Biology subjects

Karniadakis, G. E.

Publications and source records attributed to Karniadakis, G. E..

12 recordsLinked to original sources

bioRxiv2026

A Multiscale Signaling--Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease

Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage-RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for robust parameter inference. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage-RBC contact, generating spatially resolved trajectories of CD47-SIRP signaling and antibody-receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.

biophysics↗

bioRxiv2025

In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease

Sickle cell disease (SCD) is a genetic disorder that affects approximately 100,000 individuals in the United States and millions globally. Although curative therapies, such as stem cell transplant and gene therapy, are available, their application is limited by high cost and donor availability. Conseuently, drug therapy remains the most feasible treatment option for the majority of patients with SCD. To date, only four drugs have been approved by the FDA, but none of these treatments comprehensively address all SCD-related symptoms or crises, suggesting the pressing need for developing new drugs. Several high-throughput screening campaigns have been performed for SCD drug discovery based on the in vitro sickling of red blood cells (RBCs) and they have identified several hits. However, it is challenging to replicate the organ-specific oxygen level and physiological deoxygenation time in these in vitro RBC sickling assays. Furthermore, these assays do not consider the pharmacokinetics (PK) and pharmacodynamics (PD) of the identified drugs, which are essential to determine whether the drugs can provide robust and sustained efficacy in treated patients with SCD. To address these technical gaps, we have developed a computational platform to perform post-screening analysis of potential anti-sickling agents. This platform sequentially combines PK/PD models with a kinetic model of RBC sickling, enabling efficient prediction of the dosage-dependent therapeutic efficacy of various anti-sickling agents based on patient-specific hematological factors and organ-specific oxygen levels. We first demonstrate the effectiveness of our integrated platform by showcasing the therapeutic efficacy of two FDA-approved drugs, Hydroxyurea (HU) and voxelotor. Next, we evaluate the therapeutic efficacy of two potential anti-sickling agents under clinical trial, namely Bitopertin and osivelotor. Our findings suggest that Bitopertin exhibits anti-sickling effects that are considerably less pronounced than those of HU and voxelotor. On the other hand, osivelotor can achieve similar anti-sickling effects as voxelotor with significantly lower doses due to its improved PK properties. Furthermore, we show the versatility of the proposed platform in predicting the anti-sickling effect of multi-agent therapies and evaluate the conse-quences of drug noncompliance. In particular, our analysis indicates that noncompliance with voxelotor may result in rapid increases in RBC sickling, whereas osivelotor is likely to mitigate noncompliance-induced adverse effects due to improved PK properties. We further quantify the relationship between drug dosage and the duration of noncompliance that leads to loss of thera-peutic efficacy for voxelotor and osivelotor, providing guidance for optimizing dosage strategies to reduce the risk associated with noncompliance. In summary, our in silico platform serves as a valuable tool for post-screening analysis of potential anti-sickling agents by considering their PK and anti-sickling efficacy under patient-specific hemoglobin level and organ-specific oxygen level, thereby gaining insights into their potential therapeutic efficacy alone or in combination before clinical trials.

bioengineering↗

bioRxiv2025

A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning

Obstructive sleep apnea (OSA) is a common sleep disorder associated with increased cardiovascular and neurocognitive risks. While polysomnography remains the clinical gold standard for diagnosis, it is costly and unsuitable for large-scale or real-time screening. Electrocardiogram (ECG) signals offer a non-invasive, low-cost alternative for sleep apnea detection. We present a holistic new framework for OSA detection and forecasting using ECG data on two datasets: PhysioNet Apnea-ECG datasets (healthy patients with apnea), and OSASUD dataset (patients in a stroke unit). Our framework integrates feature engineering methods rooted in dynamical systems theory and statistical analysis. These features are used across a range of models, from conventional machine learning algorithms to novel deep learning architectures. To improve generalization and personalization, we incorporate transfer learning in two ways: across datasets to adapt models trained on large cohorts to smaller clinical datasets, and at the patient level to personalize models using limited individual data, hence demonstrating the use of precision medicine.

bioinformatics↗

bioRxiv2025

MR-AIV reveals in-vivo brain-wide fluid flow with physics-informed AI

The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport--especially in the deep brain--has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields--quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport ([~] 0.1 {micro}m/s) from rapid advective flow ([~] 3 {micro}m/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous media systems, from geophysics to tissue mechanics.

neuroscience↗

bioRxiv2024

In silico biophysics and rheology of blood and red blood cells in Gaucher Disease

Gaucher Disease (GD) is a rare genetic disorder characterized by a deficiency in the enzyme glucocerebrosidase, leading to the accumulation of glucosylceramide in various cells, including red blood cells (RBCs). This accumulation results in altered biomechanical properties and rheological behavior of RBCs, which may play an important role in blood rheology and the development of bone infarcts, avascular necrosis (AVN) and other bone diseases associated with GD. In this study, dissipative particle dynamics (DPD) simulations are employed to investigate the biomechanics and rheology of blood and RBCs in GD under various flow conditions. The model incorporates the unique characteristics of GD RBCs, such as decreased deformability and increased aggregation properties, and aims to capture the resulting changes in RBC biophysics and blood viscosity. This study is the first to explore the Youngs modulus and aggregation parameters of GD RBCs by validating simulations with confocal imaging and experimental RBC disaggregation thresholds. Through in silico simulations, we examine the impact of hematocrit, RBC disaggregation threshold, and cell stiffness on blood viscosity in GD. The results reveal three distinct domains of GD blood viscosity based on shear rate: the aggregation domain, where the RBC disaggregation threshold predominantly influences blood viscosity; the transition area, where both RBC aggregation and stiffness impact on blood viscosity; and the stiffness domain, where the stiffness of RBCs emerges as the primary determinant of blood viscosity. By quantitatively assessing RBC deformability, RBC disaggregation threshold, and blood viscosity in relation to bone disease, we find that the RBC aggregation properties, as well as their deformability and blood viscosity, may contribute to its onset. These findings enhance our understanding of how changes in RBC properties impact on blood viscosity and may affect bone health, offering a partial explanation for the bone complications observed in GD patients. Author summaryIn Gaucher Disease (GD), a genetic deficiency in the enzyme glucocerebrosidase leads to the accumulation of glucosylceramide in red blood cells (RBCs), resulting in altered biomechanical properties. These changes affect blood flow characteristics, particularly blood viscosity, and may contribute to bone health issues seen in GD patients, including bone infarcts, avascular necrosis (AVN), and other bone diseases. In our study, we apply dissipative particle dynamics (DPD) simulations to explore how GD impacts RBC behavior under various flow conditions. We model GD RBCs with decreased deformability and increased aggregation, examining how these properties influence blood viscosity across three distinct shear rate domains: aggregation, transition, and stiffness. By validating our simulations with confocal imaging data and experimental RBC disaggregation thresholds, we quantitatively assess the effects of RBC stiffness, aggregation, and hematocrit levels on blood flow in GD. We find that the RBC aggregation properties, deformability and blood viscosity, may contribute to the onset of bone disease. These findings improve our understanding of how changes in RBC properties influence blood viscosity and may contribute to bone health issues, providing a partial explanation for the bone complications observed in GD patients.

biophysics↗

bioRxiv2024

Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a process whose dysregulation is linked to neurodegenerative diseases like Alzheimers. Traditional approaches like particle tracking velocimetry (PTV) are limited by their reliance on single-plane two-dimensional measurements, which fail to capture the complex dynamics of CSF flow fully. To overcome these limitations, we employ Artificial Intelligence Velocimetry (AIV) to reconstruct three-dimensional velocities, infer pressure and wall shear stress, and quantify flow rates. Given the experimental nature of the data and inherent variability in biological systems, robust uncertainty quantification (UQ) is essential. Towards this end, we have modified the baseline AIV architecture to address aleatoric uncertainty caused by noisy experimental data, enhancing our measurement refinement capabilities. We also implement UQ for the model and epistemic uncertainties arising from the governing equations and network representation. Toward this end, we test multiple governing laws, representation models, and initializations. Our approach not only advances the accuracy of CSF flow quantification but also can be adapted to other applications that use physics-informed machine learning to reconstruct fields from experimental data, providing a versatile tool for inverse problems.

neuroscience↗

bioRxiv2024

Red blood cell passage through deformable interendothelial slits in the spleen: Insights into splenic filtration and hemodynamics

The spleen constantly clears altered red blood cells (RBCs) from the circulation, tuning the balance between RBC formation (erythropoiesis) and removal. The retention and elimination of RBCs occur predominantly in the open circulation of the spleen, where RBCs must cross submicron-wide inter-endothelial slits (IES). Several experimental and computational studies have illustrated the role of IES in filtrating the biomechanically and morphologically altered RBCs based on a rigid wall assumption. However, these studies also reported that when the size of IES is close to the lower end of clinically observed sizes (less than 0.5 m), an unphysiologically large pressure difference across the IES is required to drive the passage of normal RBCs, sparking debates on the feasibility of the rigid wall assumption. In this work, we perform a computational investigation based on dissipative particle dynamics (DPD) to explore the impact of the deformability of IES on the filtration function of the spleen. We simulate two deformable IES models, namely the passive model and the active model. In the passive model, we implement the worm-like string model to depict the IESs deformation as it interacts with blood plasma and allows RBC to traverse. In contrast, the active model involved regulating the IES deformation based on the local pressure surrounding the slit. To demonstrate the validity of the deformable model, we simulate the filtration of RBCs with varied size and stiffness by IES under three scenarios: 1) a single RBC traversing a single slit; 2) a suspension of RBCs traversing an array of slits, mimicking in vitro spleen-on-a-chip experiments; 3) RBC suspension passing through the 3D spleen filtration unit known as the splenon. Our simulation results of RBC passing through a single slit show that the deformable IES model offers more accurate predictions of the critical cell surface area to volume ratio that dictate the removal of aged RBCs from circulation compared to prior rigid-wall models. Our biophysical models of the spleen-on-a-chip indicates a hierarchy of filtration function stringency: rigid model > passive model > active model, providing a possible explanation of why the spleen-on-a-chip could overestimate the filtration function of IES. We also illustrate that the biophysical model of the splenon enables us to replicate the ex vivo experiments involving spleen filtration of malaria-infected RBCs. Taken together, our simulation findings indicate that the deformable IES model could serve as a mesoscopic representation of spleen filtration function closer to physiological reality, addressing questions beyond the scope of current experimental and computational models and enhancing our understanding of the fundamental flow dynamics and mechanical clearance processes within in the human spleen.

bioengineering↗

bioRxiv2023

ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results. Authors summaryMelih Agraz, after graduating from Dokuz Eylul University in Izmir, Turkiye, with a major in Mathematics from the Department of Education, he pursued his Masters degree at the same university in the field of Statistics. He furthered his education by obtaining a Ph.D. in Statistics from the Middle East Technical University, Ankara in Turkiye. As part of his academic journey, he also served as a Fulbright postdoctoral researcher at UC Berkeley in the United States. Following this, he worked as a postdoctoral researcher at Brown University and Beth Israel Hospital of Harvard Medical School. Currently, he holds the position of Assistant Professor in the Department of Statistics at Giresun University in Turkey. George Em Karniadakis is Professor of Applied Mathematics and Engineering at Brown University. He is a member of the National Academy of Engineering of USA. His interests include stochastic multiscale modeling of physical and biological systems, physics-informed machine learning, and deep neural operators. He has co-authored over 500 papers and five books, and he has the highest h-index in Applied Mathematics according to Google Scholar.

bioinformatics↗

bioRxiv2023

A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell disease

Being the largest lymphatic organ in the body, the spleen also constantly controls the quality of red blood cells (RBCs) in circulation through its two major filtration components, namely interendothelial slits (IES) and red pulp macrophages. In contrast to the extensive studies in understanding the filtration function of IES, there are relatively fewer works on investigating how the splenic macrophages retain the aged and diseased RBCs, i.e., RBCs in sickle cell disease (SCD). Herein, we perform a computational study informed by companion experiments to quantify the dynamics of RBCs captured and retained by the macrophages. We first calibrate the parameters in the computational model based on microfluidic experimental measurements for sickle RBCs under normoxia and hypoxia, as those parameters are not available in the literature. Next, we quantify the impact of a set of key factors that are expected to dictate the RBC retention by the macrophages in the spleen, namely, blood flow conditions, RBC aggregation, hematocrit, RBC morphology, and oxygen levels. Our simulation results show that hypoxic conditions could enhance the adhesion between the sickle RBCs and macrophages. This, in turn, increases the retention of RBCs by as much as five-fold, which could be a possible cause of RBC congestion in the spleen of patients with SCD. Our study on the impact of RBC aggregation illustrates a clustering effect, where multiple RBCs in one aggregate can make contact and adhere to the macrophages, leading to a higher retention rate than that resulting from RBC-macrophage pair interactions. Our simulations of sickle RBCs flowing past macrophages for a range of blood flow velocities indicate that the increased blood velocity could quickly attenuate the function of the red pulp macrophages on detaining aged or diseased RBCs, thereby providing a possible rationale for the slow blood flow in the open circulation of the spleen. Furthermore, we quantify the impact of RBC morphology on their tendency to be retained by the macrophages. We find that the sickle and granular-shaped RBCs are more likely to be filtered by macrophages in the spleen. This finding is consistent with the observation of low percentages of these two forms of sickle RBCs in the blood smear of SCD patients. Taken together, our experimental and simulation results aid in our quantitative understanding of the function of splenic macrophages in retaining the diseased RBCs and provide an opportunity to combine such knowledge with the current knowledge of the interaction between IES and traversing RBCs to apprehend the complete filtration function of the spleen in SCD.

bioengineering↗

bioRxiv2022

Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes

Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia to hyperglycemia. While dangerous hypoglycemia may lead to coma immediately, chronic hyperglycemia increases patients risks for cardiorenal and vascular diseases in the long run. In principle, an artificial pancreas - a closed-loop insulin delivery system requiring patients manually input insulin dosage according to the upcoming meals - could supply exogenous insulin to control the glucose levels and hence reduce the risks from hyperglycemia. However, insulin overdosing in some type 1 diabetic patients, who are physically active, can lead to unexpected hypoglycemia beyond the control of common artificial pancreas. Therefore, it is important to take into account the glucose decrease due to physical exercise when designing the next-generation artificial pancreas. In this work, we develop a deep reinforcement learning algorithm using a T1DM dataset, containing data from wearable devices, to automate insulin dosing for patients with T1DM. In particular, we build patient-specific computational models using systems biology informed neural networks (SBINN), to mimic the glucose-insulin dynamics for a few patients from the dataset, by simultaneously considering patient-specific carbohydrate intake and physical exercise intensity.

systems biology↗

bioRxiv2019

Systems biology informed deep learning for inferring parameters and hidden dynamics

Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems. Author summaryThe dynamics of systems biological processes are usually modeled using ordinary differential equations (ODEs), which introduce various unknown parameters that need to be estimated efficiently from noisy measurements of concentration for a few species only. In this work, we present a new "systems-informed neural network" to infer the dynamics of experimentally unobserved species as well as the unknown parameters in the system of equations. By incorporating the system of ODEs into the neural networks, we effectively add constraints to the optimization algorithm, which makes the method robust to noisy and sparse measurements.

bioinformatics↗

bioRxiv2017

Modeling Of Biomechanics And Biorheology Of Red Blood Cells In Type-2 Diabetes Mellitus

Erythrocytes in patients with type-2 diabetes mellitus (T2DM) are associated with reduced cell deformability and elevated blood viscosity, which contribute to impaired blood flow and other pathophysiological aspects of diabetes related vascular complications. In this study, by using a two-component red blood cell (RBC) model and systematic parameter variation, we perform detailed computational simulations to probe the alteration of the biomechanical, rheological and dynamic behavior of T2DM RBCs in response to morphological change and membrane stiffening. First, we examine the elastic response of T2DM RBCs subject to static tensile forcing and their viscoelastic relaxation response upon release of the stretching force. Second, we investigate the membrane fluctuations of T2DM RBCs and explore the effect of cell shape on the fluctuation amplitudes. Third, we subject the T2DM RBCs to shear flow and probe the effects of cell shape and effective membrane viscosity on their tank-treading movement. In addition, we model the cell dynamic behavior in a microfluidic channel with constriction and quantify the biorheological properties of individual T2DM RBCs. Finally, we simulate T2DM RBC suspensions under shear and compare the predicted viscosity with experimental measurements. Taken together these simulation results and their comparison with currently available experimental data are helpful in identifying a specific parametric model the first of its kind that best describes the main hallmarks of T2DM RBCs, which can be used in future simulation studies of hematologic complications of T2DM patients.

biophysics↗