We have been working in the fields of genomics, genome-wide association studies, mass spectrometry proteomics, and metabonomics technologies to identify candidate disease biomarkers for over ten years. We are currently developing a software package, called Mapping in Genomics Data Integration, which can maps relationships between various types of genomic data, and then identifies signal pathways and pathway signatures to define diseases with complex phenotypes. In addition, this software can serve as a novel bioinformatic tool to integrate high-throughput genome-wide profiling data using single-nucleotide polymorphism (SNP) array, epigenomics, gene array, microRNA array, and publically available KEGG and protein-protein interaction (PPI) databases. A NIH R01 grant was awarded for this project in 2009.

We are also developing tools for next generation sequencing data (Chip_seq, RNA_seq, and microRNA_seq) analysis in collaboration with our pathologists and biologists at TMHRI and Baylor Genome Center. One tool in development will create a modeling signaling pathway map based on Reverse Phase Protein Array (RPPA). This technology is the key to understanding signal transduction, drug combination treatment, and drug resistance in research studies.

Systems Modeling

Predictive modeling of Pharmacokinetics (PK) and Pharmacodyanmics (PD)

In collaboration with investigators in the TMH Radiology Department and the TMHRI Nanomedicine Program we are creating mathematical and biophysical systems to model:

P38 Downstream Pathway Discovery

We hypothesize that imbalance of p38 isoforms may lead to the development of MDS by disturbing the apoptosis, proliferation, and differentiation of hematopoiesis. We are studying the roles of various isoforms in MDS using systems biology approaches that identify core pathways and quantitatively characterize the behavior of feedback loops. These tools predict phenotypic changes and guide the subsequent experimental design. Understanding the roles of these isoforms in controlling hematopoiesis will allow design of more specific inhibitors to target particular isoforms.

Drug Resistance Study using Drug Combination Treatment Strategies

We are applying quantitative mapping of complex signal transduction to understand signaling processes that regulate biological responses to exogenous stimuli. We are investigating the synergies between drug combinations in silico by considering how drug combinations block feedback loops, and affect cancer-related signaling pathways. We propose a new computational approach for drug combination prioritization, in which we first infer the signaling pathway map of drug combinations. We then apply the systems of pathway modeling methods, which can translate the activation/inhibition/phosphorylation rules into quantitative analysis, and simulate the effects of combinatorial drug treatment. In combination with bioimaging validation, we can develop better combinatorial therapeutic strategies and translate such discoveries from the bench to the bedside.

Microenvironment Modeling

We are developing a mathematical differential equation model for modeling and simulating tumor initiating cells or Cancer Stem Cells (CSC), progenitor cells, mature tumor cells, and niches at multi-scale levels. In particular, we are focusing on modeling the cell-cell interactions between CSCs and stromal cells, as well as the CSC lineage model. This research is supported by a U54 center grant award from the NIH NCI ICBP program. Meanwhile, we are applying similar multi-scale modeling strategies to simulate and guide bone regeneration.

Biomedical image analysis

Dr. Zhou and his colleagues pioneered the high-content imaging informatics (see his over 100 journal and conference publications on this topic). We made substantial and original contributions on four softwares, namely DCellIQ, GCellIQ, NeuronIQ, and NeuriteIQ which have been developed and released. They were funded by four NIH R01s. Our lab is currently working on 3D in-vivo imaging informatics tool development, and medical image informatics, particularly in dental surgical planning and face reconstruction.

By collaborating with biologists, pathologists, radiologists, mathematicians, engineers, and clinical doctors, the utmost goal of our scientific research is to be a leader in the development and applications of novel systems bioinformatic methods and molecular diagnosis as well as the integration of them into clinics and disease monitoring.