

Nitridation of diamond(111) surface by density functional theory.

Density functional theory was employed to examine the adsorption and thermal evolution of nitrogen species on diamond(111) impacted by microwave N2 plasma. On bare domains of diamond, as represented by the models of C(111)-2 × 1 and graphite-like C(111), N2(ad) is identified as the major surface species; the desorption of N2(ad) proceeds on both models via a concerted process of breaking two C-N bonds. By contrast, there is evidence of the formation of (NH)2(ad) via the insertion reaction of microwave N2 plasma on hydrogenated domains of diamond, as represented by the models of C(111)-2 × 1-H and C(111)-1 × 1-H. Interestingly, contrasting dynamics of desorption of (NH)2(ad) are presented on these two models, that is, via sequential breaking of two C-N bonds on C(111)-2 × 1-H and via concerted breaking of both C-N bonds on C(111)-1 × 1-H. Our results demonstrate that the observed diversity of surface nitrogen species in composition, bonding, vibration, and desorption in prior experiments is linked to domains of a variety of surface terminations and reconstructions on diamond(111).

Evaluation of Essential Dynamics and Fixed-Length Coarse Graining for Multidomain Proteins.

For multiscale modeling of biomolecules, reliable coarse-grained (CG) models can offer great potential to simulate larger temporal and spatial scales than traditional all-atom (AA) models. In this study, we explore the essential dynamics coarse graining (EDCG) and fixed-length coarse graining (FLCG) approaches for constructing highly coarse-grained models for multidomain proteins (MDPs), with 1 to 10 amino acid residues per CG site. In the studies of 13 MDPs, our data indicate that both EDCG and FLCG can preserve the protein dynamics of MDPs. FLCG, which restricts an equal number of residues in each CG site, represents an excellent approximation to EDCG and a straightforward approach for coarse-graining MDPs. Furthermore, FLCG is tested with a class B G-protein-coupled receptor protein, and the agreement with prior experiments suggests its general application to various MDPs in different environments or conditions. Finally, we demonstrate another application of FLCG through progressive backmapping, showcasing the ability to recover from lower-resolution CG models (6 residues/CG site) to higher-resolution ones (1 residue/CG site). These promising outcomes underscore the broad applicability of FLCG to construct highly or ultra-coarse-grained models of complex biomolecules for multiscale simulations.

Investigating removal mechanisms of long- and short-chain per- and polyfluoroalkyl substances using specialty adsorbents in a field-scale surface water filtration system

This study assessed the application of two specialty adsorbents, also known as green sorption media (GSM), including clay–perlite and sand sorption media (CPS) and zero-valent iron and perlite green environmental media (ZIPGEM) to remove long- and short-chain per- and polyfluoroalkyl substances (PFAS) at field scale. The field-scale demonstration employed four GSM filter cells installed near the C-23 Canal (St. Lucie County, FL), which discharges water to the ecologically sensitive St. Lucie River estuary and to the Atlantic Ocean finally. Although prior lab-scale experiments had demonstrated the effectiveness of CPS and ZIPGEM in treating long-chain PFAS, their performance in field-scale application warranted further investigation. The study reveals the critical roles of divalent cations such as Ca2+ and monovalent cations such as ammonium and hydronium ions, as well as other water quality parameters, on PFAS removal efficacy. Ammonia, most likely resulting from photo- and bacterial ammonification, gives rise to elevated ammonium ion formation in the wet season due to the decrease in pH, which ultimately worsens PFAS adsorption. Moreover, there is a strong negative correlation between pH and PFAS removal efficiency in the presence of ammonia, as evidenced by the reduced removal of PFAS during events associated with low pH.

An Electromechanical Impedance-Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

The multiple intersecting stiffeners on the chemical milling stiffened panel (CMSP) limit the application of active health monitoring methods on it. An imaging algorithm based on electromechanical impedance (EMI) and probability-weighting is proposed to achieve quantitative evaluation and localization of the damage on CMSP. The proposed algorithm compensates for the difference in sensor performance with coefficients and there is no need to determine the key parameters of the algorithm through prior experiments. In the paper, the applicability of ultrasonic guided wave (GW) and EMI on CMSP was first studied through the finite element method. Based on EMI and the mean absolute percentage deviation (MAPD), the selected damage indicator (DI), a probability-weighted damage imaging algorithm are proposed and experimentally verified. The results indicate that due to the reflection and attenuation effects of stiffeners on GW, the signal characteristics of damage scattering waves are contaminated, making it difficult to quantitatively characterize the damage from GW signals through DIs. MAPD is positively correlated with the damage degree and has consistency in characterizing the signal of different PZTs under the same working condition. The feasibility and accuracy of the proposed algorithm are verified through experiments which show a strong engineering application capability.

Single-cell transcriptomics predicts colony stimulating factor-1 regulation of age-dependent intestinal epithelial restitution

Intestinal diseases associated with ischemic injury, which damages the principal barrier against noxious luminal contents, result in unacceptably poor outcomes in newborns. Ischemia-induced loss of the intestinal epithelial barrier predisposes patients to life-threatening sepsis unless that barrier is rapidly restored. There is an age-dependency of intestinal recovery in that neonates are the most susceptible to succumb to disease of the intestinal barrier versus older patients. While this age-dependence in repair has not been demonstrated in traditional rodent models, we have developed a highly translational pig model of intestinal ischemic injury and repair that does reflect this difference. We have shown that, while juvenile (weaned) pigs recover rapidly after ischemic intestinal injury, barrier repair is markedly underdeveloped in neonatal (nursing) pigs due to complete failure of epithelial restitution. Importantly, we found that restitution in neonates can be rescued by the direct application of homogenized mucosa from ischemia-injured small intestine from juvenile pigs. The mechanisms that allow for this restitution and rescue remain to be defined. We hypothesized that by identifying a subpopulation of restituting enterocytes by their expression of cell migration transcriptional pathways, we can then predict novel upstream regulators of age-dependent restitution response programs. Superficial mucosal epithelial cells collected from recovering ischemic jejunum of juvenile pigs were processed for single cell RNA sequencing, unbiased clustering and upstream regulator analysis. A porcine intestinal epithelial cell line (IPEC-J2) and banked tissues from prior rescue experiments were qualitatively and functionally assessed for activity of predicted upstream regulators. Single cell transcriptomics in recovering juvenile epithelium revealed a subcluster of absorptive enterocytes that express several cell migration pathways key to restitution. Differentially expressed genes in this subcluster predicted their upstream regulation by many potential molecules, including colony stimulating factor-1 (CSF-1) which is known to induce cell migration in non-intestinal epithelial tissues. To begin validating this prediction, we demonstrated that CSF-1 was enriched in the ischemic juvenile mucosa which rescues neonatal restitution and documented expression of the CSF-1 receptor (CSF1R) in both neonatal and juvenile epithelium, indicating that these cells are equipped to respond to CSF-1. CSF-1 and CSF1R co-localized in ischemic juvenile, but not neonatal, wound-adjacent epithelial cells and in the restituted epithelium of juveniles and rescued (but not control) neonates. Further, the CSF1R inhibitor BLZ945 reduced restitution in scratch wounded IPEC-J2 cells. Single cell transcriptomics have the power to inform potential novel therapeutic targets, such as CSF-1, to improve mucosal recovery in neonates with intestinal failure in this unique and powerful pig model. NIH K01 OD028207, NIH R01 HD095876, NIH U01 TR002953, NIH T32 OD011130, NIH P30 DK034987, USDA NIFA 1007263 and 07985. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Dietary restriction extends lifespan across different temperatures in the fly

Abstract Dietary restriction (DR) has been consistently shown to extend lifespan across a range of taxa. However, it has recently been reported that DR does not extend lifespan at certain, namely lower, temperatures in flies (Drosophila melanogaster). Similar to the interpretation of other findings that appear to question DR's universality, this finding has been interpreted as suggesting that lifespan extension in response to DR is an artefact of benign laboratory conditions. We re‐test this hypothesis, now using a strain that shows robust lifespan extension at 25°C (across three prior experiments), and using a range of five diets across two temperatures, 18°C and 21°C. We found the DR longevity response to be robust, extending lifespan irrespective of temperature. We measured fecundity as a positive control for the DR phenotype, and found, as predicted, that DR reduces egg laying. We suggest differences in experimental setup, genetic lines used and variation in the diet‐lifespan reaction norm are responsible for this discrepancy. In addition, starting with a strain and conditions that show a lifespan extension by DR, as we do here, and then changing environment and/or genotype promises a more robust test of DR modulating factors. In conclusion, it will be important for results that question DR as a phenotype to not be overinterpreted readily, as with a substantially larger sample size and a larger range of diets we were unable to replicate this prior work. Read the free Plain Language Summary for this article on the Journal blog.

Convolutional neural network for improved event-based Shack-Hartmann wavefront reconstruction

Shack-Hartmann wavefront sensing is a technique for measuring wavefront aberrations, whose use in adaptive optics relies on fast position tracking of an array of spots. These sensors conventionally use frame-based cameras operating at a fixed sampling rate to report pixel intensities, even though only a fraction of the pixels have signal. Prior in-lab experiments have shown feasibility of event-based cameras for Shack-Hartmann wavefront sensing (SHWFS), asynchronously reporting the spot locations as log intensity changes at a microsecond time scale. In our work, we propose a convolutional neural network (CNN) called event-based wavefront network (EBWFNet) that achieves highly accurate estimation of the spot centroid position in real time. We developed a custom Shack-Hartmann wavefront sensing hardware with a common aperture for the synchronized frame- and event-based cameras so that spot centroid locations computed from the frame-based camera may be used to train/test the event-CNN-based centroid position estimation method in an unsupervised manner. Field testing with this hardware allows us to conclude that the proposed EBWFNet achieves sub-pixel accuracy in real-world scenarios with substantial improvement over the state-of-the-art event-based SHWFS. An ablation study reveals the impact of data processing, CNN components, and training cost function; and an unoptimized MATLAB implementation is shown to run faster than 800 Hz on a single GPU.

Joint shear strength prediction for fiber reinforced concrete beam-to-column connections

Beam-to-column joints experience substantial shear forces and deformations, playing a pivotal role in maintaining the overall stability and integrity of moment-resisting frames under earthquake loading. If fiber reinforced concrete is used instead of reinforced concrete, beam flexural failure can be achieved, which leads to a ductile behavior and increased energy dissipation capacity, when compared to joint shear failure. To ensure a ductile failure mode, accurate prediction of the joint shear strength is essential. However, there are only a limited number of analytical studies on predicting the shear strength of fiber reinforced concrete joints, most of which are only applicable to steel fibers. In this study, a comprehensive database of prior experiments conducted on fiber reinforced concrete beam-to-column connection subassemblies is compiled. Based on Pearson Correlation Analyses, the factors influencing the seismic response are determined to be the composite tensile cracking strength, axial load on the column, aspect ratios of member dimensions, and specific fiber properties such as aspect ratio and volume fraction. A joint shear strength prediction equation is then developed utilizing statistical nonlinear regression method. Validation against experimental data confirms that the proposed equation provides conservative estimates of joint shear strength and can be utilized for composites with any fiber type, even hybrid fibers; in contrast to existing equations which often lead to overestimation of strength and restricted to a specific fiber type. Moreover, the proposed equation provides engineers a practical tool to assess the seismic behavior of such connections, which has not been addressed by current structural design codes yet. The improved accuracy of the proposed equation, demonstrated by the lowest mean absolute error and coefficient of variation and the highest coefficient of correlation, positions it as a promising candidate for integration into structural design codes.

Asynchronous Multi-Information Source Bayesian Optimization

Abstract Resource management in engineering design seeks to optimally allocate while maximizing the performance metrics of the final design. Bayesian optimization (BO) is an efficient design framework that judiciously allocates resources through heuristic-based searches, aiming to identify the optimal design region with minimal experiments. Upon recommending a series of experiments or tasks, the framework anticipates their completion to augment its knowledge repository, subsequently guiding its decisions toward the most favorable next steps. However, when confronted with time constraints or other resource challenges, bottlenecks can hinder the traditional BO’s ability to assimilate knowledge and allocate resources with efficiency. In this work, we introduce an asynchronous learning framework designed to utilize idle periods between experiments. This model adeptly allocates resources, capitalizing on lower fidelity experiments to gather comprehensive insights about the target objective function. Such an approach ensures that the system progresses uninhibited by the outcomes of prior experiments, as it provisionally relies on anticipated results as stand-ins for actual outcomes. We initiate our exploration by addressing a basic problem, contrasting the efficacy of asynchronous learning against traditional synchronous multi-fidelity BO. We then employ this method to a practical challenge: optimizing a specific mechanical characteristic of a dual-phase steel.

Computational modeling of dorsal root ganglion stimulation using an Injectrode

Objective. Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated Platinum/Iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which subsequently transmits the stimulation to the Injectrode via a subcutaneous metal collector. However, it is important to note that the effectiveness of stimulation through TES relies on the specific geometrical configurations of the Injectrode-collector-patch system. Hence, there is a need to investigate which design parameters influence the activation of targeted neural structures. Approach. We employed a hybrid computational modeling approach to analyze the impact of Injectrode system design parameters on charge delivery and neural response to stimulation. We constructed multiple finite element method models of DRG stimulation, followed by the implementation of multi-compartment models of DRG neurons. By calculating potential distribution during monopolar stimulation, we simulated neural responses using various parameters based on prior acute experiments. Additionally, we developed a canonical monopolar stimulation and full-scale model of bipolar bilateral L5 DRG stimulation, allowing us to investigate how design parameters like Injectrode size and orientation influenced neural activation thresholds. Main results. Our findings were in accordance with acute experimental measurements and indicate that the minimally invasive Injectrode system predominantly engages large-diameter afferents (Aβ-fibers). These activation thresholds were contingent upon the surface area of the Injectrode. As the charge density decreased due to increasing surface area, there was a corresponding expansion in the stimulation amplitude range before triggering any pain-related mechanoreceptor (Aδ-fibers) activity. Significance. The Injectrode demonstrates potential as a viable technology for minimally invasive stimulation of the DRG. Our findings indicate that utilizing a larger surface area Injectrode enhances the therapeutic margin, effectively distinguishing the desired Aβ activation from the undesired Aδ-fiber activation.