Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model

Abstract

We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor beta (TGFbeta) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFbeta concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFbeta fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFbeta signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFbeta concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFbeta profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFbeta do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.

Identifying therapeutic targets in a combined EGFR-TGF R signalling cascade using a multiscale agent-based cancer model

“…We have previously developed a set of multiscale agent-based lung cancer models integrating both molecular and microscopic levels to examine NSCLC growth dynamics in 2D and 3D microenvironments (Wang et al, 2007(Wang et al, , 2009. Using the 2D model as the computational platform, we also presented a novel 'cross-scale' sensitivity analysis method to identify model parameters that have significant effect on the tumour's expansion rate .…”

“…We briefly reintroduce the main features of the multiscale 3D agent-based NSCLC model (Wang et al, 2009), which encompasses both molecular (signalling pathway) and microscopic (multicellular) scales. At the molecular level, two stimuli, epidermal growth factor (EGF) and transforming growth factor β (TGFβ), trigger downstream signalling through different routes but converge at the activation of the Raf signal.…”

“…This process then initiates the ERK signalling cascade. Figure 1 shows, in brief, the implemented signalling scheme (see Wang et al, 2009 for detailed pathway kinetics). At the microscopic level, we construct a 3D microenvironment consisting of a discrete cube with 200 × 200 × 200 grid points (Fig.…”

“…Hence, the nutrient source is the most attractive location for the chemotactically acting tumour cells because it maintains the highest weight for each of the aforementioned four cues. Moreover, throughout the simulation, the concentrations of these four chemical cues are continuously updated at a fixed rate (see Wang et al, 2009 for corresponding equations).…”

Computational imaging and automated identification for aqueous environments

“…The MIT Cycling Team in particular was instrumental during my years, thanks especially to the cyclo-freakin-cross team, road team, and track team, and I consider many of the MIT riders to be integral parts of my Boston family. These friends have o¤ered encouragement and reminders that everyone has their own challenges to meet [209], [351], [411], and that spaggling would not be allowed no matter how hard the race seemed to be at the time.…”