ArticlePloS one2013
The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Frequency-Dependent Premature Differentiation of Pheochromocytoma Cells Exhibits Band-Pass Filter Behavior Correlation with Intracellular Enzyme Activation Kinetics.International journal of molecular sciences · 2025Article
- A Frequency Domain Analysis of the Growth Factor-Driven Extra-Cellular-Regulated Kinase (ERK) Pathway.Biology · 2025Article
- CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.Briefings in bioinformatics · 2024Article
- CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells.Bioinformatics (Oxford, England) · 2022Article
- System identification of signaling dependent gene expression with different time-scale data.PLoS computational biology · 2017Article
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Authors and funding
7 authors.
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Abstract
Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.
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