Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.

NetPyNE is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of multiscale biological neuronal networks using the NEURON simulator.  Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.

NetPyNE enables users to consolidate complex experimental data from different scales into a unified computational model. Users are then able to simulate and analyze this model to better understand brain structure, dynamics, and function in a unique framework that combines: 1) programmatic and/or GUI-driven model building using flexible, rule-based, high-level standardized specifications; 2) separation of model parameters from underlying technical implementations, preventing coding errors and making models easier to read, modify, share and reuse; 3) support for multiple scales from molecule to cell to network; 4) support for complex subcellular mechanisms, dendritic connectivity and stimulation patterns; 5) efficient parallel simulation both on stand-alone computers and supercomputers; 6) automated data analysis and visualization (e.g. connectivity, neural activity, information theoretic analyses); 7) importing and exporting to/from multiple standardized formats; 8) automated parameter tuning (molecule to network level) using grid search and evolutionary algorithms.