Amogh P. Jalihal

TL;DR: A systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data finds heterogeneous performance and suggests recommendations to users.

TL;DR: It is suggested that new ideas for avoiding the prediction of indirect interactions appear to be necessary to improve the accuracy of GRN inference algorithms for single cell gene expression data.

TL;DR: A computational model of the integrated nutrient signaling network in budding yeast is built to reconcile literature-curated quantitative experimental data with the proposed molecular mechanism, and is used to predict nutrient-responsive transcription factor activities in a number of mutant strains undergoing complex nutrient shifts.

TL;DR: In this paper, a computational model of the underlying regulatory mechanisms is proposed to study nutrient signaling, and the model's predictions are consistent with literature-curated experimental measurements. But the model does not consider the effect of environmental factors on nutrient signaling.

TL;DR: In this article, the authors divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models, which both contribute to plant biology research at different scales to answer different research questions.