Biology subjects

Takahashi, T.

Publications and source records attributed to Takahashi, T..

3 recordsLinked to original sources

bioRxiv2018

Positive-feedback defines the timing and robustness of angiogenesis

Blood vessel formation by angiogenesis is critical for tissue development, homeostasis and repair, and is frequently dysregulated in disease[1-3]. Angiogenesis is triggered by vascular endothelial growth factor receptor-2/3 (VEGFR) signalling, which induces motile endothelial cell (ECs) tip identity[4,5]. Tip cells lead new branching vessels, but also coordinate collective EC movement by repressing tip identity in adjacent ECs via Delta-Like 4 (DLL4)-Notch-mediated down-regulation of VEGFR activity[6-13]. Hence, angiogenesis is driven by lateral inhibition-mediated competition of ECs for migratory status. Recent work reveals that temporal modulation of this DLL4-Notch-mediated lateral inhibition circuit fundamentally shapes both normal and pathological angiogenesis[14-17]. However, the core regulatory network defining the timing and dynamics of EC decision-making is unclear. Here, by integrating computational modeling with in-vivo experimentation, we uncover a unique ultrasensitive switch that temporally defines EC lateral inhibition and ultimately determines the timing, magnitude and robustness of angiogenic responses. We reveal that positive-feedback to Vegfr via the atypical tetraspanin, tm4sf18, amplifies Vegfr activity and expedites EC decision-making in-vivo. Moreover, this Tm4sf18-mediated positive-feedback confers robustness to angiogenesis against changeable environmental conditions by invoking bistable-like behavior. Consequently, mutation of tm4sf18 in zebrafish delays motile EC selection, generates hypoplastic vessels, sensitizes ECs to fluctuations in pro-angiogenic signal and disrupts angiogenesis. We propose that positive-feedback transforms the normally protracted process of lateral inhibition into a quick, adaptive and robust decision-making mechanism, suggestive of a general framework for temporal modulation of cell fate specification in development and disease.

developmental biology↗

bioRxiv2018

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R ([≥] 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).

bioinformatics↗

bioRxiv2018

Correlation Detection with and without the Theory of Conditionals: A model update of Hattori & Oaksford (2007)

We view observational causal induction as a statistical independence test under rarity assumption. This paper complements the two-stage theory of causal induction proposed by Hattori and Oaksford (2007) with a computational analysis. We show that their dual-factor heuristic (DFH) model has a rational account as the square root of the index of (non-)independence under extreme rarity assumption, contrary to the criticism that the DFH model is non-normative (e.g., Lu et al., 2008). We introduce a model that considers the proportion of assumed-to-be rare instances (pARIs), which is the probability of biconditionals (according to several theories of compound conditionals) and can be seen as a simplified version of the DFH model. While being a single conditional probability, pARIs approximates the non-independence measure, the square of DFH. In reproducing the meta-analysis in Hattori and Oaksford (2007), we confirm that pARIs and DFH have the same level of descriptive adequacy, and that the two models have the highest fit among more than 40 models. Then, we critically examine the computer simulations which were central to the rational analysis in Hattori and Oaksford (2007). We point out two problems in their simluations: samples in some of the simulations being restricted to generative ones, and in-definite values of models because of the small samples. In the light of especially the latter problem of definability, pARIs shows higher applicability.

animal behavior and cognition↗