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tems, where stochastic behavior of large populations
of proliferating and signaling cells is driven by the
same underlying regulatory machinery encoded in the
genome. We also provide a prototype implementa-
tion capable of simulating cell populations with mil-
lions of cells on a standard personal computer. Even
though the method is rather simple and requires only a
handful of parameters to run a simulation, it is able to
reproduce the results of many more established meth-
ods for wide variety of models relevant for problems
currently under consideration by the modeling com-
munity. In some cases, like the linear cell lineage sys-
tem, it can give us new insights missed by the ODE
model due to its more accurate representation of small
cell populations.
While the results shown are promising, the imple-
mentation is still in an early phase and could greatly
benefit from multiple improvements. One key area
that will work on in the future is extending the model
to take into account spatial aspect of cell populations.
Such functionality would greatly expand the range of
possible applications of this model, however mod-
eling spatially variable signalling without great de-
crease in the method performance poses a consider-
able challenge.
6 AVAILABILITY
The STOPS (STOchastic Population Simulation)
software implementation is publicly available under
the GNU GPL v.2 license. The implementation of all
three case studies is included in current version avail-
able at http://launchpad.net/stops.
ACKNOWLEDGEMENTS
This work was partially supported by the Polish Min-
istry of Science and Education grant number N N301
065236 and by the Foundation for Polish Science
within Homing Plus programme co-financed by the
European Union - European Regional Development
Fund.
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