Sml regression xlstat5/7/2023 ![]() The cue-signal relationship has frequently been analyzed using mechanistic models, such as mass-action kinetics ( 7). Typically, systems biology studies are concerned with analyzing the “cue-signal-response” relationship, in which cells take in information from their environment (for example, growth factors, extracellular matrix signals, or mechanical stimuli) and this information is propagated through the cellular signaling network to ultimately make cell phenotype decisions (for example, proliferation, death, or differentiation). ![]() Methods to Analyze Signal-Response Relationships Here, this Teaching Resource first briefly describes univariate approaches to analyze the signal-response relationship-that is, how the amount of one signal is used to predict the cellular response ( 9, 10)-and then provides a detailed description of multivariate approaches, such as PLSR, to determine how multiple signaling cascades are integrated in the cellular decision-making process. As more pathways or time scales are added, the problem of accurately describing the network topology and estimating parameters becomes even more challenging, making mass-action kinetics impractical. For example, fitting of a mass-action kinetic model of the signaling mediated by the epidermal growth factor receptor (EGFR) family, which incorporated several receptor and ligand forms, receptor trafficking, and activation of the kinases ERK and AKT, resulted in approximately 100 different parameter sets that could be fit equally well to the training data set ( 8). In contrast, the signal-response relationship is strongly affected by interactions among multiple pathways and occurs on a longer time scale, making it generally too complex for these detailed mechanistic model forms (Slide 2). Lecture Notes Cue-Signal-Response Relationships In particular, PLSR has been used in the systems biology community to analyze relationships between intracellular signals and cellular responses ( 4– 6). PLSR is a multivariate regression technique developed to analyze relationships between independent and dependent variables in large data sets and can, therefore, be applied to analyze proteomic, transcriptomic, metabolomic, and other cellular data. The topic of PLSR followed earlier lectures on the quantitative experimental methods frequently used to gather these data sets ( 1, 2) and principal component analysis (PCA) ( 3). ![]() This lecture on partial least squares regression (PLSR) was part of an introductory systems biology course focused on implementation and analysis of systems biology models, which included overviews of several experimental techniques and computational methods. This Teaching Resource is intended for instructors who have familiarity with linear algebra familiarity with MATLAB will be helpful for the problem assignment. ![]()
0 Comments
Leave a Reply. |