Cells in Silico – introducing a high-performance framework for large-scale tissue modeling

Berghoff, Marco; Rosenbauer, Jakob; Hoffmann, Felix; Schug, Alexander

doi:10.1186/s12859-020-03728-7

Cited by 18 publications

(16 citation statements)

References 22 publications

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“…Hence, CiS has already been used for simulating tissues composing millions of cells [19]. Here, we briefly outline the main properties of the microscale, mesoscale and macroscale layers, and a more detailed description can be found in [19].…”

Section: Methodsmentioning

confidence: 99%

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Development of a Scoring Function for Comparing Simulated and Experimental Tumor Spheroids

Herold¹,

Behle

Rosenbauer

et al. 2022

Preprint

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Enormous progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), thus allowing cells to escape and invade alone or as multicellular collectives. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving internal organization of cells and external interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenging task. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel data-agnostic method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by our group. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable informing in silico spheroids based on their in vitro counterparts, and vice versa, thus enabling both basic and applied researchers to close the loop between modeling and experiments in cancer research.

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Section: Methodsmentioning

confidence: 99%

“…Cells in Silico is a framework for simulating the dynamics of cells and tissues at subcellular resolution, which was previously developed by our group [19]. It combines a Cellular Potts Model (CPM) at the microscale with nutrient and signal exchange at the mesoscale and an agent-based layer at the macroscale.…”

Section: Model Descriptionmentioning

confidence: 99%

Development of a Scoring Function for Comparing Simulated and Experimental Tumor Spheroids

Herold¹,

Behle

Rosenbauer

et al. 2022

Preprint

Self Cite

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“…We also use a cell–cell interaction model to simulate the fusion process of two spheroids (Figure 3d, Note S2, Supporting Information). [ 30 ] We use two 150 µm cell spheroids composed of 400 cells with cell–cell adhesive interactions to simulate the fusion process (Table S3, Supporting Information). The simulated deformation of double spheroids during the fusion process is consistent with the experimental results.…”

Section: Figurementioning

confidence: 99%

Assembly of Multi‐Spheroid Cellular Architectures by Programmable Droplet Merging

et al. 2020

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Reproduction of native tissues in vitro is important as a tool, as it both enables investigation of fundamental biological processes, and drug and toxicity screenings. In order to closely mimic complex tissues in vitro, artificial multicellular systems are created from different cell types in spatially ordered structures or welldefined geometries in a 3D microenvironment. [1-3] These systems can be built from building blocks [4-7] such as cell sheets, [8] cell-laden microgels, [5] cell spheroids, [9] and organoids. [10,11] Precise control of cellular composition and spatial distribution of building blocks within artificial multicellular systems allows for reconstitution of native tissues in their healthy and disease state in vitro. [12] There are a number of methodologies developed for fabrication of complex 3D cell systems in vitro. [3-7,13,14] Directed assembly allows manual positioning or stacking building blocks to form 3D architectures. [15,16] Birey et al. applied this method for fusion of two forebrain organoids in order to mimic the human brain development and demonstrate inter-neuronal migration. [15] The method of directed assembly is, however, manual and not compatible with high throughput. Remote assembly, such as, acoustic node, [14,17] magnetic cell levitation, [13,18] optical tweezers, [19] or laser-guided direct writing, [20] can achieve assembly of cells or spheroids against gravity or viscous forces. Chen et al. demonstrated the assembly of hepatic organoids by an acoustic node technique, and the technique was able to achieve formation of bile canaliculi networks resembling native hepatic tissue. [17] Souza et al. used magnetic cell levitation to manipulate a glioblastoma cell spheroid, and a human astrocyte spheroid in order to create a cell invasion model. [18] These methods depend on sophisticated equipment, paramagnetic media, or introduce a risk of laser-induced cell damage. Another common strategy used to fabricate multicellular architecture is assembly of cell-laden hydrogels or microgels, [5,21,22] or cell seeding on scaffolds. [7,23] However, these biomaterial-based methods failed to provide high cell packing density. The use of artificial scaffolds or gel matrices additionally lead to disadvantages for constructing 3D tissue models due to their influence on cell-cell interactions, autocrine, and paracrine signaling. 3D printing [3] is a promising method for designing and achieving multicellular architectures, but it is relatively slow, not always compatible with high throughput and relies on printable bio-inks for maintaining 3D structure and cell viability. Artificial multicellular systems are gaining importance in the field of tissue engineering and regenerative medicine. Reconstruction of complex tissue architectures in vitro is nevertheless challenging, and methods permitting controllable and high-throughput fabrication of complex multicellular architectures are needed. Here, a facile and high-throughput method is developed based on a tunable droplet-fusion technique, allowing prog...

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“…In particular, the computational cost increases rapidly with the number of simulated agents since the evolution of the system relies upon the interactions of the individual cells with both each other and the surrounding milieu. This makes ABMs extraordinarily challenging for simulating large biological systems on practical time and length scales (see, e.g., [ 36 , 37 ]). Another challenge arises when attempting to calibrate hybrid ABMs to experimental data as the models typically require measurements that span the micro- to macroscopic scales.…”

Section: Introductionmentioning

confidence: 99%

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

et al. 2021

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Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

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Cells in Silico – introducing a high-performance framework for large-scale tissue modeling

Cited by 18 publications

References 22 publications

Development of a Scoring Function for Comparing Simulated and Experimental Tumor Spheroids

Development of a Scoring Function for Comparing Simulated and Experimental Tumor Spheroids

Assembly of Multi‐Spheroid Cellular Architectures by Programmable Droplet Merging

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

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