Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models

AbstractMathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. The focus in this paper is to study the regeneration of liver after drug-induced depletion of hepatocytes, in which surviving dividing and migrating hepatocytes must squeeze through a blood vessel network to fill the emerged lesions. Here, the cells’ response to mechanical stress might significantly impact on the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined quantitative understanding of the cell-biomechanical impact on the closure of drug-induced lesions in liver. Our model represents each cell individually, constructed as a physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow and divide, and infer the nature of their mechanical elements and their parameters from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models. This effect is expected to be even more present in chronic liver disease, where tissue stiffens and excess collagen narrows pores for cells to squeeze through.

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A quantitative high-resolution computational mechanics cell model for growing and regenerating tissues

Biomechanics and Modeling in Mechanobiology ◽

10.1007/s10237-019-01204-7 ◽

2019 ◽

Vol 19 (1) ◽

pp. 189-220 ◽

Cited By ~ 2

Author(s):

Paul Van Liedekerke ◽

Johannes Neitsch ◽

Tim Johann ◽

Enrico Warmt ◽

Ismael Gonzàlez-Valverde ◽

...

Keyword(s):

High Resolution ◽

Blood Vessels ◽

Cell Model ◽

Medical Decision ◽

Drug Induced ◽

Tissue Organization ◽

A Cell ◽

Mechanical Elements ◽

Scalable Network ◽

The Impact

AbstractMathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. This paper considers as showcase example the regeneration of liver after drug-induced depletion of hepatocytes, in which the surviving and dividing hepatocytes must squeeze in between the blood vessels of a network to refill the emerged lesions. Here, the cells’ response to mechanical stress might significantly impact the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined and quantitative understanding of the impact of cell-biomechanical effects on the closure of drug-induced lesions in liver. Our model represents each cell individually and is constructed by a discrete, physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow, and divide, and the nature and parameters of their mechanical elements can be inferred from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a largely rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models. To stress generality of the approach, the liver simulations were complemented by monolayer and multicellular spheroid growth simulations. In summary, our model can give quantitative insight in many tissue organization processes, permits hypothesis testing in silico, and guide experiments in situations in which cell mechanics is considered important.

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Faculty Opinions recommendation of Transcranial DC stimulation in fibromyalgia: optimized cortical target supported by high-resolution computational models.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.12144957.13300055 ◽

2011 ◽

Author(s):

Josimari Melo DeSantana

Keyword(s):

High Resolution ◽

Computational Models

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Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽

10.3390/antiox10020229 ◽

2021 ◽

Vol 10 (2) ◽

pp. 229

Author(s):

JunHyuk Woo ◽

Hyesun Cho ◽

YunHee Seol ◽

Soon Ho Kim ◽

Chanhyeok Park ◽

...

Keyword(s):

Oxidative Stress ◽

Mitochondrial Dysfunction ◽

Computational Models ◽

Neuronal Damage ◽

Living Organism ◽

The Body ◽

Mitochondrial Functions ◽

A Cell ◽

The Brain ◽

And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

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Drug-induced pneumonitis detected earlier by 18F-FDG-PET than by high-resolution CT: a case report with non-Hodgkin’s lymphoma

Annals of Nuclear Medicine ◽

10.1007/s12149-008-0183-7 ◽

2008 ◽

Vol 22 (8) ◽

pp. 719-722 ◽

Cited By ~ 12

Author(s):

Tomohiko Yamane ◽

Osami Daimaru ◽

Satoshi Ito ◽

Takeshi Nagata ◽

Kazuhiko Yoshiya ◽

...

Keyword(s):

Case Report ◽

High Resolution ◽

Fdg Pet ◽

Hodgkin’S Lymphoma ◽

Hodgkin's Lymphoma ◽

Drug Induced ◽

High Resolution Ct ◽

Non Hodgkin’S Lymphoma ◽

Non Hodgkin's Lymphoma ◽

18F Fdg

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Order and stochasticity in the folding of individual Drosophila genomes

Nature Communications ◽

10.1038/s41467-020-20292-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sergey V. Ulianov ◽

Vlada V. Zakharova ◽

Aleksandra A. Galitsyna ◽

Pavel I. Kos ◽

Kirill E. Polovnikov ◽

...

Keyword(s):

Random Walk ◽

High Resolution ◽

3D Genome ◽

Topologically Associating Domains ◽

Chromatin Folding ◽

A Cell ◽

Single Nucleus ◽

High Level ◽

Cell To Cell Variability

AbstractMammalian and Drosophila genomes are partitioned into topologically associating domains (TADs). Although this partitioning has been reported to be functionally relevant, it is unclear whether TADs represent true physical units located at the same genomic positions in each cell nucleus or emerge as an average of numerous alternative chromatin folding patterns in a cell population. Here, we use a single-nucleus Hi-C technique to construct high-resolution Hi-C maps in individual Drosophila genomes. These maps demonstrate chromatin compartmentalization at the megabase scale and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which closely resembles the TAD profile in the bulk in situ Hi-C data. Over 40% of TAD boundaries are conserved between individual nuclei and possess a high level of active epigenetic marks. Polymer simulations demonstrate that chromatin folding is best described by the random walk model within TADs and is most suitably approximated by a crumpled globule build of Gaussian blobs at longer distances. We observe prominent cell-to-cell variability in the long-range contacts between either active genome loci or between Polycomb-bound regions, suggesting an important contribution of stochastic processes to the formation of the Drosophila 3D genome.

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

10.20944/preprints202002.0178.v1 ◽

2020 ◽

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Comparison of narrow band imaging with high resolution white light endoscopy for the assessment of non-steroidal anti-inflammatory drug induced gastroduodenal injury

Gut ◽

10.1136/gut.2011.239301.223 ◽

2011 ◽

Vol 60 (Suppl 1) ◽

pp. A106-A106

Author(s):

V. Subramanian ◽

E. Telakis ◽

J. Mannath ◽

V. Ramappa ◽

M. Desai ◽

...

Keyword(s):

High Resolution ◽

White Light ◽

Narrow Band ◽

Narrow Band Imaging ◽

Drug Induced ◽

Inflammatory Drug ◽

White Light Endoscopy ◽

Anti Inflammatory

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

International Journal of Molecular Sciences ◽

10.3390/ijms21062114 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2114

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Self-Moving Cell Linear Motor Using Piezo Actuators

Volume 6B: 18th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc2001/vib-21490 ◽

2001 ◽

Author(s):

Jaehwan Kim ◽

Jinho Lee

Keyword(s):

High Resolution ◽

Shell Structure ◽

Linear Motor ◽

The Self ◽

Fast Speed ◽

A Cell ◽

Working Principle

Abstract The concept of a new linear motor that uses piezo-stack actuator is demonstrated. The working principle is far different from the conventional inchworm motor. This motor is based on the self-moving cell concept. The linear motor has three cells and each cell is constructed with one piezo-stack actuator and a shell structure. A cell train is constructed by connecting these cells and the train is fitted into a giudeway with interference. By activating each cell in succession, the train can move along the guideway. The moving motion of the motor is tested. Since this linear motor uses piezo-stack actuator with unified clamping cell, there is possibility to produce fast speed, high resolution and large push force.

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