Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

Cancer is an acquired genetic disease of clonal origin. Carcinogenesis and its subsequent development follow the principles of evolution, starting with a single cell with stem cell properties and a proliferative advantage, leading to clonal expansion, clonal evolution and subsequent demise by killing the host: "Evolution gone awry". Evolution can be divided into three essential steps: 1. "Chance" or random movement of molecules allows structures to interact. 2. Molecular affinity, "Necessity" (J. Monod, Chance and Necessity: Essay on the Natural Philosophy of Modern Biology, 1970), leads to a new structure providing novel properties and function. The 3rd step of the evolutionary processes, for which I would propose the term "Synclipse", occurs, if the new constellation provides a biochemical and biological survival advantage, "survival of the fittest", in a given environmental context.

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