Mechanistic Computational Model Research Articles

Multiphysics performance evaluation of thermoelectric generator arrays

Thermoelectric generators (TEGs) are widely used nowadays in heat recovery and power generation applications. TEGs are connected in series and/or parallel configurations in an array to meet the voltage and/or current requirements of electric load. Therefore, it is necessary to examine the electrical performance of different TEG array configurations employed in TEG systems. In the present study, a hydro-thermoelectric Multiphysics computational model is experimentally validated by integrating the computational fluid mechanics (CFD) model with the TEG model to test the electrical performance of various TEG array configurations in terms of load voltage, current, associated power, and conversion efficiency. Each TEGs array configuration’s performance is evaluated by varying the hot water temperature from 27 °C to 42 °C and hot/cold water flowrate from 0.5 L/min to 2 L/min. Three various configurations including series and a combination of series and parallel connections were designed. The findings show that the series configuration of the TEGs array generates higher electric power than series and parallel combined configurations whereas the latter achieves the maximum power point at a higher electric current than the series configuration. The comparative analysis of CFD model with the experimental results indicates a maximum discrepancy of 7 %. For thermoelectric power-generating systems, the proposed model might serve as a benchmark for optimizing TEGs array configurations according to the electric load requirements.

Digital twins for cardiac electrophysiology: state of the art and future challenges.

Cardiac arrhythmias remain amajor cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as apromising strategy to realize the vision of precision cardiology. While there is no uniform definition of adigital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides ashort overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable asignificant precision medicine-based advancement in cardiac arrhythmia management.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

Contact lens fitting and changes in the tear film dynamics: mathematical and computational models review.

This review explores mathematical models, blinking characterization, and non-invasive techniques to enhance understanding and refine clinical interventions for ocular conditions, particularly for contact lens wear. The review evaluates mathematical models in tear film dynamics and their limitations, discusses contact lens wear models, and highlights computational mechanical models. It also explores computational techniques, customization of models based on individual blinking dynamics, and non-invasive diagnostic tools like high-speed cameras and advanced imaging technologies. Mathematical models provide insights into tear film dynamics but face challenges due to simplifications. Contact lens wear models reveal complex ocular physiology and design aspects, aiding in lens development. Computational mechanical models explore eye biomechanics, often integrating tear film dynamics into a Multiphysics framework. While different computational techniques have their advantages and disadvantages, non-invasive tools like OCT and thermal imaging play a crucial role in customizing these Multiphysics models, particularly for contact lens wearers. Recent advancements in mathematical modeling and non-invasive tools have revolutionized ocular health research, enabling personalized approaches. The review underscores the importance of interdisciplinary exploration in the Multiphysics approach involving tear film dynamics and biomechanics for contact lens wearers, promoting advancements in eye care and broader ocular health research.

Evolution of stress field and plastic failure characteristics non-isobaric narrow gas storage spaces

The uncertainty of stress distribution and plastic damage threatens the safety of underground gas storage facilities. The bi-directional computational mechanics model is established first, the analytical solution for stress calculation and the implicit equation of plastic zone boundaries are derived. Then, the distribution of stress field in non-isobaric narrow gas storage spaces are studied, and the distribution law of plastic zone is revealed. The results show that: (1) The analytical solution of the surrounding rock of the non-isobaric gas storage roadway is derived, and the implicit equation of the boundary of the plastic zone is obtained; (2) The maximum principal stress exhibits an axisymmetric distribution, gradually decreasing with the increase of the distance; (3) As the stress P0 increases, the pressure equilibrium point increases; as λ increases, the pressure equilibrium point decreases first and then increases; (4) λ is more sensitive to roof failure than the two gangs; When λ is less than 1, the “elliptical” plastic zone appears at the roof, manifested as tensile and shear failure; when λ is 1, the “circular” plastic zone appear, manifested as shear failure; when λ is greater than 1, the elliptical plastic zone appears at the both sides, manifested as shear failure.

Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.

Introduction: Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. Methods: We calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. Results: Our results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. Discussion: This project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.

A computational mechanical constitutive modeling method based on thermally-activated microstructural evolution and strengthening mechanisms

A mechanical constitutive relationship is the basis for the design and application of structural materials, and the establishment of a mechanical constitutive relationship generally relies on experimental data. Herein a computational mechanical constitutive modeling method is proposed, in which all required parameters possess unambiguous physical interpretations and/or can be obtained from accurate numerical simulations. With the inclusion of interaction between dislocations and crystal defects, the dislocation density and grain size evolution with strain for polycrystalline metallic materials are mathematically modeled, and the corresponding contribution to strength is analyzed, further a computational mechanical constitutive relationship can be obtained. Its effectiveness is verified by comparing it with experimental stress-strain relationships for Cu, Al-Mg, and Cu-W alloys. This method might be a powerful tool for the design of structural materials with desirable mechanical properties.

Computational modeling of cardiac electrophysiology and arrhythmogenesis.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias. from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient clinical data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook towards potential future advances, including the combination of mechanistic modeling and machine learning / artificial intelligence, is provided. As the field of cardiology is embarking on a journey towards precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

Towards the Biomechanics of Alzheimer’s Disease: Computational Models

AbstractBackgroundPhysical changes in the brain microenvironment are highly associated with changes in mechanosensitivity of neuronal cells and mechanical properties of brain tissue. The progression of amyloid plaques and neurofibrillary tangles in the brain network significantly affects brain microstructure in Alzheimer’s disease (AD). To better understand the underlying mechanisms of AD, it is crucial to explore the effect of neuropathologies that cause brain tissue softening.MethodWe propose a microstructural model of brain white matter characterized by axons embedded in an extracellular matrix (ECM). The microscale is described by a representative volume element and includes features such as mechanical properties of axons and ECM, distribution of axon diameters, and axon tortuosity from experimental observations. The aim of this work is to investigate how the presence of β‐amyloid as hard senile plaques in the ECM and neurofibrillary tangles leads to softer mechanical properties of the brain during AD. The model is validated by simulating healthy brain tissue and comparing the homogenized response with existing experimental data.ResultPreliminary analyses to validate the model have shown tissue behavior similar to that seen in the mechanical experiments. In the case of AD, as expected, changes in the mechanical properties of the ECM due to the presence of hard senile plaques, demyelination due to the detachment of hyperphosphorylated tau proteins and neuronal death significantly affect the material properties at the macroscale.ConclusionThe combination of computational mechanical models with experimental observations opens a window to assess the structure‐function relationship of brain tissues in neuropathological disorders, which may be particularly useful to precisely design biomarkers for early detection or more informed therapeutic strategies.References• Schäfer, J. Weickenmeier, and E. Kuhl, “The interplay of biochemical and biomechanical degeneration in Alzheimer’s disease”, Comput. Methods Appl. Mech. Eng., vol. 352, pp. 369‐388, 2019.• M. Hall, E. Moeendarbary, and G.K. Sheridan, “Mechanobiology of the brain in ageing and Alzheimer’s disease”, Eur. J. Neurosci., pp. 1‐28, 2020.• Budday, G. Sommer, C. Birkl, C. Langkammer, J. Haybaeck, J. Kohnert, M. Bauer, F. Paulsen, P. Steinmann, E. Kuhl, G.A. Holzapfel, “Mechanical characterization of human brain tissue”, Acta Biomater., pp. 319‐340, 2017.

Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data.

Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant's body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients' goals.

Inferring rock strength and fault activation from high-resolution in situ Vp/Vs estimates surrounding induced earthquake clusters

Fluid injection/extraction activity related to hydraulic fracturing can induce earthquakes. Common mechanisms attributed to induced earthquakes include elevated pore pressure, poroelastic stress change, and fault loading through aseismic slip. However, their relative influence is still an open question. Estimating subsurface rock properties, such as pore pressure distribution, crack density, and fracture geometry can help quantify the causal relationship between fluid-rock interaction and fault activation. Inferring rock properties by means of indirect measurement may be a viable strategy to help identify weak structures susceptible to failure in regions where increased seismicity correlates with industrial activity, such as the Western Canada Sedimentary Basin. Here we present in situ estimates of Vp/Vs for 34 induced earthquake clusters in the Kiskatinaw area in northeast British Columbia. We estimate significant changes of up to ±4.5% for nine clusters generally associated with areas of high injection volume. Predominantly small spatiotemporal Vp/Vs variations suggest pore pressure increase plays a secondary role in initiating earthquakes. In contrast, computational rock mechanical models that invoke a decreasing fracture aspect ratio and increasing fluid content in a fluid-saturated porous medium that are consistent with the treatment pressure history better explain the observations.

Design of knowledge-based mechanistic model of atherosclerotic cardiovascular disease for in silico trials

Abstract In silico trials applying a computational model (CM) of atherosclerotic cardiovascular disease (ASCVD) to virtual patients receiving alternative treatments provide an attractive option to complement randomised clinical trials (RCTs) by adding comparative effectiveness data and facilitating the demonstration of drug benefit. In silico modelling allows comparison between treatments with each virtual patient being his own control, and is not limited by the number of patients, comparative arms or trial duration. This study aims at building a knowledge-based mechanistic model of ASCVD. Once validated, the model will be used to run in silico clinical trials to compare the benefit of inclisiran, an siRNA targeting PCSK9 mRNA, vs other lipid-lowering therapies (LLT) on cardiovascular (CV) events in patients with ASCVD. An extensive literature review was performed to identify pathophysiological mechanisms involved in ASCVD and therapeutic mechanisms of action (MOAs). Every piece of knowledge extracted from the literature is awarded a strength of evidence grading to allow tracking of uncertainty in the model. A panel of multidisciplinary experts reviewed knowledge models and subsequent modelling hypotheses to validate their relevance. Knowledge was translated into mathematical equations. Each functional relationship between entities is represented by a biochemical/biophysical reaction with its reaction rate. A system of ordinary differential equations provides dynamics of modelled biological entities. A strategy of model calibration/validation was defined with the expert panel, by selecting relevant RCT and registry data, that the model should be able to reproduce. A virtual population was generated to account for inter-patient variability. A mechanistic CM of ASCVD (including 72 biological entities, 750 parameters) was built from knowledge, describing lipoproteins metabolism and cholesterol homeostasis, dynamics of atherosclerotic plaque growth with lipoproteins infiltration in the intima and evolution of lipidic, necrotic and fibrotic tissues as well as plaque rupture leading to myocardial infarction, ischemic stroke, lower extremity arterial disease or CV death. It also includes the impact of several risk factors (age, sex medical history, diabetes, hypertension, smoking, systemic inflammation, chronic kidney disease) and available LLT (atorvastatin, rosuvastatin, ezetimibe, evolocumab and inclisiran). After calibration, the model and virtual population reproduce pharmacokinetics of LLT, lipoproteins decrease under combinations of LLT and associated reduction in clinical events. ASCVD is ideally suited for in silico modelling: extensive literature is available regarding the pathophysiology, therapeutic MOAs are known and easily integrated in the model, clinically relevant outcomes are adequate model outputs, RCT and registry data exist to calibrate/validate the model. The next step is validation of the model before using it to run in silico trials.Plaque growth and rupture modelMulti-scale in silico model

Physics-informed graph neural network emulation of soft-tissue mechanics

Modern computational soft-tissue mechanics models have the potential to offer unique, patient-specific diagnostic insights. The deployment of such models in clinical settings has been limited however, due to the excessive computational costs incurred when performing mechanical simulations using conventional numerical solvers. An alternative approach to obtaining results in clinically relevant time frames is to make use of a computationally efficient surrogate model, called an emulator, in place of the numerical simulator. In this work, we propose an emulation framework for soft-tissue mechanics which builds on traditional approaches in two ways. Firstly, we use a Graph Neural Network (GNN) to perform emulation. GNNs can naturally handle the unique soft-tissue geometry of a given patient, without requiring any low-order approximations to be made. Secondly, the emulator is trained in a physics-informed manner to minimise a potential energy functional, meaning that no costly numerical simulations are required for training. We present results showing that our framework allows for highly accurate emulation for a range of soft-tissue mechanical models, while making predictions several orders of magnitude more quickly than the simulator.

A computational modeling on thermodynamic performances of BeX(X = Ag, Au) intermetallics

The present work reports thermodynamics of BeX(X = Ag, Au) intermetallics in terms of their unit cell volume, bulk moduli, molar heat capacity at constant volume, Gruneisen parameter, thermal expansion coefficient and Debye temperature using a computational quantum mechanical modeling in the frame work of Density-functional theory (DFT). The thermodynamic parameters were studied under temperature range of 0–1200 K and pressure range of 0–40 GPa. The molar heat capacity at constant volume for the considered intermetallics followed Debye T3 law and Dulong-Petit limit was achieved at certain temperature. Further, to understand the highest modes of vibrations in the materials, Debye temperatures for BeAg and BeAu were estimated as 430 K and 335 K, respectively at 0 K and 0 GPa. Furthermore, the thermal expansion coefficient described the dependence of expansion and contraction of the materials on changing temperature, depicting the strength of atomic bonding. Its high value indicated weak atomic bonding, which led to the low melting temperature in the solids.

Mechanistic Computational Modeling of Implantable, Bioresorbable Drug Release Systems.

Implantable, bioresorbable drug delivery systems offer an alternative to current drug administration techniques; allowing for patient-tailored drug dosage, while also increasing patient compliance. Mechanistic mathematical modeling allows for the acceleration of the design of the release systems, and for prediction of physical anomalies that are not intuitive and may otherwise elude discovery. This study investigates short-term drug release as a function of water-mediated polymer phase inversion into a solid depot within hours to days, as well as long-term hydrolysis-mediated degradation and erosion of the implant over the next few weeks. Finite difference methods are used to model spatial and temporal changes in polymer phase inversion, solidification, and hydrolysis. Modeling reveals the impact of non-uniform drug distribution, production and transport of H+ ions, and localized polymer degradation on the diffusion of water, drug, and hydrolyzed polymer byproducts. Compared to experimental data, the computational model accurately predicts the drug release during the solidification of implants over days and drug release profiles over weeks from microspheres and implants. This work offers new insight into the impact of various parameters on drug release profiles, and is a new tool to accelerate the design process for release systems to meet a patient specific clinical need.

The pH-dependent lactose metabolism of Lactobacillus delbrueckii subsp. bulgaricus: An integrative view through a mechanistic computational model.

The fermentation process of milk to yoghurt using Lactobacillus delbrueckii subsp. bulgaricus in co-culture with Streptococcus thermophilus is hallmarked by the breakdown of lactose to organic acids such as lactate. This leads to a substantial decrease in pH - both in the medium, as well as cytosolic. The latter impairs metabolic activities due to the pH-dependence of enzymes, which compromises microbial growth. To quantitatively elucidate the impact of the acidification on metabolism of L. bulgaricus in an integrated way, we have developed a proton-dependent computational model of lactose metabolism and casein degradation based on experimental data. The model accounts for the influence of pH on enzyme activities as well as cellular growth and proliferation of the bacterial population. We used a machine learning approach to quantify the cell volume throughout fermentation. Simulation results show a decrease in metabolic flux with acidification of the cytosol. Additionally, the validated model predicts a similar metabolic behaviour within a wide range of non-limiting substrate concentrations. This computational model provides a deeper understanding of the intricate relationships between metabolic activity and acidification and paves the way for further optimization of yoghurt production under industrial settings.

Role of lung lobar sliding on parenchymal distortion during breathing.

Sliding between lung lobes along lobar fissures is a poorly understood aspect of lung mechanics. The objective of this study was to test the hypothesis that lobar sliding helps reduce distortion in the lung parenchyma during breathing. Finite element models of left lungs with geometries and boundary conditions derived from medical images of human subjects were developed. Effect of lobar sliding was studied by comparing nonlinear finite elastic contact mechanics simulations that allowed and disallowed lobar sliding. Lung parenchymal distortion during simulated breath-holds and tidal breathing was quantified with the model's spatial mean anisotropic deformation index (ADI), a measure of directional preference in volume change that varies spatially in the lung. Models that allowed lobar sliding had significantly lower mean ADI (i.e., lesser parenchymal distortion) than models that disallowed lobar sliding under simulations of both tidal breathing (5.3% median difference, P = 0.008, n = 8) and lung deformation between breath-holds at total lung capacity and functional residual capacity (3.2% median difference, P = 0.03, n = 6). This effect was most pronounced in the lower lobe where lobar sliding reduced parenchymal distortion with statistical significance, but not in the upper lobe. In addition, more lobar sliding was correlated with greater reduction in distortion between sliding and nonsliding models in our study cohorts (Pearson's correlation coefficient of 0.95 for tidal breathing, 0.87 for breath-holds, and 0.91 for the combined dataset). These findings are consistent with the hypothesis that lung lobar sliding reduces parenchymal distortion during breathing.NEW & NOTEWORTHY The role of lobar sliding in lung mechanics is poorly understood. Delineating this role could help explain how breathing is affected by anatomical differences between subjects such as incomplete and missing lobar fissures. We used computational contact mechanics models of lungs from human subjects to delineate the effect of lobar sliding by comparing simulations that allowed and disallowed sliding. We found evidence consistent with the hypothesis that lung lobar sliding reduces parenchymal distortion during breathing.

Learning to predict future locations with internally generated theta sequences.

Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, we reported recently that the position represented by CA1 place cells at a given theta phase corresponds to the location where animals were or will be located at a fixed time interval into the past or future assuming the animal ran at its typical, not the current, speed through that part of the environment. This coding scheme leads to longer theta trajectories, larger place fields and shallower phase precession in areas where animals typically run faster. Here we present a mechanistic computational model that accounts for these experimental observations. The model consists of a continuous attractor network with short-term synaptic facilitation and depression that internally generates theta sequences that advance at a fixed pace. Spatial locations are then mapped onto the active units via modified Hebbian plasticity. As a result, neighboring units become associated with spatial locations further apart where animals run faster, reproducing our earlier experimental results. The model also accounts for the higher density of place fields generally observed where animals slow down, such as around rewards. Furthermore, our modeling results reveal that an artifact of the decoding analysis might be partly responsible for the observation that theta trajectories start behind the animal's current position. Overall, our results shed light on how the hippocampal code might arise from the interplay between behavior, sensory input and predefined network dynamics.