A5047479179- Gene Regulatory Network Analysis
- Protein Structure and Dynamics
- Bacterial Genetics and Biotechnology
- Model Reduction and Neural Networks
- Gaussian Processes and Bayesian Inference
- Cell Image Analysis Techniques
- Single-cell and spatial transcriptomics
- Mathematical Biology Tumor Growth
- Cellular Mechanics and Interactions
- RNA Research and Splicing
- Machine Learning in Bioinformatics
- Neurogenesis and neuroplasticity mechanisms
- Neuroinflammation and Neurodegeneration Mechanisms
- 3D Printing in Biomedical Research
- Cell Adhesion Molecules Research
University of California, Santa Barbara
2020-2024
Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set biomarkers for perturbations metabolites interest. Here, we develop data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data discovery analyte-responsive promoters. This provides that act as proxy transcriptional state referred cell state. We construct low-dimensional models gene expression dynamics by their ability capture...
The molecular basis of human brain evolution is a key piece in understanding the human-specific cognitive and behavioral traits. Comparative studies have suggested that was accompanied by accelerated changes gene expression (referred to as “regulatory evolution”), especially those leading an increase products involved energy production metabolism. However, signals regulatory were not always consistent across studies. One confounding factor diversity distinctive cell types brain. Here, we...
In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as function media conditions. We first introduce generic data-driven framework training operator-theoretic models to predict rate. then experimental design and data generated in study, namely curves Pseudomonas putida casein glucose concentrations. use driven approach identification, specifically nonlinear autoregressive (NAR) represent dynamics. show theoretically that Hankel DMD can be...
Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set biomarkers for perturbations metabolites interest. Here, we develop data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data discovery analyte-responsive promoters. This provides that act as proxy transcriptional state referred cell state. We construct low-dimensional models gene expression dynamics by their ability capture...
In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as function media conditions. We first introduce generic data-driven framework training operator-theoretic models to predict rate. then experimental design and data generated in study, namely curves Pseudomonas putida casein glucose concentrations. use driven approach identification, specifically nonlinear autoregressive (NAR) represent dynamics. show theoretically that Hankel DMD can be...