Abstract
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines—or agents—to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex.
Highlights
Quantifying, and even defining, the complexity paradigm has been challenging due to differences among systems that are considered complex in terms of their information content, dimensionality and basic functional units [1]
Complex systems are usually characterized by the presence of numerous heterogeneous components that can interact non-linearly to yield a large variety of possible configurations [1], absence of rigid boundaries [2], flexibility in terms of component membership [2], and the ability to display emergent, selforganizing and adaptive behaviour [3]
Complicated systems—relatively straightforward to define in mathematical terms—partially share the first characteristic of a complex system, they differ from complex systems in terms of connectivity among system components
Summary
Quantifying, and even defining, the complexity paradigm has been challenging due to differences among systems that are considered complex in terms of their information content, dimensionality and basic functional units [1]. One may argue that cells contain the information needed to form the organism, but the outcome itself is a result of interactions between genes and proteins at the sub-cellular and cells at the cellular level Another reason why agent-based models tend to do better than their continuum counterparts is because the latter tend to be population-based, relating observables to each other via equations that may either be algebraic, or capture variability temporally (ODE) or spatiotemporally (PDE) [66]. Integrating agent-based models with their continuum counterparts, as has been tried elsewhere [31,68,69,70,71], is an elegant and, from a biological perspective, a more precise way of addressing the nontrivial problem of modelling biological systems Another weakness generally associated with ABM is the flexible and dynamic nature of agent interactions, which makes the patterns and outcomes of these interactions inherently unpredictable. The agent-based paradigm is probably the most perfect embodiment of these characteristics
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