The difficulty of predicting the emergent development, homeostasis and disfunction of tissues from cells’ molecular signatures limits our ability to integrate molecular and genetic information to make meaningful predictions at the organ or organism level. Virtual Tissues are an approach to constructing quantitative, predictive Agent-Based mechanistic models starting from cell behaviors and combining subcellular molecular kinetics models, the physical and mechanical behaviors of cells and the longer-range effects of the extracellular environment. For the past 15 years, we have been developing the open-source Virtual-Tissue model specification and execution framework CompuCell3D (CC3D) (www.compucell3d.org) which aims to make Virtual-Tissue modeling more accessible to biologists and bioengineers. I will talk about some recent applications of CC3D(to model in host infection and immune response and corneal damage and recovery and some extensions of CC3D to function better with the increasingly popular Jupyter Notebook environment. CC3D uses the lattice-based Cellular-Potts Model (CPM) formalism to represent cells, which allows for description of cell shape but also has certain limitations. Recently, we have developed a scriptable Python-native (Jupyter-Notebook) off-lattice Center and Vertex model Virtual-Tissue modeling environment, Tissue Forge (https://tissue-forge-documentation.readthedocs.io/en/latest/) which allows for real-time interactive simulations with hundreds of thousands of cells. I will introduce Tissue Forge with the example of modeling zebrafish epiboly. Finally, solving the diffusion equation is one of the most computationally costly components of many Virtual Tissue models. I will discuss our preliminary efforts to develop U-net based Machine-Learning (ML) surrogates for diffusion solvers and invite discussion of how and where to integrate ML and mechanistic modeling.