BioDynaMo is a biological processes simulator developed by an international community of researchers and software engineers working closely with neuroscientists. The authors have taken part in the development of the physical engine, and they are currently working on gene expression, i.e. the process by which the heritable information in a gene - the sequence of DNA base pairs - is made into a functional gene product, such as protein or RNA. Typically, gene regulatory models employ either statistical or analytical approaches, being the former already well understood and broadly used. In this paper, we utilize analytical approaches representing the regulatory networks by means of differential equations, such as Euler and Runge-Kutta methods. The two solutions are implemented in the BioDynaMo project and are compared for accuracy and performance.
@cite_0: A large percentage of buildings in domestic orspecial-purpose is expected to become increasingly "smarter"in the future, due to the immense benefits in terms of en-ergy saving, safety, flexibility, and comfort, that relevant newtechnologies offer. As concerns hardware, software, or platformlevel, however, no clearly dominant standards currently exist. Such standards, would ideally, fulfill a number of importantdesiderata, which are to be touched upon in this paper. Here, we will present a prototype platform for supporting multipleconcurrent applications for smart buildings, which is utilizing anadvanced sensor network as well as a distributed microservicesarchitecture, centrally featuring the Jolie programming language. The architecture and benefits of our system are discussed, as wellas a prototype containing a number of nodes and a user interface, deployed in a real-world academic building environment. Ourresults illustrate the promising nature of our approach, as wellas open avenues for future work towards its wider and largerscale applicability.
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@cite_17: Spatial organization is a core challenge for all large agent-based models with local interactions. In biological tissue models, spatial search and reinsertion are frequently reported as the most expensive steps of the simulation. One of the main methods utilized in order to maintain both favourable algorithmic complexity and accuracy is spatial hierarchies. In this paper, we seek to clarify to which extent the choice of spatial tree affects performance, and also to identify which spatial tree families are optimal for such scenarios. We make use of a prototype of the new BioDynaMo tissue simulator for evaluating performances as well as for the implementation of the characteristics of several different trees.
@cite_17 presented a markup language to describe biochemical network models named SBML. One of their primary goals was to create a common format that can be used across simulators. Several implementation exist for this specification @cite_12 , @cite_0 .