In Silico Biology: Making the Most of Parallel Computing

In Silico Biology: Making the Most of Parallel Computing

Dimitri Perrin, Heather J. Ruskin, Martin Crane
Copyright: © 2010 |Pages: 20
DOI: 10.4018/978-1-60566-768-3.ch003
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Abstract

Biological systems are typically complex and adaptive, involving large numbers of entities, or organisms, and many-layered interactions between these. System behaviour evolves over time, and typically benefits from previous experience by retaining memory of previous events. Given the dynamic nature of these phenomena, it is non-trivial to provide a comprehensive description of complex adaptive systems and, in particular, to define the importance and contribution of low-level unsupervised interactions to the overall evolution process. In this chapter, the authors focus on the application of the agent-based paradigm in the context of the immune response to HIV. Explicit implementation of lymph nodes and the associated lymph network, including lymphatic chain structure, is a key objective, and requires parallelisation of the model. Steps taken towards an optimal communication strategy are detailed.
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Modelling Complex Systems

Two categories of complex system modelling are discussed, top-down and bottom-up designs (Bohringer and Rutherford, 2008). The main concept of a top-down design is to break down a system into several components, expected to be easier to manipulate and understand. The overall system is formulated and specified, but without going into details of its parts. In an iterative process, each component is then defined in more detail and, if necessary, split into lower-level subsystems. This process, repeated until the entire specification is obtained for its base elements, involves use of black boxes which facilitate model development, but may also hinder model validation if these fail to elucidate elementary mechanisms of the system studied.

In a bottom-up approach, individual base components are detailed and designed, and then linked together. These form more complex systems, which are again linked, in an iterative process, and the top-level model increasingly emerges. This approach is, therefore, particularly suited to complex adaptive systems, which in their structure demonstrate both emergence and self-organisation. The remainder of this Section considers several examples of top-down/bottom-up design, grouped in three families: mathematical, shape-space, and agent-based models.

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