The Warburg Effect Reinterpreted 100 yr on: A First-Principles Stoichiometric Analysis and Interpretation from the Perspective of ATP Metabolism in Cancer Cells.

The Warburg Effect is a longstanding enigma in cancer biology. Despite the passage of 100 yr since its discovery, and the accumulation of a vast body of research on the subject, no convincing biochemical explanation has been given for the original observations of aerobic glycolysis in cancer cell metabolism. Here, we have worked out a first-principles quantitative analysis of the problem from the principles of stoichiometry and available electron balance. The results have been interpreted using Nath's unified theory of energy coupling and adenosine triphosphate (ATP) synthesis, and the original data of Warburg and colleagues have been analyzed from this new perspective. Use of the biomass yield based on ATP per unit substrate consumed, [Formula: see text], or the Nath-Warburg number, NaWa has been shown to excellently model the original data on the Warburg Effect with very small standard deviation values, and without employing additional fitted or adjustable parameters. Based on the results of the quantitative analysis, a novel conservative mechanism of synthesis, utilization, and recycling of ATP and other key metabolites (eg, lactate) is proposed. The mechanism offers fresh insights into metabolic symbiosis and coupling within and/or among proliferating cells. The fundamental understanding gained using our approach should help in catalyzing the development of more efficient metabolism-targeting anticancer drugs.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models.

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

Concentration-QTc modeling of sitravatinib in patients with advanced solid malignancies.

Sitravatinib (MGCD516) is an orally available, small molecule, tyrosine kinase inhibitor that has been evaluated in patients with advanced solid tumors. Concentration-corrected QT interval (QTc; C-QTc) modeling was undertaken, using 767 matched concentration-ECG observations from 187 patients across two clinical studies in patients with advanced solid malignancies, across a dose range of 10-200 mg, via a linear mixed-effects (LME) model. The effect on heart rate (HR)-corrected QT interval via Fridericia's correction method (QTcF) at the steady-state maximum concentration (Cmax,ss) for the sitravatinib proposed therapeutic dosing regimen (100 mg malate once daily [q.d.]) without and with relevant intrinsic and extrinsic factors were predicted. No significant changes in HR from baseline were observed. Hysteresis between sitravatinib plasma concentration and change in QTcF from baseline (ΔQTcF) was not observed. There was no significant relationship between sitravatinib plasma concentration and ΔQTcF. The final C-QTc model predicted a mean (90% confidence interval [CI]) ΔQTcF of 3.92 (1.95-5.89) ms and 2.94 (0.23-6.10) ms at the proposed therapeutic dosing regimen in patients with normal organ function (best case scenario) and patients with hepatic impairment (worst-case scenario), respectively. The upper bounds of the 90% CIs were below the regulatory threshold of concern of 10 ms. The results of the described C-QTc analysis, along with corroborating results from nonclinical safety pharmacology studies, indicate that sitravatinib has a low risk of QTc interval prolongation at the proposed therapeutic dose of 100 mg malate q.d.

Establishing Standardization Guidelines For Finite-Element Optomechanical Simulations of Refractive Laser Surgeries: An Application to Photorefractive Keratectomy.

Purpose: Computational models can help clinicians plan surgeries by accounting for factors such as mechanical imbalances or testing different surgical techniques beforehand. Different levels of modeling complexity are found in the literature, and it is still not clear what aspects should be included to obtain accurate results in finite-element (FE) corneal models. This work presents a methodology to narrow down minimal requirements of modeling features to report clinical data for a refractive intervention such as PRK. Methods: A pipeline to create FE models of a refractive surgery is presented: It tests different geometries, boundary conditions, loading, and mesh size on the optomechanical simulation output. The mechanical model for the corneal tissue accounts for the collagen fiber distribution in human corneas. Both mechanical and optical outcome are analyzed for the different models. Finally, the methodology is applied to five patient-specific models to ensure accuracy. Results: To simulate the postsurgical corneal optomechanics, our results suggest that the most precise outcome is obtained with patient-specific models with a 100 µm mesh size, sliding boundary condition at the limbus, and intraocular pressure enforced as a distributed load. Conclusions: A methodology for laser surgery simulation has been developed that is able to reproduce the optical target of the laser intervention while also analyzing the mechanical outcome. Translational Relevance: The lack of standardization in modeling refractive interventions leads to different simulation strategies, making difficult to compare them against other publications. This work establishes the standardization guidelines to be followed when performing optomechanical simulations of refractive interventions.

Cell deformability drives fluid-to-fluid phase transition in active cell monolayers.

Cell deformability is an essential determinant for tissue-scale mechanical nature, such as fluidity and rigidity, and is thus crucial for tissue homeostasis and stable developmental processes. However, large-scale simulations of deformable cells have been restricted to those of polygonal-shaped cells, limiting our understanding of populations of arbitrarily deformable cells, such as mesenchymal, amoeboid cells, and nonconfluent epithelial cells. Here, we present an efficient approach for simulating large populations of nonpolygonally deformable cells with considerably higher computational efficiency than existing methods. Using the method, we demonstrate that the densely packed active cell population interacting via excluded volume interactions exhibits a fluid-to-fluid transition. An experimentally measurable index of topological defects, defined using the number of neighboring cells, is also proposed to characterize this transition. This study provides a flexible approach to tissue-scale cell population and a broader perspective on the biological fluid phases.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process.

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK®to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Decoding the principle of cell-fate determination for its reverse control.

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas.

Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.

In vitro effects and mathematical modelling of CTCE-9908 (a chemokine receptor 4 antagonist) on melanoma cell survival.

CTCE-9908, a CXC chemokine receptor 4 (CXCR4) antagonist, prevents CXCR4 phosphorylation and inhibits the interaction with chemokine ligand 12 (CXCL12) and downstream signalling pathways associated with metastasis. This study evaluated the in vitro effects of CTCE-9908 on B16 F10 melanoma cells with the use of mathematical modelling. Crystal violet staining was used to construct a mathematical model of CTCE-9908 B16 F10 (melanoma) and RAW 264.7 (non-cancerous macrophage) cell lines on cell viability to predict the half-maximal inhibitory concentration (IC50). Morphological changes were assessed using transmission electron microscopy. Flow cytometry was used to assess changes in cell cycle distribution, apoptosis via caspase-3, cell survival via extracellular signal-regulated kinase1/2 activation, CXCR4 activation and CXCL12 expression. Mathematical modelling predicted IC50 values from 0 to 100 h. At IC50, similar cytotoxicity between the two cell lines and ultrastructural morphological changes indicative of cell death were observed. At a concentration 10 times lower than IC50, CTCE-9908 induced inhibition of cell survival (p = 0.0133) in B16 F10 cells but did not affect caspase-3 or cell cycle distribution in either cell line. This study predicts CTCE-9908 IC50 values at various time points using mathematical modelling, revealing cytotoxicity in melanoma and non-cancerous cells. CTCE-9908 significantly inhibited melanoma cell survival at a concentration 10 times lower than the IC50 in B16 F10 cells but not RAW 264.7 cells. However, CTCE-9908 did not affect CXCR4 phosphorylation, apoptosis,\ or cell cycle distribution in either cell line.

Modeling and experimental study of the intervention forces between the guidewire and blood vessels.

A profound investigation of the interaction mechanics between blood vessels and guidewires is necessary to achieve safe intervention. An interactive force model between guidewires and blood vessels is established based on cardiovascular fluid dynamics theory and contact mechanics, considering two intervention phases (straight intervention and contact intervention at a corner named "J-vessel"). The contributing factors of the force model, including intervention conditions, guidewire characteristics, and intravascular environment, are analyzed. A series of experiments were performed to validate the availability of the interactive force model and explore the effects of influential factors on intervention force. The intervention force data were collected using a 2-DOF mechanical testing system instrumented with a force sensor. The guidewire diameter and material were found to significantly impact the intervention force. Additionally, the intervention force was influenced by factors such as blood viscosity, blood vessel wall thickness, blood flow velocity, as well as the interventional velocity and interventional mode. The experiment of the intervention in a coronary artery physical vascular model confirms the practicality validation of the predicted force model and can provide an optimized interventional strategy for vascular interventional surgery. The enhanced intervention strategy has resulted in a considerable reduction of approximately 21.97 % in the force exerted on blood vessels, effectively minimizing the potential for complications associated with the interventional surgery.

Agent-based approaches for biological modeling in oncology: A literature review.

CONTEXT: Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY: This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS: Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Understanding stoichiometric constraints on growth using resource use efficiency imbalances.

Growth is a function of the net accrual of resources by an organism. Energy and elemental contents of organisms are dynamically linked through their uptake and allocation to biomass production, yet we lack a full understanding of how these dynamics regulate growth rate. Here, we develop a multivariate imbalance framework, the growth efficiency hypothesis, linking organismal resource contents to growth and metabolic use efficiencies, and demonstrate its effectiveness in predicting consumer growth rates under elemental and food quantity limitation. The relative proportions of carbon (%C), nitrogen (%N), phosphorus (%P), and adenosine triphosphate (%ATP) in consumers differed markedly across resource limitation treatments. Differences in their resource composition were linked to systematic changes in stoichiometric use efficiencies, which served to maintain relatively consistent relationships between elemental and ATP content in consumer tissues and optimize biomass production. Overall, these adjustments were quantitatively linked to growth, enabling highly accurate predictions of consumer growth rates.

Assessment of human dermal absorption of flame retardant additives in polyethylene and polypropylene microplastics using 3D human skin equivalent models.

To overcome ethical and technical challenges impeding the study of human dermal uptake of chemical additives present in microplastics (MPs), we employed 3D human skin equivalent (3D-HSE) models to provide first insights into the dermal bioavailability of polybrominated diphenyl ether (PBDEs) present in MPs; and evaluated different factors influencing human percutaneous absorption of PBDEs under real-life exposure scenario. PBDEs were bioavailable to varying degrees (up to 8 % of the exposure dose) and percutaneous permeation was evident, albeit at low levels (≤0.1 % of the exposure dose). While the polymer type influenced the release of PBDEs from the studied MPs to the skin, the polymer type was less important in driving the percutaneous absorption of PBDEs. The absorbed fraction of PBDEs was strongly correlated (r2 = 0.88) with their water solubility, while the dermal permeation coefficient Papp of PBDEs showed strong association with their molecular weight and logKOW. More sweaty skin resulted in higher bioavailability of PBDEs from dermal contact with MPs than dry skin. Overall, percutaneous absorption of PBDEs upon skin contact with MPs was evident, highlighting, for the first time, the potential significance of the dermal pathway as an important route of human exposure to toxic additive chemicals in MPs.

How do in vitro digestion and cell metabolism affect the biological activity and phenolic profile of grape juice and wine.

Cabernet Sauvignon grape juice and wine underwent in vitro digestion, resulting in a reduction of most phenolic compounds (10%-100% decline), notably impacting anthocyanins (82%-100% decline) due to pH variations. However, specific phenolics, including p-hydroxybenzoic, protocatechuic, vanillic, p-coumaric, gallic and syringic acids, and coumarin esculetin, increased in concentration (10%-120%). Grape juice and wine samples showed comparable polyphenolic profile during all phases of digestion. Antioxidant activity persisted, and inhibition of angiotensin-I converting enzyme was improved after the digestion process, likely because of increased concentrations of listed phenolic acids and esculetin. Digested grape juice displayed comparable or superior bioactivity to red wine, indicating it as a promising source of accessible grape polyphenols for a broader audience. Nevertheless, Caco-2 cell model metabolization experiments revealed that only 3 of 42 analyzed compounds passed to the basolateral compartment, emphasizing the significant impact of digestion on polyphenol bioactivity, suggesting potential yet unmeasurable and overlooked implications for human health.

Robustness of carbon-ion radiotherapy against DNA damage repair associated radiosensitivity variation based on a biophysical model.

BACKGROUND: Interpatient variation of tumor radiosensitivity is rarely considered during the treatment planning process despite its known significance for the therapeutic outcome. PURPOSE: To apply our mechanistic biophysical model to investigate the biological robustness of carbon ion radiotherapy (CIRT) against DNA damage repair interference (DDRi) associated patient-to-patient variability in radiosensitivity and its potential clinical advantages against conventional radiotherapy approaches. METHODS AND MATERIALS: The "UNIfied and VERSatile bio response Engine" (UNIVERSE) was extended by carbon ions and its predictions were compared to a panel of in vitro and in vivo data including various endpoints and DDRi settings within clinically relevant dose and linear energy transfer (LET) ranges. The implications of UNIVERSE predictions were then assessed in a clinical patient scenario considering DDRi variance. RESULTS: UNIVERSE tests well against the applied benchmarks. While in vitro survival curves were predicted with an R2 > 0.92, deviations from in vivo RBE data were less than 5.6% The conducted paradigmatic patient plan study implies a markedly reduced significance of DDRi based radiosensitivity variability in CIRT (13% change of D 50 ${{D}_{50}}$ in target) compared to conventional radiotherapy (62%) and that boosting the LET within the target further amplifies this robustness of CIRT (8%). In the case of heightened tumor radiosensitivity, a dose de-escalation strategy for photons allows a reduction of the maximum effective dose within the normal tissue (NT) from a D 2 ${{D}_2}$ of 2.65 to 1.64 Gy, which lies below the level found for CIRT ( D 2 ${{D}_2}$  = 2.41 Gy) for the analyzed plan and parameters. However, even after de-escalation, the integral effective dose in the NT is found to be substantially higher for conventional radiotherapy in comparison to CIRT ( D m e a n ${{D}_{mean}}$ of 0.75, 0.46, and 0.24 Gy for the conventional plan, its de-escalation and CIRT, respectively). CONCLUSIONS: The framework offers adequate predictions of in vitro and in vivo radiation effects of CIRT while allowing the consideration of DRRi based solely on parameters derived from photon data. The results of the patient planning study underline the potential of CIRT to minimize important sources of interpatient divergence in therapy outcome, especially when combined with techniques that allow to maximize the LET within the tumor. Despite the potential of de-escalation strategies for conventional radiotherapy to reduce the maximum effective dose in the NT, CIRT appears to remain a more favorable option due to its ability to reduce the integral effective dose within the NT.

Coarse-grained modeling and dynamics tracking of nanoparticles diffusion in human gut mucus.

The gastrointestinal (GI) tract's mucus layer serves as a critical barrier and a mediator in drug nanoparticle delivery. The mucus layer's diverse molecular structures and spatial complexity complicates the mechanistic study of the diffusion dynamics of particulate materials. In response, we developed a bi-component coarse-grained mucus model, specifically tailored for the colorectal cancer environment, that contained the two most abundant glycoproteins in GI mucus: Muc2 and Muc5AC. This model demonstrated the effects of molecular composition and concentration on mucus pore size, a key determinant in the permeability of nanoparticles. Using this computational model, we investigated the diffusion rate of polyethylene glycol (PEG) coated nanoparticles, a widely used muco-penetrating nanoparticle. We validated our model with experimentally characterized mucus pore sizes and the diffusional coefficients of PEG-coated nanoparticles in the mucus collected from cultured human colorectal goblet cells. Machine learning fingerprints were then employed to provide a mechanistic understanding of nanoparticle diffusional behavior. We found that larger nanoparticles tended to be trapped in mucus over longer durations but exhibited more ballistic diffusion over shorter time spans. Through these discoveries, our model provides a promising platform to study pharmacokinetics in the GI mucus layer.

A Spatial Interpolation Approach to Assign Magnetic Resonance Imaging-Derived Material Properties for Finite Element Models of Adeno-Associated Virus Infusion Into a Recurrent Brain Tumor.

Adeno-associated virus (AAV) is a clinically useful gene delivery vehicle for treating neurological diseases. To deliver AAV to focal targets, direct infusion into brain tissue by convection-enhanced delivery (CED) is often needed due to AAV's limited penetration across the blood-brain-barrier and its low diffusivity in tissue. In this study, computational models that predict the spatial distribution of AAV in brain tissue during CED were developed to guide future placement of infusion catheters in recurrent brain tumors following primary tumor resection. The brain was modeled as a porous medium, and material property fields that account for magnetic resonance imaging (MRI)-derived anatomical regions were interpolated and directly assigned to an unstructured finite element mesh. By eliminating the need to mesh complex surfaces between fluid regions and tissue, mesh preparation was expedited, increasing the model's clinical feasibility. The infusion model predicted preferential fluid diversion into open fluid regions such as the ventricles and subarachnoid space (SAS). Additionally, a sensitivity analysis of AAV delivery demonstrated that improved AAV distribution in the tumor was achieved at higher tumor hydraulic conductivity or lower tumor porosity. Depending on the tumor infusion site, the AAV distribution covered 3.67-70.25% of the tumor volume (using a 10% AAV concentration threshold), demonstrating the model's potential to inform the selection of infusion sites for maximal tumor coverage.

Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. METHODS: Realistic hepatic steatosis models encompassing clinical FF range (0-60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5-100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. RESULTS: The virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95-1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. CONCLUSION: Our study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.

A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework.

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

Measuring mechanical cues for modeling the stromal matrix in 3D cell cultures.

A breast-cancer tumor develops within a stroma, a tissue where a complex extracellular matrix surrounds cells, mediating the cancer progression through biomechanical and -chemical cues. Current materials partially mimic the stromal matrix in 3D cell cultures but methods for measuring the mechanical properties of the matrix at cell-relevant-length scales and stromal-stiffness levels are lacking. Here, to address this gap, we developed a characterization approach that employs probe-based microrheometry and Bayesian modeling to quantify length-scale-dependent mechanics and mechanical heterogeneity as in the stromal matrix. We examined the interpenetrating network (IPN) composed of alginate scaffolds (for adjusting mechanics) and type-1 collagen (a stromal-matrix constituent). We analyzed viscoelasticity: absolute-shear moduli (stiffness/elasticity) and phase angles (viscous and elastic characteristics). We determined the relationship between microrheometry and rheometry information. Microrheometry reveals lower stiffness at cell-relevant scales, compared to macroscale rheometry, with dependency on the length scale (10 to 100 µm). These data show increasing IPN stiffness with crosslinking until saturation (≃15 mM of Ca2+). Furthermore, we report that IPN stiffness can be adjusted by modulating collagen concentration and interconnectivity (by polymerization temperature). The IPNs are heterogeneous structurally (in SEM) and mechanically. Interestingly, increased alginate crosslinking changes IPN heterogeneity in stiffness but not in phase angle, until the saturation. In contrast, such changes are undetectable in alginate scaffolds. Our nonlinear viscoelasticity analysis at tumor-cell-exerted strains shows that only the softer IPNs stiffen with strain, like the stromal-collagen constituent. In summary, our approach can quantify the stromal-matrix-related viscoelasticity and is likely applicable to other materials in 3D culture.