Computational Model Research Articles

Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism.

Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.

Exploring temporal sensitivity in the brain using multi-timescale language models: an EEG decoding study

Abstract The brain’s ability to perform complex computations at varying timescales is crucial, ranging from understanding single words to grasping the overarching narrative of a story. Recently, multi-timescale long short-term memory (MT-LSTM) models (Mahto et al. 2020; Jain et al. 2020) have been introduced, which use temporally-tuned parameters to induce sensitivity to different timescales of language processing (i.e. related to near/distant words). However, there has not been an exploration of the relation between such temporally-tuned information processing in MT-LSTMs and the brain’s language processing using high temporal resolution recording modalities, such as electroencephalography (EEG). To bridge this gap, we used an EEG dataset recorded while participants listened to Chapter 1 of “Alice in Wonderland” and trained ridge regression models to predict the temporally-tuned MT-LSTM embeddings from EEG responses. Our analysis reveals that EEG signals can be used to predict MT-LSTM embeddings across various timescales. For longer timescales, our models produced accurate predictions within an extended time window of ±2 s around word onset, while for shorter timescales, significant predictions are confined to a narrow window ranging from −180 ms to 790 ms. Intriguingly, we observed that short timescale information is not only processed in the vicinity of word onset but also at distant time points. These observations underscore the parallels and discrepancies between computational models and the neural mechanisms of the brain. As word embeddings are used more as in silico models of semantic representation in the brain, a more explicit consideration of timescale-dependent processing enables more targeted explorations of language processing in humans and machines.

Methods for Implementing Tactile Sensations in Virtual Space by Integrating Physical Objects with Their Computer Models

The paper presents original methods and approaches for integrating, from the user's point of view, three-dimensional virtual models visible to them in a virtual reality headset, and their physical prototypes. This allows for the addition of some important elements of physical reality to the virtual environment, such as tactile and muscle-motor sensations. Developed solutions are based on the use of such wireless devices as HTC Vive Trackers, which determine their position and orientation in space, and can be placed on various objects of the real world. An original approach to making a tracking system by means of two HTC Base Stations and several Vive Trackers with selecting one anchor element is proposed, as well as new methods for real-time integration of physical reality objects with their virtual models by using functional control schemes with own blocks for trackers. Based on methods and approaches proposed in this work, software modules were implemented. Approbation of these modules in the VirSim virtual environment system, which was developed in Scientific Research Institute for System Analysis of the Russian Academy of Sciences, showed that the solutions developed are adequate and effective, and can have practical application in virtual environment systems and training complexes.

Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability.

Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability. We utilized a biophysics model to rapidly simulate cardiac growth following mitral valve regurgitation (MVR). Here we developed a two-tiered Bayesian History Matching approach augmented with Gaussian process emulators for efficient calibration of model parameters to align with growth outcomes within a 95 % confidence interval. We first generated a synthetic data set to assess the accuracy of our framework, and the effect of changes in data uncertainty on growth predictions. We then calibrated our model to match baseline and chronic canine MVR data and used an independent data set to successfully validate the ability of our calibrated model to accurately predict cardiac growth probability. The combined biophysics and machine learning modeling framework we proposed in this study can be easily translated to predict patient-specific cardiac growth.

Study on the axial compression behavior of masonry columns strengthened with engineered cementitious composite splints and fiber-reinforced polymer strips

This paper presents experimental and analytical studies on the axial compressive properties and failure mechanics of 19 strengthened masonry columns. The masonry column was strengthened singly and compositely by applying engineered cementitious composite (ECC) splints and wrapping discontinuous fiber-reinforced polymer (FRP) strips around the sides of the masonry column. The effects of the type of strengthening material (ECC splints or FRP strips), the thickness of the ECC splint, and the FRP strip strengthening area ratio on the failure mode, peak load, strain behavior, and energy dissipation of the specimens were analyzed and discussed in this experimental study. Under the appropriate parameters, the best strengthening effect was observed for the compositely strengthened masonry columns. In particular, the ductility, bearing capacity, and energy dissipation of masonry columns can be significantly improved by utilizing FRP with lower tensile strength, a higher FRP strip strengthening area ratio and a thicker ECC splint. Based on the existing computational models and code calculation methods for masonry columns strengthened with ECC splints and FRP strips, a formula for calculating the compressive bearing capacity of strengthened masonry columns was derived. The error between all the calculated and the measured results was less than 7.1%.

Conical Intersections at Interfaces Revealed by Phase-Cycling Interface-Specific Two-Dimensional Electronic Spectroscopy (i2D-ES).

Conical intersections (CIs) hold significant stake in manipulating and controlling photochemical reaction pathways of molecules at interfaces and surfaces by affecting molecular dynamics therein. Currently, there is no tool for characterizing CIs at interfaces and surfaces. To this end, we have developed phase-cycling interface-specific two-dimensional electronic spectroscopy (i2D-ES) and combined it with advanced computational modeling to explore nonadiabatic CI dynamics of molecules at the air/water interface. Specifically, we integrated the phase locked pump pulse pair with an interface-specific electronic probe to obtain the two-dimensional interface-specific responses. We demonstrate that the nonadiabatic transitions of an interface-active azo dye molecule that occur through the CIs at the interface have different kinetic pathways from those in the bulk water. Upon photoexcitation, two CIs are present: one from an intersection of an optically active S2 state with a dark S1 state and the other from the intersection of the progressed S1 with the ground state S0. We find that the molecular conformations in the ground state are different for interfacial molecules. The interfacial molecules are intimately correlated with the locally populated excited state S2 being farther away from the CI region. This leads to slower nonadiabatic dynamics at the interface than in bulk water. Moreover, we show that the nonadiabatic transition from the S1 dark state to the ground state is significantly longer at the interface than that in the bulk, which is likely due to the orientationally restricted configuration of the excited state at the interface. Our findings suggest that orientational configurations of molecules manipulate reaction pathways at interfaces and surfaces.

DdrC, a unique DNA repair factor from D. radiodurans, senses and stabilizes DNA breaks through a novel lesion-recognition mechanism.

The bacterium Deinococcus radiodurans is known to survive high doses of DNA damaging agents. This resistance is the result of robust antioxidant systems which protect efficient DNA repair mechanisms that are unique to Deinococcus species. The protein DdrC has been identified as an important component of this repair machinery. DdrC is known to bind to DNA in vitro and has been shown to circularize and compact DNA fragments. The mechanism and biological relevance of this activity is poorly understood. Here, we show that the DdrC homodimer is a lesion-sensing protein that binds to two single-strand (ss) or double-strand (ds) breaks. The immobilization of DNA breaks in pairs consequently leads to the circularization of linear DNA and the compaction of nicked DNA. The degree of compaction is directly proportional with the number of available nicks. Previously, the structure of the DdrC homodimer was solved in an unusual asymmetric conformation. Here, we solve the structure of DdrC under different crystallographic environments and confirm that the asymmetry is an endogenous feature of DdrC. We propose a dynamic structural mechanism where the asymmetry is necessary to trap a pair of lesions. We support this model with mutant disruption and computational modeling experiments.

Computational modeling of cell motility and clusters formation in enzyme-sensitive hydrogels

AbstractIn this paper, we propose an extension of a previous model of cell motility in tissue engineering applications recently developed by the authors. Achieving large-scale production of neo-tissue through biofabrication technologies remains challenging owing to the need of thoroughly optimizing all the relevant process variables, a task hardly attainable through solely trial and error approaches. Therefore, the present work is intended to provide a valid and effective computational-based support for neo-tissue formation, with a specific focus on the preliminary phase of such process, in which cells move through a polymeric scaffold (hydrogel) and then compact into clusters. Cell motility is modeled by resorting to the phase-field method, and by incorporating diffusion of nutrients from the external culture bath as well as the expression by cells of chemoattractant substances that bias the random path they otherwise would follow. The previous model has been enriched by additionally encompassing the secretion of enzymes by cells that cleave the crosslinks between the hydrogel polymer chains. As such, in the present model hydrogel degradation exhibits spatio-temporal variations in its chemo-physical properties related to the local amount of enzymes, which deeply affects cell motility. Numerical results showcase the pivotal importance of the cells micro-environment properties for their crawling in hydrogel scaffolds, opening towards the development of a predictive computational-aided optimization tool for neo-tissue growth in bioprinted scaffolds.

An intelligent predictive and optimized wastewater treatment plant.

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.

Days to re-entrainment following the spring and autumn changes in local clock time: beyond simple heuristics

Circadian disruption, a breakdown in the regularity of activity patterns across the 24-h day, can lead to a variety of maladies. Some individuals and organizations object to the twice-yearly, seasonal changes in local time because it contributes to circadian disruption. The number of days required to re-entrain the circadian system to the new local time following transitions to or from daylight saving time is not completely understood, but several simple rules of thumb (i.e., heuristics) have been offered to minimize the days to re-entrainment and, thus, circadian disruption (e.g., go for a morning walk). Recently, the authors developed a computational model for predicting circadian phase from calibrated light-dark exposure patterns, based largely on the pioneering work of Kronauer and colleagues. This model was used here to predict the days to re-entrainment of the circadian systems of “larks” and “owls” to a new local time if they were exposed to one of three specific light interventions. Simulations showed that the timing of a light intervention must account for chronotypes (e.g., timing of minimum core body temperature) and direction of shift (i.e., phase advance or delay) to achieve re-entrainment to the time change more quickly. Simple heuristics are not necessarily adequate for minimizing the days to re-entrainment.

Dopamine-mediated interactions between short- and long-term memory dynamics.

In dynamic environments, animals make behavioral decisions based on the innate valences of sensory cues and information learnt about these cues across multiple timescales1-3. However, it remains unclear how the innate valence of a sensory stimulus affects acquisition of learnt valence information and subsequent memory dynamics. Here we show that in the Drosophila brain, interconnected short- and long-term memory units of the mushroom body jointly regulate memory via dopamine signals that encode innate and learnt sensory valences. Through time-lapse, in vivo voltage-imaging studies of neural spiking in >500 flies undergoing olfactory associative conditioning, we found that protocerebral posterior lateral 1 dopamine neurons (PPL1-DANs)4 heterogeneously and bi-directionally encode innate and learnt valences of punishment, reward, and odor cues. During learning, these valence signals regulate memory storage and extinction in mushroom body output neurons (MBONs)5. In initial conditioning bouts, PPL1-γ1pedc and PPL1-γ2α'1 neurons control short-term memory formation, which weakens inhibitory feedback from MBON-γ1pedc>α/β to PPL1-α'2α2 and PPL1-α3. During further conditioning, this diminished feedback allows these two PPL1-DANs to encode the net innate plus learnt valence of the conditioned odor cue, which gates long-term memory formation. A computational model constrained by the fly connectome6,7 and our spiking data explains how dopamine signals mediate the circuit interactions between short- and long-term memory traces, yielding predictions that our experiments confirm. Overall, the mushroom body achieves flexible learning via the integration of innate and learnt valences within parallel learning units sharing feedback interconnections. This hybrid physiologic-anatomic mechanism may be a general means by which dopamine regulates memory dynamics in other species and brain structures, including the vertebrate basal ganglia.

Cognitive Mechanisms of Aberrant Self-Referential Social Perception in Psychosis and Bipolar Disorder: Insights from Computational Modeling.

Individuals with schizophrenia (SZ) and bipolar disorder (BD) show disruptions in self-referential gaze perception-a social perceptual process related to symptoms and functioning. However, our current mechanistic understanding of these dysfunctions and relationships is imprecise. The present study used mathematical modeling to uncover cognitive processes driving gaze perception abnormalities in SZ and BD, and how they relate to cognition, symptoms, and social functioning. We modeled the behavior of 28 SZ, 38 BD, and 34 controls (HC) in a self-referential gaze perception task using drift-diffusion models (DDM) parameterized to index key cognitive components: drift rate (evidence accumulation efficiency), drift bias (perceptual bias), start point (expectation bias), threshold separation (response caution), and non- decision time (encoding/motor processes). Results revealed that aberrant gaze perception in SZ and BD was driven by less efficient evidence accumulation, perceptual biases predisposing self-referential responses, and greater caution (SZ only). Across SZ and HC, poorer social functioning was related to greater expectation biases. Within SZ, perceptual and expectancy biases were associated with hallucination and delusion severity, respectively. These findings indicate that diminished evidence accumulation and perceptual biases may underlie altered gaze perception in patients and that SZ may engage in compensatory cautiousness, sacrificing response speed to preserve accuracy. Moreover, biases at the belief and perceptual levels may relate to symptoms and functioning. Computational modeling can, therefore, be used to achieve a more nuanced, cognitive process-level understanding of the mechanisms of social cognitive difficulties, including gaze perception, in individuals with SZ and BD.

Designing a cost-effective policy mix for transition toward net-zero emissions: a case study of the mid-term plan by 2035 of Taiwan

This study explores the design of a cost-effective climate policy mix for transition toward net-zero emissions in the mid-term (by 2035). Its novelty lies in the methodological advancements through the development of a soft-linking procedure that integrates a top-down computable general equilibrium model with a bottom-up energy system model. The integrated assessment focuses on the climate policy mix under the framework of Taiwan’s Climate Change Response Act passed in 2023. The analysis shows that a policy mix combining investments in energy transition with user-pay hybrid pricing measures can facilitate cost-effective mitigation and enhance public acceptance. The proposed hybrid pricing includes cost-based electricity pricing and a two-step carbon pricing strategy that encourages early action (before 2030) for larger emitters. Finally, we demonstrate that accelerating the transition is crucial, as it reduces the economic costs of meeting emission targets by inducing further investments in energy efficiency improvement and lowering the carbon pricing levels required to close the abatement gap.

Computational optimisation and modelling of sacrificial anode placement and dimension for maximising the corrosion prevention of screw piles

Computational optimisation and modelling of sacrificial anode placement and dimension for maximising the corrosion prevention of screw piles

Investigation into the impact of charging rates on the stress development within silicon composite electrodes.

Silicon, renowned for its remarkable energy density, has emerged as a focal point in the pursuit of high-energy storage solutions for the next generation. Nevertheless, silicon electrodes are known to undergo significant volume expansion during the insertion of lithium ions, leading to structural deformation and the development of internal stresses, and causing a rapid decline in battery capacity and overall lifespan. To gain deeper insights into the intricacies of charge rate effects, this study employs a combination of in situ measurements and computational modeling to elucidate the cyclic performance of composite silicon electrodes. The findings derived from the established model and curvature measurement system unveil the substantial alterations in stress and deformation as a consequence of varying charge rates. Notably, the active layer experiences compressive forces that diminish as the charge rate decreases. At a charge rate of 0.2, the active layer endures a maximum stress of 89.145 MPa, providing a comprehensive explanation for the observed deterioration in cycling performance at higher charge rates. This study not only establishes a fundamental basis for subsequent stress analyses of silicon electrodes but also lays a solid foundation for further exploration of the impact of charge rates on composite silicon electrodes.

Analytical and Computational Modeling of Relaxation Times for Non-Newtonian Fluids

With the availability of efficient and sophisticated finite element analysis (FEA) and computational fluid dynamics (CFD) tools, engineering designs are becoming more software-driven and simulation-based. However, the insights relevant to engineering designs tend to be hidden within massive temporal and spatial data produced with full-fledged three-dimensional simulations. In this paper, we present a preliminary study of the controlled intermittent dispensing of a typical non-Newtonian glue employed in the manufacturing of electric vehicles (EVs). The focus of the study is on the scaling issues derived from different computational and analytical models of interest and importance to the precision control of this non-Newtonian fluid, the lowest dynamic viscosity of which at extremely high shear rates is nearly four million times that of water. More specifically, the abrupt change of the inlet pressure with a constant outlet or ambient pressure and various modeling strategies for transient viscous internal flow with both Newtonian and non-Newtonian fluids are modeled and compared. The analytical and computational results of the developing Newtonian fluid, i.e., water, are derived and computed for validation and verification purposes before the actual applications to the developing non-Newtonian fluid. The concept of a well-established relaxation time before the onset of the steady solution for Newtonian fluids has been validated with both analytical and computational approaches before its expansion and adoption to non-Newtonian fluids with complex rheological behaviors. Other issues attributed to transient operations and precision controls of non-Newtonian fluid delivery involve the pressure pulse and pressure wave propagation within the flexible pipe with compressible or almost incompressible non-Newtonian fluids with a constant pressure at the outlet and a constant mass flow rate or average axial velocity at the inlet, which will be addressed in a separate paper.

Ab Initio Modelling of g-ZnO Deposition on the Si (111) Surface

Recent studies show that zinc oxide (ZnO) nanostructures have promising potential as an absorbing material. In order to improve the optoelectronic properties of the initial system, this paper considers the process of adsorbing multilayer graphene-like ZnO onto a Si (111) surface. The density of electron states for two- and three-layer graphene-like zinc oxide on the Si (111) surface was obtained using the Vienna ab-initio simulation package by the DFT method. A computer model of graphene-like Zinc oxide on a Si (111)-surface was created using the DFT+U approach. One-, two- and three-plane-thick graphene-zinc oxide were deposited on the substrate. An isolated cluster of Zn3O3 was also considered. The compatibility of g-ZnO with the S (100) substrate was tested, and the energetics of deposition were calculated. This study demonstrates that, regardless of the possible configuration of the adsorbing layers, the Si/ZnO structure remains stable at the interface. Calculations indicate that, in combination with lower formation energies, wurtzite-type structures turn out to be more stable and, compared to sphalerite-type structures, wurtzite-type structures form longer interlayers and shorter interplanar distances. It has been shown that during the deposition of the third layer, the growth of a wurtzite-type structure becomes exothermic. Thus, these findings suggest a predictable relationship between the application method and the number of layers, implying that the synthesis process can be modified. Consequently, we believe that such interfaces can be obtained through experimental synthesis.

Simulation model for advanced astigmatism corrective eyeglasses and contact lenses

Mathematical eye models are crucial for developing treatment strategies for astigmatic eye conditions. Extensive research has been conducted on various treatment techniques, and also various surfaces in glasses and contact lenses. This study aims to address the existing research gap by optimizing mathematical eye models for specific astigmatic conditions. Using Liu and Brennan's model, we simulate the correction of astigmatism with glasses and contact lenses. We optimize toroidal and aspherical lens surfaces to maximize their effectiveness in correcting astigmatism and evaluate image quality using modulation transfer function (MTF), point spread function (PSF), and focal spot diagrams. Our results show that both glasses and contact lenses can effectively treat astigmatism, with glasses providing superior correction. However, individual factors such as oblique vision, decentration, and anterior surface asphericity can influence treatment outcomes. This study contributes to advancing ophthalmology by integrating computational models with clinical data to improve treatment planning and personalization.

Body plan evolvability: The role of variability in gene regulatory networks.

Recent computational modeling of early fruit fly (Drosophila) development has characterized the degree to which gene regulation networks can be robust to natural variability. In the first few hours of development, broad spatial gradients of maternally derived transcription factors activate embryonic gap genes. These gap patterns determine the subsequent segmented insect body plan through pair-rule gene expression. Gap genes are expressed with greater spatial precision than the maternal patterns. Computational modeling of the gap-gap regulatory interactions provides a mechanistic understanding for this robustness to maternal variability in wild-type (WT) patterning. A long-standing question in evolutionary biology has been how a system which is robust, such as the developmental program creating any particular species' body plan, is also evolvable, i.e. how can a system evolve or speciate, if the WT form is strongly buffered and protected? In the present work, we use the WT model to explore the breakdown of such Waddington-type 'canalization'. What levels of variability will push the system out of the WT form; are there particular pathways in the gene regulatory mechanism which are more susceptible to losing the WT form; and when robustness is lost, what types of forms are most likely to occur (i.e. what forms lie near the WT)? Manipulating maternal effects in several different pathways, we find a common gap 'peak-to-step' pattern transition in the loss of WT.We discuss these results in terms of the evolvability of insect segmentation, and in terms of experimental perturbations and mutations which could test the model predictions. We conclude by discussing the prospects for using continuum models of pattern dynamics to investigate a wider range of evo-devo problems.

Modelling the effect of cell motility on mixing and invasion in epithelial monolayers.

Collective cell invasion underlies several biological processes such as wound healing, embryonic development, and cancerous invasion. Here, we investigate the impact of cell motility on invasion in epithelial monolayers and its coupling to cellular mechanical properties, such as cell-cell adhesion and cortex contractility. We develop a two-dimensional computational model for cells with active motility based on the cellular Potts model, which predicts that the cellular invasion speed is mainly determined by active cell motility and is independent of the biological and mechanical properties of the cells. We also find that, in general, motile cells out-compete and invade non-motile cells, however, this can be reversed by differential cell proliferation. Stable coexistence of motile and static cell types is also possible for certain parameter regimes.