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The concentration gradient of the Bicoid morphogen, which is establishedduring the early stages of a Drosophila melanogaster embryonic development,determines the differential spatial patterns of gene expression and subsequentcell fate determination. This is mainly achieved by diffusion elicited by thedifferent concentrations of the Bicoid protein in the embryo. Such chemicaldynamic progress can be simulated by stochastic models, particularly theGillespie alogrithm. However, as with various modelling approaches in biology,each technique involves drawing assumptions and reducing the model complexitysometimes limiting the model capability. This is mainly due to the complexityof the software modelling approaches to construct these models. Agent-basedmodelling is a technique which is becoming increasingly popular for modellingthe behaviour of individual molecules or cells in computational biology.  This paper attempts to compare these two popular modelling techniques ofstochastic and agent-based modelling to show how the model can be studied indetail using the different approaches. This paper presents how to use thesetechniques with the advantages and disadvantages of using either of these.Through various comparisons, such as computation complexity and resultsobtained, we show that although the same model is implemented, both approachescan give varying results. The results of the paper show that the stochasticmodel is able to give smoother results compared to the agent-based model whichmay need further analysis at a later stage. We discuss the reasons for theseresults and how these could be rectified in systems biology research.

Converting a Systems Dynamic Model to an Agent-based model for studying the Bicoid morphogen gradient in Drosophila embryo