Inference on autoregulation in gene expression

Wang, Yue; He, Siqi

doi:10.48550/arxiv.2201.03164

Cited by 4 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both technical and biological noise, however, might result in significant deviations between cells of same state. The stochastic and dynamical modeling of gene regulation and splicing, compared with deterministic and steady-state approaches in spliceJAC, could in the future improve the goodness of fitting toward low-expressed genes and strengthen the inference of gene auto-regulations (preprint: Wang & He, 2022). Finally, like other models of gene regulation based on scRNA-seq data, spliceJAC assumes that mRNA counts provide a consistent approximation for protein copy numbers, which might not necessarily be true due to post-translations regulations.…”

Section: Discussionmentioning

confidence: 99%

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Bocci

Zhou

Nie

2022

Molecular Systems Biology

View full text Add to dashboard Cite

Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

show abstract

Section: Discussionmentioning

confidence: 99%

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Bocci

Zhou

Nie

2022

Molecular Systems Biology

View full text Add to dashboard Cite

show abstract

“…Certain types of data can be used to infer the existence of gene autoregulation, such as time series data [48,71,51] or interventional data for single-cell gene expression [27]. There also exists an inference method that only requires one-time non-interventional single-cell gene expression data [57]. Although different inference methods are based on different types of models, and require different data types, the basic idea is the same: build two models, one with autoregulation, and one without autoregulation; then try to find different behaviors between these two models.…”

Section: Inference Methods For Autoregulationmentioning

confidence: 99%

“…Autoregulation means for some fixed nī, h i (n) is (locally) increasing/decreasing with n i , thus f i (n) increases/decreases and/or g i (n) decreases/increases with n i . In this model, we have the following result [57]:…”

Section: Inference Methods For Autoregulationmentioning

confidence: 99%

Three facets of mathematical cancer biology research

Wang¹

2023

Preprint

View full text Add to dashboard Cite

Cancer, as the uncontrollable cell growth, is related to many branches of biology. In this review, we will discuss three mathematical approaches for studying cancer biology: population dynamics, gene regulation, and developmental biology. If we understand all biochemical mechanisms of cancer cells, we can directly calculate how the cancer cell population behaves. Inversely, just from the cell count data, we can use population dynamics to infer the mechanisms. Cancer cells emerge from certain genetic mutations, which affect the expression of other genes through gene regulation. Therefore, knowledge of gene regulation can help with cancer prevention and treatment. Developmental biology studies acquisition and maintenance of normal cellular function, which is inspiring to cancer biology in the opposite direction. Besides, cancer cells implanted into an embryo can differentiate into normal tissues, which provides a possible approach of curing cancer. This review illustrates the role of mathematics in these three fields: what mathematical models are used, what data analysis tools are applied, and what mathematical theorems need to be proved. We hope that applied mathematicians and even pure mathematicians can find meaningful mathematical problems related to cancer biology.

show abstract

“…When studying the inference for mutual regulation, I also found an idea to infer the existence of autoregulation. In a Markov chain model of gene expression, if there is no autoregulation, then for the expression level, the variance should be larger than the mean [24]. Thus if the mean is larger than the variance, there might be autoregulation.…”

Section: Gene Regulationmentioning

confidence: 99%

Finding meaningful and workable applied mathematics problems in science

Wang¹

2023

Preprint

View full text Add to dashboard Cite

In this short review, I will summarize my research experience in three fields in applied mathematics: mathematical biology, applied probability, and applied discrete mathematics. Specifically, I will show how each project was initiated, and what wrong approaches were applied. Such details are important in learning how to do research, but they cannot be read out from research papers. I wish that students and junior researchers in applied mathematics could learn a lesson from this summary.

show abstract

Inference on autoregulation in gene expression

Cited by 4 publications

References 27 publications

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Three facets of mathematical cancer biology research

Finding meaningful and workable applied mathematics problems in science

Contact Info

Product

Resources

About