Parameter-less approaches for interpreting dynamic cellular response

Parameter-less approaches for interpreting dynamic cellular response

Cellular response such as cell signaling is an integral part of information processing in biology. Upon receptor stimulation, numerous intracellular molecules are invoked to trigger the transcription of genes for specific biological purposes, such as growth, differentiation, apoptosis or immune response. How complex are such specialized and sophisticated machinery? Computational modeling is an ...

Analysis of Response Robustness for a Multi-Objective Mathematical Model of Dynamic Cellular Manufacturing

The multi-objective optimization problem is the main purpose of generating an optimal set of targets known as Pareto optimal frontier to be provided the ultimate decision-makers. The final selection of point of Pareto frontier is usually made only based on the goals presented in the mathematical model to implement the considered system by the decision-makers. In this paper, a mathematical model...

Computational approaches for interpreting scRNA‐seq data

The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and...

Parameter-Less Hierarchical BOA

The parameter-less hierarchical Bayesian optimization algorithm (hBOA) enables the use of hBOA without the need for tuning parameters for solving each problem instance. There are three crucial parameters in hBOA: (1) the selection pressure, (2) the window size for restricted tournaments, and (3) the population size. Although both the selection pressure and the window size influence hBOA perform...

The Parameter - less Geneti Algorithm