Mariko Okada 교수

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course m

Abstract Mixed lymphocyte cultures (MLC) contain soluble mediator(s) that are able to support primary cytotoxic responses to ultraviolet- (UV) inactivated allogeneic cells. The production of such amplifying factor(s) in vitro was studied by using spleen cells from a variety of BIO congenic mice, with a view to defining the antigenic and cellular requirements for mediator production. K, D, or I region stimulation in MLC all led to the production of amplifying factor(s). The kinetics of production

NF-κB is a transcription factor that activates super enhancers (SEs) and typical enhancers (TEs) and triggers threshold and graded gene expression, respectively. However, the mechanisms by which NF-κB selectively participates in these enhancers remain unclear. Here we show using mouse primary B lymphocytes that SE activity simultaneously associates with chromatin opening and enriched NF-κB binding, resulting in a higher fold change and threshold expression upon B cell receptor (BCR) activation.

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing

Understanding how cells use complex transcriptional programs to alter their fate in response to specific stimuli is an important question in biology. For the MCF-7 human breast cancer cell line, we applied gene expression trajectory models to identify the genes involved in driving cell fate transitions. We modified trajectory models to account for the scenario where cells were exposed to different stimuli, in this case epidermal growth factor and heregulin, to arrive at different cell fates, i.e

The transcription factor NF-κB, which plays an important role in cell fate determination, is involved in the activation of super-enhancers (SEs). However, the biological functions of the NF-κB SEs in gene control are not fully elucidated. We investigated the characteristics of NF-κB-mediated SE activity using fluorescence imaging of RelA, single-cell transcriptome and chromatin accessibility analyses in anti-IgM-stimulated B cells. The formation of cell stimulation-induced nuclear RelA foci was

The NF-κB signaling pathway is crucial for cellular responses to environmental factors. Several studies have tried to decipher the mechanism of cells utilizing this pathway for information transfer and accurately encoding extracellular information that is translated into unique transcriptional programs. This fine-tuned encoding is possible owing to the complex regulatory mechanisms in the NF-κB pathway and is relayed through the nuclear dynamics of the NF-κB transcription factor. The "message" i

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using <i>in silico</i> signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB recepto