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Mechanisms of chemical reactions can often be predicted by determining the dependence of the lifetime of reaction intermediates on the structure of the reactants. When there is no lifetime in the presence of another reactant or catalyst the reaction proceeds through an enforced concerted mechanism. Noncovalent binding interactions between enzymes and their substrates provide a major contribution to catalysis by decreasing entropy and by destabilizing the ground state relative to the transition state, as well as by covalent and noncovalent chemical interactions with the substrate. Movement in biological systems, such as muscle contraction and the active transport of ions, is generally brought about through a series of alternating chemical and vectorial steps that involve a series of changes in the specificity for catalysis of the chemical and vectorial reactions. These changes divide the overall reaction into segments so that neither the chemical nor the vectorial reaction will be completed unless the other is also completed. [ABSTRACT FROM AUTHOR]

Adipogenesis is essential in in vitro experimentation to assess differentiation capability of stem cells, and therefore, its accurate measurement is important. Quantitative analysis of adipogenic levels, however, is challenging and often susceptible to errors due to non-specific reading or manual estimation by observers. To this end, we developed a novel adipocyte quantification algorithm, named Fast Adipogenesis Tracking System (FATS), based on computer vision libraries. The FATS algorithm is versatile and capable of accurately detecting and quantifying percentage of cells undergoing adipogenic and browning differentiation even under difficult conditions such as the presence of large cell clumps or high cell densities. The algorithm was tested on various cell lines including 3T3-L1 cells, adipose-derived mesenchymal stem cells (ASCs), and induced pluripotent stem cell (iPSC)-derived cells. The FATS algorithm is particularly useful for adipogenic measurement of embryoid bodies derived from pluripotent stem cells and was capable of accurately distinguishing adipogenic cells from false-positive stains. We then demonstrate the effectiveness of the FATS algorithm for screening of nuclear receptor ligands that affect adipogenesis in the high-throughput manner. Together, the FATS offer a universal and automated image-based method to quantify adipocyte differentiation of different cell lines in both standard and high-throughput workflows. [ABSTRACT FROM AUTHOR]