MicroC is a novel computational framework for conducting in-silico biological
experiments and generate or test new hypotheses. MicroC may be used to study
the effects of mutations and cell-cell or cell-microenvironment interactions
on the dynamics of cell growth. Almost all features of MicroC (networks, cell microenvironment, cell-cell interaction, mutations) can be customised by the user.
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 has typically lacked a component
describing the physical context, which is a key determinant of phenotype.
In our recent
study, we present a novel modelling framework that enables to study the link between genotype,
signalling networks and cell behaviour in a 3D physical environment. 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 co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as
nutrients availability, and their interactions.
Figure. Conceptual representation of microC environment and simulation components.
The microC framework enables multiscale simulations linking genotype to phenotype, via gene networks,
by fully exploiting agent-based modelling. The technical details on the framework and its implementation
are provided in the Methods. Here, we illustrate a study where four setups of increasing complexity are
considered, evaluating the impact of new elements in a controlled fashion.
A. We simulate the effect of introducing gene network perturbations (activating, +, or inactivating, -,
mutations) in single clones
B. We grow mono-clonal or multi-clonal populations of cells in a 3D environment (spheres visually
represent single cells)
C. We study competition between different clones grown together (colours represent different mutation
profiles; the growth curve demonstrates exponential growth of aggressive clones; 2D simulation show
growth patterns under competition)
D. We enable interaction with the surrounding microenvironment, e.g. oxygen concentration variation that
results to necrotic cells
E. We enable signalling between cells (represented here as tiny black spheres produced by activated cells;
junctions represent receptors).
The source code is subject to an aGPLv3.0 licence.