This deliverable describes the requirements and scenario definitions of the Life Sciences use case. We introduce the requirements of the multiscale model framework, termed PhysiBoSSa, and justify our choice of selection of this framework. Later, we detail the framework’s components: the agent-based, the environment, the signalling network and the cell cycle components. Additionally, we explain how integrating these in a physics-based cell simulator allows us to study different aspects crucial for the development and growth of tumours and how, given the biophysical, biochemical, and biomechanical factors present, multiscale model can help identify the factors that drive a given treatment to be a success or a failure. The Life Sciences scenario includes the study of two drug resistance-related biological models with different cell signalling networks: the first one is a study of different drug regimes using TNF and the second is a study of drug combinations using the AGS gastric cancer cell line. Using these biological models and our PhysiBoSSa, we replicate experimental data of growth profiles of cancer cells treated with different drug regimes. The ultimate goal of this use case is to provide a “virtual laboratory” for studying cancer growth and evolution by using multiscale models of tumour systems. The development of such a framework facilitates the design, test, and optimisation of cancer treatments based on combinations of different drugs and dose scheduling strategies. These simulations are being scaled up by several orders of magnitude by parallelising the code in a hybrid OpenMP-MPI implementation, aiming to scale up simulations of cancer cell 3D spheroids up to a billion cells using high-performance computing. These scaled-up simulations using the Barcelona Supercomputing Center (BSC) MareNostrum4 will incorporate forecasting techniques for various events of interest, as well as techniques to reduce uninformative simulations. Moreover, we present a model exploration technique that allows us to study the structure and hierarchy of the model’s parameters and to evaluate its sensibility to the parameters’ perturbation. All these developments will facilitate the design of different set-ups that tally cancer tumour growth conditions with increased number of cells, altered microenvironmental physical properties, different cell types, as well as, study the interaction between cancer cells and the immune system. Lastly, we present the Initial Evaluation Report from expert users, including the results of INFORE prototype on the available data streams.