BioModel engineering for multiscale Systems Biology

Monika Heiner, David Gilbert
2013 Progress in Biophysics and Molecular Biology  
We discuss some motivational challenges arising from the need to model and analyse complex biological systems at multiple scales (spatial and temporal), and present a biomodel engineering framework to address some of these issues within the context of multiscale Systems Biology. Our methodology is based on a structured family of Petri net classes which enables the investigation of a given system using various modelling abstractions: qualitative, stochastic, continuous and hybrid, optionally in
more » ... spatial context. We illustrate our approach with case studies demonstrating hierarchical flattening, treatment of space, and hierarchical organisation of space. Ó 2012 Elsevier Ltd. Motivation BioModel Engineering. Biology is increasingly becoming an informational science. This revolution has been driven by technological advances which have supported the development of studies at many levels of intra-and intercellular activity. These advances have facilitated the analysis of how the components of a biological system interact functionally e namely the field of Systems Biology (Hood, 2003) . At the heart of this field lies the construction of models of biological systems, see Fig. 1 . These models are used for description of acquired understanding, or analysis which should ideally be both explanatory of biological mechanisms and predictive of the behaviour of the system when it is perturbed by, e.g., mutations, chemical interventions or changes in the environment. Furthermore, models can be used to help make genetic engineering easier and more reliable, serving as design templates for novel synthetic biological systems e an emerging discipline known as Synthetic Biology (Endy, 2005, Heinemann and Panke, 2006) . Central to both Systems and Synthetic Biology is BioModel Engineering (BME) which is the science of designing, constructing and analysing computational models of biological systems (Breitling et al., 2010) . Modelling means abstraction. A general discussion of the relationships between computational modelling and abstraction can be found in (Melham, 2013) . We can vary the degree of abstraction and the specific information abstracted away from along each of the following dimensions. 1. Hierarchical organisation of components e like molecules, organelles, cells, tissues, organs, organisms, i.e., the abstraction levels of the model objects are chosen as appropriate; indeed several levels can be mixed within one model. 2. Function e the model operations (atomic events) can be abstracted to their essential effects and can refer to a wide variety of events. In the most abstract case the relationship between objects being modelled can be reduced to interactions, e.g., between genes or proteins. More detailed descriptions can be given at the level of, e.g., chemical reactions with precise or abstract stoichiometries, or conformational change of a protein, transport of a molecule, etc. 3. Granularity of description e i.e., the resolution within a particular level of abstraction, depending on the completeness of our knowledge within one level. 4. Time e governs everything; however, we may abstract away from time to obtain simplified qualitative models with corresponding analysis, specifically if insufficient kinetic information is available. This kind of abstraction typically yields a conservative approximation of the behaviour, and considers more behaviour than is actually possible under some given timing constraints.
doi:10.1016/j.pbiomolbio.2012.10.001 pmid:23067820 fatcat:mf4cq3jawbbfrgqb6kut5b4equ