Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.
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
Several Unidentified Aerial Phenomena (UAP) encountered by military, commercial, and civilian aircraft have been reported to be structured craft that exhibit ‘impossible’ flight characteristics. We consider a handful of well-documented encounters, including the 2004 encounters with the Nimitz Carrier Group off the coast of California, and estimate lower bounds on the accelerations exhibited by the craft during the observed maneuvers. Estimated accelerations range from almost 100 g to 1000s of gs with no observed air disturbance, no sonic booms, and no evidence of excessive heat commensurate with even the minimal estimated energies. In accordance with observations, the estimated parameters describing the behavior of these craft are both anomalous and surprising. The extreme estimated flight characteristics reveal that these observations are either fabricated or seriously in error, or that these craft exhibit technology far more advanced than any known craft on Earth. In many cases, the number and quality of witnesses, the variety of roles they played in the encounters, and the equipment used to track and record the craft favor the latter hypothesis that these are indeed technologically advanced craft. The observed flight characteristics of these craft are consistent with the flight characteristics required for interstellar travel, i.e., if these observed accelerations were sustainable in space, then these craft could easily reach relativistic speeds within a matter of minutes to hours and cover interstellar distances in a matter of days to weeks, proper time.
Count data is prevalent in many different areas of linguistics, such as when counting words, syntactic constructions, discourse particles, case markers, or speech errors. The Poisson distribution is the canonical distribution for characterising count data with no or unknown upper bound. Given the prevalence of count data in linguistics, Poisson regression has wide utility no matter what subfield of linguistics is considered. However, in contrast to logistic regression, Poisson regression is surprisingly little known. Here, we make a case for why linguists need to consider Poisson regression, and give recommendations for when Poisson regression is more appropriate compared to logistic regression. This tutorial introduces readers to foundational concepts needed to understand the basics of Poisson regression, followed by a hands-on tutorial using the R package brms. We discuss a dataset where Catalan and Korean speakers change the frequency of their co-speech gestures as a function of politeness contexts. This dataset also involves exposure variables (the incorporation of time to deal with unequal intervals) and overdispersion (excess variance). Altogether, we hope that more linguists will consider Poisson regression for the analysis of count data.
COVID-19 vaccines are effective and induce a strong immune response. Cure-Hub's own data shows high neutralizing antibody levels after vaccination, especially after the second mRNA dose. In fact, two doses of the Pfizer and Moderna vaccines can push antibody production higher than natural infection (https://www.cure-hub.com/vaccinesvsinfection). However, this does not mean vaccine induced immunity is superior to natural immunity. Below we provide evidence that recovery from natural infection generates a more diverse immune response.
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.
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.
The quantification of invasion and migration is an important aspect of cancer research, used both in the study of the molecular processes involved in this collection of diseases and the evaluation of the efficacy of new potential treatments. The transwell assay, while being one of the most widely used techniques for the evaluation of these characteristics, shows a high dependence on the operator’s ability to correctly identify the cells and a low protocol standardization. Here we present I-AbACUS, a software tool specifically designed to aid the analysis of transwell assays that automatically and specifically recognizes cells in images of stained membranes and provides the user with a suggested cell count. A complete description of this instrument, together with its validation against the standard analysis technique for this assay is presented. Furthermore, we show that I-AbACUS is versatile and able to elaborate images containing cells with different morphologies and that the obtained results are less dependent on the operator and their experience. We anticipate that this instrument, freely available (Gnu Public Licence GPL v2) at www.marilisacortesi.comas a standalone application, could significantly improve the quantification of invasion and migration of cancer cells.
Cell invasiveness quantification is of paramount importance in cancer research and is often evaluated in vitro through scratch wound healing assays that determine the rate at which a population of cells fills a gap created in a confluent 2D culture. The quantification of the results of this experiment, however, lacks standardization and is often highly time consuming and user dependent. To overcome these limitations, we have developed AIM (Automatic Invasiveness Measure), a freely-available software tool for the automatic quantification of the cell-free region in scratch wound healing assays. This study will completely describe AIM and will show its equivalence to three analysis methods commonly used for the quantification of the scratch area and the measure of true wound extension. Furthermore, the analysis time and the dependency of the results of these techniques on the structure of the time course (total duration and number of points) will be studied. To the best of our knowledge, AIM is the first entirely-automated analysis method for scratch wound healing assays and represents a significant improvement of this technique both in terms of results’ quality and reliability.
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.