We use systems biology approaches to uncover the underlying principles governing the operation of genetic networks.
Specifically, we integrate computational modeling and data analysis to elucidate the relationship among robustness of network dynamics, stochasticity in gene expression and heterogeneity in cancer evolution.
Our studies will contribute to a systems-level understanding of cancer and will eventually lead to the design of personalized therapies for cancer patients.
|Modeling Methods||Network Models||Lu Laboratory Gallery|
I'm interested in a computational systems genetics approach to mediation analysis of complex traits in mice.
Statistics, Biostatistics, Computational Biology, Statistical Methods Development, Software Development (R packages)
We are passionate about the development and application of computational modeling methods to study the operating mechanisms of cancer...
To study the evolution of genes related to fertility and sexual reproduction, and to use this information to understand infertility in...
Five-year, $2.1 million federal grant to Olga Anczuków of JAX will reveal the role of splicing-factor defects in cardiac or...
New five-year, $2.2M outstanding investigator grant to Mingyang Lu will support systems biology algorithm approach.
ChIA-PIPE expands the capabilities of previous analysis pipelines that identified chromatin loops, the DNA sequences of...
Develops and applies computational methods and web apps to construct, model, visualize, and simulate gene regulatory networks and analyze...
JAX researchers have developed a new computational algorithm that is capable of modeling the effects of both stochastic gene expression and...
Investigates the key genomic change in cancers and reproduce and demonstrate the effect in vitro/vivo
Employs genomic and computational approaches to investigate how the immune response shapes brain tumor development.
Bioinformatics, Systems Biology, and Genomics in cancers and coronary artery diseases.
Multi-omic profiling of pediatric brain and bone cancer to discover novel prognostic biomarkers and therapeutic targets.
I am interested in applying machine learning and advanced statistical modelling into biological questions.
Studying 3D Genome Organization and Function in different human and mouse cell lines.
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