Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer

Driving cell response through deep learning, a study in simulated 3D cell cultures

Computational simulations are becoming increasingly relevant in biomedical research, providing strategies to reproduce experimental results, improve the resolution of in-vitro experiments, and predict the system's behavior in untested conditions. Their use to determine the features associated with an extensive response to treatment and optimize treatment schedules has, however received little attention. To bridge this gap, we propose a deep learning framework capable of reliably classifying simulated time series data and identifying class-defining features. This information will be shown to be useful for the determination of which changes in treatment schedule elicit a more extensive cellular response. This analysis pipeline will be initially tested on a synthetic dataset created ad-hoc to identify its accuracy in identifying the most relevant portion of the signals. Successively this method will be applied to simulations describing the behaviors of populations of cancer cells treated with either one or two drugs in different concentrations. The proposed method will be shown to be effective in identifying which changes in the treatment protocol lead to a more extensive response to treatment. While lacking direct experimental validation, this result holds great potential for the integration of in-silico and in-vitro analyses and the effective optimization of experimental conditions in complex experimental setups.

Design of a custom-made device for real-time optical measurement of differential mineral concentrations in three-dimensional scaffolds for bone tissue engineering

Monitoring bone tissue engineered (TEed) constructs during their maturation is important to ensure the quality of applied protocols. Several destructive, mainly histochemical, methods are conventionally used to this aim, requiring the sacrifice of the investigated samples. This implies (i) to plan several scaffold replicates, (ii) expensive and time consuming procedures and (iii) to infer the maturity level of a given tissue construct from a cognate replica. To solve these issues, non-destructive techniques such as light spectroscopy-based methods have been reported to be useful. Here, a miniaturized and inexpensive custom-made spectrometer device is proposed to enable the non-destructive analysis of hydrogel scaffolds. Testing involved samples with a differential amount of calcium salt. When compared to a reference standard device, this custom-made spectrometer demonstrates the ability to perform measurements without requiring elaborate sample preparation and/or a complex instrumentation. This preliminary study shows the feasibility of light spectroscopy-based methods as useful for the non-destructive analysis of TEed constructs. Based on these results, this custom-made spectrometer device appears as a useful option to perform real-time/in-line analysis. Finally, this device can be considered as a component that can be easily integrated on board of recently prototyped bioreactor systems, for the monitoring of TEed constructs during their conditioning.

Development of an electrical impedance tomography set-up for the quantification of mineralization in biopolymer scaffolds

Objective. 3D cell cultures are becoming a fundamental resource forin-vitrostudies, as they mimic more closelyin-vivobehavior. The analysis of these constructs, however, generally rely on destructive techniques, that prevent the monitoring over time of the same construct, thus increasing the results variability and the resources needed for each experiment.Approach. In this work, we focus on mineralization, a crucial process during maturation of artificial bone models, and propose electrical impedance tomography (EIT) as an alternative non-destructive approach. In particular, we discuss the development of an integrated hardware/software system capable of acquiring experimental data from 3D scaffolds and reconstructing the corresponding conductivity maps. We also show how the same software can test how the measurement is affected by biological features such as scaffold shrinking during the culture.Main results. An initial validation, comprising the acquisition of both a non-conductive phantom and alginate/gelatin scaffolds with known calcium content will be presented, together with thein-silicostudy of a cell-induced mineralization process. This analysis will allow for an initial verification of the systems functionality while limiting the effects of biological variability due to cell number and activity.Significance. Our results show the potential of EIT for the non-destructive quantification of matrix mineralization in 3D scaffolds, and open to the possible long term monitoring of this fundamental hallmark of osteogenic differentiation in hybrid tissue engineered constructs.

Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions

Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.

Stochastic models for single-cell data: Current challenges and the way forward

Although the quantity and quality of single-cell data have progressed rapidly, making quantitative predictions with single-cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single-cell data: (a) because variability in single-cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single-cell data can lead to violations of the assumption of symmetric errors in least-squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single-cell data.

Development and validation of an in-silico tool for the study of therapeutic agents in 3D cell cultures

Computational models constitute a fundamental asset for cancer research and drug R&D, as they provide controlled environments for testing of hypotheses and are characterized by the total knowledge of the system. These features are particularly useful for 3D cell culture models where a complex interaction among cells and their environments ensues. In this work, we present a programmable simulator capable of reproducing the behavior of cells cultured in 3D scaffolds and their response to pharmacological treatment. This system will be shown to be able to accurately describe the temporal evolution of the density of a population of MDA-MB-231 cells following their treatment with different concentrations of doxorubicin, together with a newly described drug-resistance mechanism and potential re-sensitization strategy. An extensive technical description of this model will be coupled to its experimental validation and to an analysis aimed at identifying which variables and behaviors account for differences in the response to treatment. Comprehensively, this work contributes to the growing field of integrated in-silico/in-vitro analysis of biological processes which has great potential for both the increase of our scientific knowledge and the development of novel, more effective treatments.

An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment

3D cell cultures are in-vitro models representing a significant improvement with respect to traditional monolayers. Their diffusion and applicability, however, are hampered by the complexity of 3D systems, that add new physical variables for experimental analyses. In order to account for these additional features and improve the study of 3D cultures, we here present SALSA (ScAffoLd SimulAtor), a general purpose computational tool that can simulate the behavior of a population of cells cultured in a 3D scaffold. This software allows for the complete customization of both the polymeric template structure and the cell population behavior and characteristics. In the following the technical description of SALSA will be presented, together with its validation and an example of how it could be used to optimize the experimental analysis of two breast cancer cell lines cultured in collagen scaffolds. This work contributes to the growing field of integrated in-silico/in-vitro analysis of biological systems, which have great potential for the study of complex cell population behaviours and could lead to improve and facilitate the effectiveness and diffusion of 3D cell culture models.