A5063574984- Gene Regulatory Network Analysis
- Single-cell and spatial transcriptomics
- Organic Electronics and Photovoltaics
- Microbial Metabolic Engineering and Bioproduction
- DNA and Nucleic Acid Chemistry
- Bioinformatics and Genomic Networks
- Bacteriophages and microbial interactions
- Perovskite Materials and Applications
- Advanced biosensing and bioanalysis techniques
- Pluripotent Stem Cells Research
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- Conducting polymers and applications
University of California, Irvine
2016-2020
Rochester Institute of Technology
2013
Stochastic simulation has been a powerful tool for studying the dynamics of gene regulatory networks, particularly in terms understanding how cell-phenotype stability and fate-transitions are impacted by noisy expression. However, networks often have characterized multiple attractors. is inefficient such systems, because most time spent waiting rare, barrier-crossing events to occur. We present rare-event simulation-based method computing epigenetic landscapes phenotype-transitions...
A newly developed coarse-grained model called BioModi is utilized to elucidate the effects of temperature and concentration on DNA hybridization in self-assembly. Large-scale simulations demonstrate that complementary strands either tetrablock sequence or randomized with equivalent number cytosine guanine nucleotides can form completely hybridized double helices. Even though end states are same for two sequences, there exist multiple kinetic pathways populated a wider range transient...
Single-cell transcriptomics is advancing discovery of the molecular determinants cell identity, while spurring development novel data analysis methods. Stochastic mathematical models gene regulatory networks help unravel dynamic, mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic network with single challenging. Here, we present a method for analyzing gene-pair...
ABSTRACT Single-cell transcriptomics is advancing discovery of the molecular determinants cell identity, while spurring development novel data analysis methods. Stochastic mathematical models gene regulatory networks help unravel dynamic, mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic network with single challenging. Here, we present a method for analyzing...