A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype.In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling cooccurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions.Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth.Availability and Implementation: Freely available on the web at http://www.microc.org. Research Resource Identification Initiative ID (https://scicrunch.org/): SCR 016672Contact: Francesca M. Buffa, University of Oxford, francesca.buffa@imm.ox.ac.uk permits to predict the behaviour of individual cells, and the entire population of cells, and it also enables to study of the possible causative mechanisms of such behaviour.In this paper, we introduce this modelling framework, illustrate the capabilities of microC, a first cloudbased implementation of the framework, and we illustrate the range of potential applications that it enables.Specifically, we perform perturbation experiments of increasing complexity, in which we monitor over time the three-dimensional (3D) growth and evolution of mixed populations of cells.To achieve this, we chose the example of cancer: a complex disease where methods to study the link between genotype and phenotype are particularly and urgently needed (4). To inform our choices for the gene network and the model parameters, we exploited previously acquired data on gene interactions and cell growth from a number of independent publications. We then built our initial model in a mechanistic "bottom-up" fashion, progressing from the individual elements to the whole system. Following this strategy, we simulated the 3D growth of cell spheroids focusing on pathways underlying the main hallmarks of cancer, including sustained proliferative signals, resistance to cell death and evasion of growth suppressors (9).We thus considered a set of alterations amongst the most frequently obse...