Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

Abstract

Modeling is essential to integrating knowledge of human physiology. Comprehensive self-consistent descriptions expressed in quantitative mathematical form define working hypotheses in testable and reproducible form, and though such models are always "wrong" in the sense of being incomplete or partly incorrect, they provide a means of understanding a system and improving that understanding. Physiological systems, and models of them, encompass different levels of complexity. The lowest levels concern gene signaling and the regulation of transcription and translation, then biophysical and biochemical events at the protein level, and extend through the levels of cells, tissues and organs all the way to descriptions of integrated systems behavior. The highest levels of organization represent the dynamically varying interactions of billions of cells. Models of such systems are necessarily simplified to minimize computation and to emphasize the key factors defining system behavior; different model forms are thus often used to represent a system in different ways. Each simplification of lower level complicated function reduces the range of accurate operability at the higher level model, reducing robustness, the ability to respond correctly to dynamic changes in conditions. When conditions change so that the complexity reduction has resulted in the solution departing from the range of validity, detecting the deviation is critical, and requires special methods to enforce adapting the model formulation to alternative reduced-form modules or decomposing the reduced-form aggregates to the more detailed lower level modules to maintain appropriate behavior. The processes of error recognition, and of mapping between different levels of model complexity and shifting the levels of complexity of models in response to changing conditions, are essential for adaptive modeling and computer simulation of large-scale systems in reasonable time.

The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery

“…An exception is the comprehensive high-level description presented by Bassingthwaighte et al (2006), which includes a review of strategies and tactics for adaptive multiscale modelling together with important cautionary notes on model reductions, adaptive model modification and related error propagation while confirming that multiscale and multilevel systems modelling is in its infancy.…”

Biological Control Through Intraguild Predation: Case Studies in Pest Control, Invasive Species and Range Expansion

“…The reduction in model complexity from a multiple-dimensional system to a two-dimensional system is a simplification. While predictions can only be made at the complexity level of the model studied (see Bassingthwaighte et al, 2006, for a discussion), there are numerous studies finding that increasing model complexity often does not improve the fit to time series data and that there is very little loss of predictive ability (Stillman et al, 2000;Smets et al, 2002;Arhonditsis and Brett, 2004). We have included the minimum level of detail to describe our system and a more realistic model is being analysed elsewhere (Drury et al, manuscript in preparation).…”

The bio-nano-interface in predicting nanoparticle fate and behaviour in living organisms: towards grouping and categorising nanomaterials and ensuring nanosafety by design

“…Additionally, given the degree of redundancy in signalling pathways, decoupling effects of biomolecules bound to nanoparticles from those of their unbound counterparts will be challenging. However, systems biology approaches are making significant strides here [104][105][106].…”