Global Analysis Research Articles

Climate change as observed through the IMS radionuclide station in Spitzbergen

The International Monitoring System (IMS), installed and maintained by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) with the support of States Signatories, is a global system of monitoring stations based on four complementary technologies: seismic, hydroacoustic, infrasound and radionuclide. One of the IMS radionuclide stations is located in Spitzbergen, the largest island of the Norwegian Svalbard Archipelago, which borders the Barents Sea and the Northern Atlantic Ocean. It has been demonstrated that signs of climate change are particularly noticeable in that region. As many other radionuclides observed in environmental measurements, 212Pb is always observed at IMS stations, in varying quantities. This is also the case for the IMS station RN49, Spitzbergen, where it can be demonstrated that the average concentration of the measured lead 212Pb increases. This is observable specifically October through December. This paper demonstrates the asset of IMS data to study climate change effects. Our conclusions are supported by global temperature anomaly data from NOAA’s Global Surface Temperature Analysis, covering the period 1850 to 2023.

Economy-wide Impacts of India–UAE CEPA: A CGE Analysis

The present study investigates the potential trade effects of tariff liberalisation under India–United Arab Emirates (UAE) Comprehensive Economic Partnership Agreement (CEPA) using general equilibrium analysis. The study specifically examines output, employment, exports and welfare effects of India–UAE CEPA in computable general equilibrium (CGE) framework using the Global Trade Analysis Project (GTAP) model and database version 10. The study indicates output gains in manufacturing sector for both countries and an increase in demand for unskilled labour in India. The study argues that the CEPA will result in an increase in global exports of all commodity groups for both India and UAE and a substantial gain in bilateral exports of both countries. The present study suggests that deeper integration through tariff reduction on imports under India–UAE CEPA will be welfare enhancing for both India and UAE. Overall results show that there is a lot of untapped potential to bring the welfare gains for both trading partners. Given the present schedule of tariff reduction of CEPA, a win–win scenario will emerge for both partners in short as well as long run. Finally, the study cautioned about the implications of sensitive list for trade effects and protection from misuse of rules of origin in the CEPA. JEL Codes: F1, F13, F14, F15, F17

Integrated Time-Dependent Analysis of a Hydraulic Structure on Soft Foundations during Construction

An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation during the construction period. Finite element analyses are conducted, incorporating a transient thermal creep model for concrete and an elasto-plastic consolidation model for the soil. The modified Cam-clay model is employed to describe the elasto-plastic behavior of the soil. Subsequently, global sensitivity analyses are conducted to determine the relative importance of the model parameters on the system’s response, using Garson’s and partial derivative algorithms based on the backpropagation (BP) neural network. The results indicate that the integrated system exhibits pronounced time-dependent displacement and stress, with dangerous values appearing during specific periods. These values are easily neglected, highlighting the importance of integrated time-dependent analysis. Construction activities, particularly the backfilling process, could cause a sudden change in stress and significantly impact the stress redistribution of the superstructure. Additionally, the mechanical properties of concrete have a significant impact on the stress on the superstructure, while the mechanical properties of the soil control the settlement of the integrated system.

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events

Abstract Motivation Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor binding sites, and protein domains. These elements involve in exon usage, and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes. With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing. Availability and implementation The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R. Supplementary data are available at Bioinformatics Advances online.

Uncertainty quantification and global sensitivity analysis of bearing capacity of push-out specimen considering randomness in bond-slip behaviour

As an artificial composite material, the uncertainty in mechanical property of concrete significantly impacts the bond-slip behaviour of the shaped steel concrete structure. To investigate the effect of the uncertainty of concrete mechanical properties on the bearing capacity of push-out specimens, based on the data of a small number of push-out test with identical design parameters, the improved Bootstrap method is utilized to execute sample augmentation for the bond stress-slip curves, and the stochastic bond slip constitutive model is established after considering five parameters (K1, …, K5) in the deterministic bond-slip model as random variables. Simultaneously, considering the randomness in elastic modulus of concrete (Ec), uncertainty quantification (UQ) of the bearing capacities of the push-out specimens is conducted using the arbitrary polynomial chaos (aPC) method. Subsequently, the global sensitivity analysis of the ultimate and residual bearing capacities is accomplished through the aPC surrogate model. The results indicate that the probability distribution types of the parameters in the stochastic bond-slip constitutive model can be arbitrary when considering the randomness of concrete material, and the aPC method with at least a third-order expansion is recommended to execute UQ for the push-out test due to the strong nonlinearty between the bearing capacities and the random parameters. The ultimate bearing capacity is primarily affected by the parameter K2, followed by K4, K3, and K5. Whereas the residual bearing capacity is predominantly influenced by the parameter K4, followed by K5, the remaining parameters exhibit negligible influence. In contrast, K1 and Ec have the greatest influence on equivalent plastic strain of the concrete near the loading end of the push-out specimen.

A SENSITIVITY ANALYSIS METHOD COMBINING DEMPSTER-SHAFER THEORY AND MACHINE LEARNING FOR ENERGY-SAVING EVALUATION OF BUILDING OCCUPANT BEHAVIOR

ABSTRACT For a very long time, the research of the sensitivity analysis of occupant behavior to energy assessment has been in the spotlight. The key element of the research is determining the exact probability of occupant behavior uncertainty. However, due to the specificity of occupant behavior, data on occupant behavior from different independent sources of information can differ significantly. This paper explores the use of Dempster-Shafer theory to the sensitivity analysis of energy evaluation of occupant behavior in buildings. The Dempster-Shafer theory is an imprecise probability theory that allows the system to create assumed confidence intervals based on interval values probability combined with knowledge of uncertainty factors from many different sources of information. The findings show that the data processing approach based on Dempster-Shafer theory provides effective and reliable information for evaluating energy related to human behavior in buildings. To begin with, the sensitivity analysis process might be accelerated by applying machine learning to process the data. Then, in order to ensure the accuracy of the simulation results, multiple learning methods can be used. Finally, in this paper, model parameters were chosen based on the specific circumstances as soon as the model had been built in order to effectively reduce costs related to operation and increase model accuracy. To establish the final results, the model is evaluated using global sensitivity analysis methods.

KNOWLEDGE DISSEMINATION TRAJECTORY OF BIM IN CONSTRUCTION ENGINEERING APPLICATIONS

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

OPERATE–H2: A tool for optimizing underground hydrogen storage

Underground hydrogen storage (UHS) is crucial in facilitating the transition to clean energy by reducing reliance on fossil fuels and ensuring a stable supply of emission-free energy. However, safe and efficient storage conditions require careful planning. While reservoir simulations could determine optimal storage conditions, exploring all possible scenarios can be computationally prohibitive and time-consuming. Reduced-order models (ROMs) can efficiently simulate reservoir performance and analyze multiple strategies with reduced computational cost, providing valuable insights for UHS decision-making. This paper introduces an innovative integrated assessment framework with a user-friendly graphical user interface (GUI), designed to optimize UHS operations. The GUI features several advanced functionalities, including (1) temporal evolution for visualizing and analyzing UHS performance over withdrawal cycles, (2) local sensitivity analysis to determine the impact of individual parameters on UHS sites while keeping other inputs constants, (3) uncertainty quantification to identify the key factors for effective decision-making via tornado plots, (4) global sensitivity analysis to comprehensively quantify the interactions between geologic and operational parameters and their effect on UHS performance, and (5) sites screening feature to identify the most prominent potential sites for UHS installations. The GUI is built based on ROMs and the PyQt5 packages in Python. With user-friendly design and advanced features, Optimization, Evaluation, and Risk-Assessment Techniques for Hydrogen Energy tool (OPERATE–H2) enables users to evaluate different UHS scenarios quickly and effectively, helping to accelerate the transition to a cleaner and more sustainable energy future.

PD-L1 intrinsically promotes the proliferation of breast cancer cells through the SKP2-p27/p21 axis

BackgroundPD-L1 intrinsically promotes tumor progression through multiple mechanisms, which potentially leads to resistance to anti-PD-1/PD-L1 therapies. The intrinsic effect of PD-L1 on breast cancer (BC) cell proliferation has not been fully elucidated.Methodswe used proteomics, gene expression knockdown (KD), quantitative immunofluorescence (qIF), western blots, functional assays including colony-forming assay (CFA) and real-time cell analyzer (RTCA), and in vivo data using immunohistochemistry in breast cancer patients.ResultsPD-L1 promoted BC cell proliferation by accelerating cell cycle entry at the G1-to-S phase transition. Global proteomic analysis of the differentially expressed nuclear proteins indicated the involvement of several proliferation-related molecules, including p21CIP1/WAF1. Western blotting and qIF demonstrated the higher expression of SKP2 and the lower expression of p21CIP1/WAF1 and p27Kip1 in PD-L1 expressing (PD-L1pos) cells as compared to PD-L1 KD (PD-L1KD) cells. Xenograft-derived cells and the TCGA BC dataset confirmed this relationship in vivo. Functionally, CFA and RTCA demonstrated the central role of SKP2 in promoting PD-L1-mediated proliferation. Finally, immunohistochemistry in 74 breast cancer patients confirmed PD-L1 and SKP-p21/p27 axis relationship, as it showed a highly statistically significant correlation between SKP2 and PD-L1 expression (p < 0.001), and both correlated significantly with the proliferation marker Ki-67 (p < 0.001). On the other hand, there was a statistically significant inverse relationship between PD-L1 and p21CIP1/WAF1 expression (p = 0.005). Importantly, double negativity for p21CIP1/WAF1 and p27Kip1 correlated significantly with PD-L1 (p < 0.001), SKP2 (p = 0.002), and Ki-67 (p = 0.002).Conclusionswe have demonstrated the role of the SKP2-p27/p21 axis in intrinsic PD-L1-enhanced cell cycle progression. Inhibitors of SKP2 expression can alleviate resistance to ICPIs.

A global analysis of the SMEFT under the minimal MFV assumption

We present comprehensive global fits of the SMEFT under the minimal minimal flavour violation (MFV) hypothesis, i.e. assuming that only the flavour-symmetric and CP-invariant operators are relevant at the high scale. The considered operator set is determined by this flavour assumption rather than the used datasets. We establish global limits on these Wilson coefficients using leading order and next-to-leading order SMEFT predictions for electroweak precision observables, Higgs, top, flavour and dijet data as well as measurements from parity violation experiments and lepton scattering. Our investigations reveal an intriguing crosstalk among different observables, underscoring the importance of combining diverse observables from various energy scales in global SMEFT analyses.

Global Proteomic Analysis Reveals Alterations in Differentially Expressed Proteins between Cardiopathic Lamin A/C Mutations.

Lamin A/C (LMNA) is an important component of nuclear lamina. Mutations cause arrhythmia, heart failure, and sudden cardiac death. While LMNA-associated cardiomyopathy typically has an aggressive course that responds poorly to conventional heart failure therapies, there is variability in severity and age of penetrance between and even within specific mutations, which is poorly understood at the cellular level. Further, this heterogeneity has not previously been captured to mimic the heterozygous state, nor have the hundreds of clinical LMNA mutations been represented. Herein, we have overexpressed cardiopathic LMNA variants in HEK cells and utilized state-of-the-art quantitative proteomics to compare the global proteomic profiles of (1) aggregating Q353 K alone, (2) Q353 K coexpressed with WT, (3) aggregating N195 K coexpressed with WT, and (4) nonaggregating E317 K coexpressed with WT to help capture some of the heterogeneity between mutations. We analyzed each data set to obtain the differentially expressed proteins (DEPs) and applied gene ontology (GO) and KEGG pathway analyses. We found a range of 162 to 324 DEPs from over 6000 total protein IDs with differences in GO terms, KEGG pathways, and DEPs important in cardiac function, further highlighting the complexity of cardiac laminopathies. Pathways disrupted by LMNA mutations were validated with redox, autophagy, and apoptosis functional assays in both HEK 293 cells and in induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) for LMNA N195 K. These proteomic profiles expand our repertoire for mutation-specific downstream cellular effects that may become useful as druggable targets for personalized medicine approach for cardiac laminopathies.

Spatial distribution of Japanese encephalitis cases and correlated geo-environmental factors in southern and northern counties of China

Japanese encephalitis (JE) is a mosquito-borne disease with a spatial distribution that is linked to geo-environmental factors. The spatial distribution of JE cases and correlated geo-environmental factors were investigated in two critical counties in southern and northern China. Based on maps, enhanced thematic mapper (ETM) remote sensing datasets from Landsat and spatial datasets of JE cases, spatial distribution and spatial cluster analyses of JE cases at the village scale were performed by using the standard deviational ellipse and Ripleys K-function. Global and regional spatial cluster analyses of JE cases were also performed by using Moran's index. Regression analysis was used to analyze the relationships between geo-environmental characteristics and the risk of JE cases. At the study sites, the JE cases were not spatially clustered at the village or district (global) level, whereas there was a spatial cluster at the district (local) level. Diversity-related features for JE patients at the district and village levels were detected at two sites. In the southern counties, the distance of a village from a road was related to the village-level JE risk (OR: 0.530, 95 CI: 0.297–0.947, P = 0.032), and the number of township-level JE cases was linked to the distance of the district center from the road (R =-0.467, P = 0.025) and road length (R = 0.516, P = 0.012) in the administrative area. In northern China, the modified normalized difference water index (MNDWI) in the 5 km buffer around the village was related to village-level JE risk (OR: 0.702, 95% CI: 0.524–0.940, P = 0.018), and the number of township-level JE cases was related to the MNDWI in the administrative region (R =-0.522, P = 0.038). This study elucidates the spatial distribution patterns of JE cases and risk, as well as correlated geo-environmental features, at various spatial scales. This study will significantly assist the JE control efforts of the local Centers for Disease Control and Prevention (CDC), which is the base-level CDC, particularly concerning the allocation of medicine and medical staff, the development of immunological plans, and the allocation of pesticides and other control measures for the mosquito vectors of JE.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

Global stability analysis and modelling onchocerciasis transmission dynamics with control measures

ABSTRACT Background: Onchocerciasis infection is one of the neglected tropical diseases targeted for eradication by 2030. The disease is usually transmitted to humans through the bites of black flies. These black flies mostly breed near well-oxygenated fast-running water bodies. The disease is common in mostly remote agricultural villages near rivers and streams. Objective: In this study, a deterministic model describing the infection dynamics of human onchocerciasis disease with control measures is presented. Methods: We derived the model’s reproductive number and used a stability theorem of a Metzler matrix to show that disease-free equilibrium is both locally and globally asymptotically stable whenever the reproductive number is less than one. Parameter contribution was conducted using sensitivity analysis. The model endemic equation is shown to be a cubic polynomial in the presence of infected immigrants and a quadratic form in their absence. Results: When the inflow of infected immigrants is null, the model endemic equation may admit a unique equilibrium if the reproductive number is greater than one, or admits multiple endemic equilibria if the reproductive number is less than unity. We carried out a sensitivity analysis to identify the significant parameters that contribute to onchocerciasis spread. Conclusion: Onchocerciasis disease can be eradicated if the importation of infected immigrants is properly monitored. The integration of the One Health concept in the public health system is key in tackling the emergence and spread of diseases.

Modeling gas flow in low-permeability formations: An efficient combination of mixed finite elements and high order time integration schemes

Numerical simulation of gas flow in low permeability formations is a challenging task due to the high nonlinearity induced by (i) the compressibility of the gas, (ii) the Klinkenberg slippage effect and (iii) the Langmuir adsorption of the gas on the pore surface. Because of these nonlinearities, modeling gas flow in low permeability formations requires a great deal of computational effort. In this work, we develop an efficient numerical model using advanced spatial and temporal discretization methods for a simultaneous solution of the coupled equations of gas flow, cubic Peng-Robinson equation of state, slippage effect and Langmuir adsorption. The spatial discretization is performed with the lumped hybrid formulation of the mixed finite element method which is well adapted for fluid flow in heterogeneous porous media. The time integration is performed with high-order methods via the method of lines (MOL) which allows large time steps and efficient solution of the nonlinear system of equations. Numerical experiments, performed for gas extraction in a heterogeneous domain, point out the high efficiency of the new model since it can be until 10 times more efficient than the classical first-order time discretization method. Results of global sensitivity analysis show that the intrinsic gas-phase permeability and the Klinkenberg factor are the most influential parameters controlling the pumping rate in the case of gas extraction in a homogeneous domain.

Barriers to environmental education in Ethiopia: do they differ from a global analysis?

Environmental education in primary schools plays a critical role in fostering pro-environmental values and behavior among young children. However, multiple contextual barriers hinder the teaching of environmental issues in schools. Drawing on the evident gap observed in Ethiopian primary teachers’ beliefs and actual practices, the study elucidates the major barriers teachers encounter in teaching environmental issues. Interviews, grounded in phenomenology, were conducted with teachers in four primary schools. The analysis outlined four ranked categories: training and capacity, administrative and logistical, safety concerns, and attitudinal. Most teachers feel hindered by their lack of pedagogical knowledge for teaching children. While most of the barriers are common in global analyses, the current study also identified noble barriers. Suggestions for tackling the barriers and areas for further inquiry are discussed.

Efficient global sensitivity analysis method for dynamic models in high dimensions

AbstractDynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs.

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.