Investigating biocomplexity through the agent-based paradigm - PubMed (original) (raw)

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Investigating biocomplexity through the agent-based paradigm

Himanshu Kaul et al. Brief Bioinform. 2015 Jan.

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.

Keywords: agent-based model; biological complexity; cell; computational modeling; emergence; hybrid models.

© The Author 2013. Published by Oxford University Press.

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Figures

Figure 1:

Figure 1:

This figure highlights the parallels between an agent and a cell. Top: A communicating agent (stream X-machine); adapted from [29]. Bottom: Cell decision-making; signalling cues derived from [40]. Depending on the multitude of input signals that a cell responds to, it transitions into a phenotype based on hitherto unknown biological rules. The input signals (represented on the arrows) can be spatial, chemical or electrical and induce a response from the cell. The cell in its new transition state seems to be quite aware of its latest phenotype, a feature that in the ABM is represented by the update of agent-memory.

Figure 2:

Figure 2:

The dynamics of X-machine communication. The message board maintains a database of all the messages sent by the agents. The agents read, and send, messages from (and to) the message board. Adapted from the FLAME user manual available at

http://www.flame.ac.uk/docs/

.

Figure 3:

Figure 3:

The figure shows the parallel between how the continuum and discrete approaches are used to simulate biological phenomena. Calculating the error function in ABM is analogous to setting the convergence criteria in continuum methods. Similarly, meshing a geometry to assign discrete locations where the differential equations are solved is equivalent to distributing agents (in the environment) capable of transitioning between a finite set of states based on the logic/mathematical rules assigned to them.

Figure 4:

Figure 4:

This sequence displays results generated from a platform developed by integrating the agent-based with the continuum approach. The figure shows various stages of cell chemotaxis under the influence of an arbitrary chemokine. The cells, on sensing chemokine-deficient conditions, try to move into chemokine-rich regions. The four frames were captured at 0, 20, 30 and 50 hours of experiment time. Whereas chemokine concentration in the cellular microenvironment was modelled using the transport phenomena solver, cellular chemotaxis was simulated using FLAME. The image first appeared in [31] and was reprinted under the Creative Commons Attribution License. A colour version of this figure is available at BIB online:

http://bib.oxfordjournals.org

.

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