1. Physical transport barriers in cancer
Our aim is to investigate and overcome biobarriers in cancer treatment, using an integrated experimental and computational approach. We believe a mathematical model that accounts for the physics of drug transport can accurately predict the response to chemotherapy in vivo from vasculature and cytotoxicity parameters measured in vitro. Measurement of parameter values are obtained with respect to spatial distribution (i.e., vessels) and over a period of time, with sufficient resolution to quantify cellular behavior (proliferation, apoptosis, necrosis) and diffusion gradients (oxygen, VEGF, drug). Grant support: CTO PSOC - 1U54CA143837, TCCN - 1U54CA151668, USC PSOC - 1U54CA143907, ICBP - 1U54CA149196.
- Application of mathematical model to predict chemotherapy outcomes in patients with breast cancer liver metastases
- Application of mathematical model to predict chemotherapy outcomes in patients with CRC metastases to the liver
- Application of mathematical model to predict dose response in pancreatic cancer cells in vitro, in vivo, and in patients
2. Nanomedicine
To develop a mathematical model from fundamental conservation considerations and fit it to cytotoxicity monolayer data to determine cellular biology parameters for free doxorubicin (DOX) and DOX-loaded protocell delivery to hepatocellular carcinoma (HCC) cells. This work explains the differences in free drug and protocell delivery, and can be used to understand and predict cellular response to drug treatment which has implications to the clinical setting. Grant support: CTO PSOC - 1U54CA143837, TCCN - 1U54CA151668.
- Calibration and validation of a mathematical model of in vitro drug PK-PD
- Modeling of nanoparticle active transport using macrophages
3. Mammary gland development and breast cancer research
To provide a multiscale modeling tool for mammary gland development that can accurately predict the development of ductal tree morphologies from detailed subcellular- and cell-scale models of detailed cell arrangements. This tool will then be used to study breast cancer initiation and progression. Grant support: ICBP - 1U54CA149196.
- A geometrically-constrained multi-compartment population-based model to study the ductal elongation rate and cell lineages for normal mammary gland development
- An agent-based model to study dynamic cell patterning within the differentiation region of the terminal end bud (TEB)
4. Multiscale cancer modeling
To develop a new class of hybrid multiscale models for simulating growing, heterogeneous tissues, including cancer tissue. These models will (1) provide accurate descriptions of the feedback and interactions among processes across different scales and (2) enable the model components at the molecular-, cell-, and tissue-scales to be precisely determined from cell scale modeling, and underlying biological measurements, without “fitting” them directly. Grant support: CTO PSOC - 1U54CA143837, TCCN - 1U54CA151668, USC PSOC - 1U54CA143907, ICBP - 1U54CA149196.
5. Modeling of intracellular cargo-motor competitive transport
To develop a multi-motor, multi-receptor agent-based model with stochastic simulations of cargo-motor interactions producing intracellular transport that predicts emergent transport behavior. The model will be used to investigate the mechanics of transport and identification of new targets for therapeutic interventions of transport defects and for delivery of gene therapy or cell-killing pharmaceuticals. Grant support: CTO PSOC - 1U54CA143837.
