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Computational models are becoming an increasingly valuable tool in biomedical research. They enable the quantification of variables difficult to measure experimentally, an increase in the spatio-temporal resolution of the experiments and the testing of hypotheses.Parameter estimation fromin-vitrodata, remains a challenge, due to the limited availability of experimental datasets acquired in directly comparable conditions. While the use of computational models to supplement laboratory results contributes to this issue, a more extensive analysis of the effect of incomplete or inaccurate data on the parameter optimization process and its results is warranted. To this end, we compared the results obtained from the samein-silicomodel of ovarian cancer cell growth and metastasis, calibrated with datasets acquired from two different experimental settings: a traditional 2D monolayer, and 3D cell culture models.The differential behaviour of these models will inform the role and importance of experimental data in the calibration of computational models’ calibration. This work will also provide a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational modelsAuthor summaryParameter identification is a key step in the development of a computational model, that is used to establish a connection between the simulated and experimental results and verify the accuracy of thein-silicoframework.The selection of thein-vitrodata to be used in this phase is fundamental, but little attention has been paid to the role of the experimental model in this process. To bridge this gap we present a comparative analysis of the same computational model calibrated using experimental data acquired from cells cultured (i) in 2D monolayers, (ii) in 3D culture models and (iii) a combination of the two.Data acquired in different experimental settings induce changes in the optimal parameter sets and the corresponding computational model’s behaviour. This translates in a varying degree of accuracy during the validation procedure, when the simulated data are compared to experimental measurements not used during the calibration step.Overall, our work provides a workflow and a set of guidelines to select the most appropriate experimental setting for the calibration and validation of computational models.

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.

Objective. 3D cell cultures are becoming a fundamental resource for in-vitro studies, as they mimic more closely in-vivo behavior. 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 the in-silico study 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.

Conventional 2D cell culture, a traditional tool in pre-clinical studies, can hardly be regarded as a representation of a natural cell microenvironment. In this respect, it might result in altered cellular behaviors. To overcome such a limitation, different approaches have been tested to conduct more representative in vitro studies. In particular, the use of 3D cell culture introduces variables, such as cell-cell and cell-extracellular matrix interactions; cell features such as survival, proliferation and migration are consequently influenced. For an example, an enhanced drug resistance and increased invasiveness are shown by cancer cells when cultured in 3D versus 2D conventional culture models. In this setting however, non-uniform cell distribution and biological behaviors appear throughout the scaffold, due to reduced diffusion of oxygen and nutrients. Perfusion in bioreactor systems can be used to improve medium transport. In this line of reasoning, this study proposes a breast cancer cell culture model sustained by an integrated approach that couples a 3D environment and a fluid perfusion. This model improves viability and uniformness of cell distribution, while inducing morphological, functional and molecular cancer cell remodeling.

Purpose The aim of this study was to evaluate the effects exerted over chondrogenic commitment of human adipose-derived mesenchymal stem cells (ADSCs) by a very low oxygen tension ( Materials/methods Cell morphology, mRNA levels of chondrocyte-specific marker genes and the involvement of p38 MAPK signalling were monitored in human ADSCs under a very low oxygen tension. Results Cell morphology was significantly changed after two days of hypoxic preconditioning when they featured as elongated spindle-shaped cells. SRY-box containing gene 9, aggrecan and collagen type II mRNA levels were enhanced under severe hypoxic culture conditions. Moreover, the inhibition of p38 MAPK resulted in a substantial reduction in transcription of the above-mentioned specific genes, proving the pivotal role of this pathway in the transcriptional regulation of chondrogenesis. Conclusions Here, we propose a protocol showing the early commitment of stem cells towards the chondrogenic phenotype in only 2 days of culture via a very low hypoxic environment, in the absence of growth factors added in the culture medium.

A series of hybrid compounds was designed to target histone deacetylases and ds-/G-quadruplex DNAs by merging structural features deriving from Scriptaid and compound 1. Compound 6 binds different DNA arrangements, inhibits HDACs both in vitro and in cells, and is able to induce a reduction of cell proliferation. Moreover, compound 6 displays cell phenotype-reprogramming properties since it prevents the epithelial to mesenchymal transition in cancer cells, inducing a less aggressive and migratory phenotype, which is one of the goals of present innovative strategies in cancer therapies.

Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.

In this study, we explore the behaviour of intracellular magnesium during bone phenotype modulation in a 3D cell model built to mimic osteogenesis. In addition, we measured the amount of magnesium in the mineral depositions generated during osteogenic induction. A two-fold increase of intracellular magnesium content was found, both at three and seven days from the induction of differentiation. By X-ray microscopy, we characterized the morphology and chemical composition of the mineral depositions secreted by 3D cultured differentiated cells finding a marked co-localization of Mg with P at seven days of differentiation. This is the first experimental evidence on the presence of Mg in the mineral depositions generated during biomineralization, suggesting that Mg incorporation occurs during the bone forming process. In conclusion, this study on the one hand attests to an evident involvement of Mg in the process of cell differentiation, and, on the other hand, indicates that its multifaceted role needs further investigation.

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.

The quantification of intercellular variability and biological noise is a fundamental aspect of the characterization of synthetic gene circuits [1]. Since the most common techniques used for this evaluation (flow cytometers and complex microscopy set-ups) are expensive and generally unaffordable by small laboratories, we have developed MUSIQ (Microscope flUorescence SIngle cell Quantification), a protocol that combines a standard fluorescence microscope set-up with a custom made software and allows the quantitative evaluation of the fluorescent signal emitted by single E. coli cells [2]. MUSIQ was entirely validated, through a comparative analysis with standard cytofluorimetric assays. These results, here fully reported, determined the equivalence of the two methods on a wide range of fluorescence intensities, both in terms of average signal and phenotypic variability. While our approach was determined to have a slightly lower sensitivity, with respect to the flow cytometer, it allows for a more flexible analysis that can include the study of cellular morphology and culture spatial patterns. Furthermore it is accessible to a large base of researchers, relying on general purpose instrumentation and freeware software [3]. These characteristics determine MUSIQ to be an affordable alternative for the evaluation of intercellular variability in bacterial cultures and thus a significant advancement in synthetic biology, that will improve the accuracy of gene expression quantification and the functional characterization of synthetic gene circuits. [1] Bandiera et al. Front Microbiol. 2016. doi:10.3389/fmicb.2016.00479. [2] Cortesi et al. Journal of Biological Engineering (2017) 11:8 [3] http://www.mcbeng.it/en/downloads/software/musiq.html

Blood potassium concentration ([K+]) influences the electrocardiogram (ECG), particularly T-wave morphology. We developed a new method to quantify [K+] from T-wave analysis and tested its clinical applicability on data from dialysis patients, in whom [K+] varies significantly during the therapy. To elucidate the mechanism linking [K+] and T-wave, we also analysed data from long QT syndrome type 2 (LQT2) patients, testing the hypothesis that our method would have underestimated [K+] in these patients. Moreover, a computational model was used to explore the physiological processes underlying our estimator at the cellular level. We analysed 12-lead ECGs from 45 haemodialysis and 12 LQT2 patients. T-wave amplitude and downslope were calculated from the first two eigenleads. The T-wave slope-to-amplitude ratio (TS/A) was used as starting point for an ECG-based [K+] estimate (KECG). Leave-one-out cross-validation was performed. Agreement between KECG and reference [K+] from blood samples was promising (error: −0.09 ± 0.59 mM, absolute error: 0.46 ± 0.39 mM). The analysis on LQT2 patients, also supported by the outcome of computational analysis, reinforces our interpretation that, at the cellular level, delayed-rectifier potassium current is a main contributor of KECG correlation to blood [K+]. Following a comprehensive validation, this method could be effectively applied to monitor patients at risk for hyper/hypokalemia.

Background Quantifying gene expression at single cell level is fundamental for the complete characterization of synthetic gene circuits, due to the significant impact of noise and inter-cellular variability on the system’s functionality. Commercial set-ups that allow the acquisition of fluorescent signal at single cell level (flow cytometers or quantitative microscopes) are expensive apparatuses that are hardly affordable by small laboratories. Methods A protocol that makes a standard optical microscope able to acquire quantitative, single cell, fluorescent data from a bacterial population transformed with synthetic gene circuitry is presented. Single cell fluorescence values, acquired with a microscope set-up and processed with custom-made software, are compared with results that were obtained with a flow cytometer in a bacterial population transformed with the same gene circuitry. Results The high correlation between data from the two experimental set-ups, with a correlation coefficient computed over the tested dynamic range > 0.99, proves that a standard optical microscope– when coupled with appropriate software for image processing– might be used for quantitative single-cell fluorescence measurements. The calibration of the set-up, together with its validation, is described. Conclusions The experimental protocol described in this paper makes quantitative measurement of single cell fluorescence accessible to laboratories equipped with standard optical microscope set-ups. Our method allows for an affordable measurement/quantification of intercellular variability, whose better understanding of this phenomenon will improve our comprehension of cellular behaviors and the design of synthetic gene circuits. All the required software is freely available to the synthetic biology community (MUSIQ Microscope flUorescence SIngle cell Quantification). Electronic supplementary material The online version of this article (doi:10.1186/s13036-017-0050-y) contains supplementary material, which is available to authorized users.