Simulating non-small cell lung cancer with a multiscale agent-based model

Abstract

Background: The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silico modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment.

Computational modeling of brain tumors: discrete, continuum or hybrid?

“…Conversely, continuum models can capture tumor growth at a collective scale that allows monitoring the expansion of a larger cluster of homogeneously behaving cells yet fail to register single cells, genes or proteins. Since both discrete and continuum modeling approaches have their own advantages and shortcomings (Table 1), and because quantifying the relationships between complex cancer phenomena at different scales is highly desirable, we and others have begun to move into the direction of hybrid modeling e.g., [4,58,67,72], or more appropriately, towards hybrid, multi-scale and multi-resolution algorithms as the next stage of cancer modeling in general, and brain tumor modeling in particular. While 'hybrid' refers to the integration of both discrete and continuum techniques, 'multi-resolution' means that cells at distinct topographic regions are treated differently in terms of the modeling approach applied.…”

“…Figure 1 shows a series of simulation results produced by the model [6], explaining how tumor growth dynamics at the cellular level can be related to alterations at the molecular level. This algorithm is flexible so that it can accommodate the governing, physical requirements of other cancer types, such as non-small cell lung cancer [67], which demonstrates the versatility of this design concept. [6] It is noteworthy that some efforts employ techniques analogous to ABM to study the clinical level of brain tumor behavior.…”

A Influencia da Diferenciação Fenotípica na Dinâmica do Crescimento Tumoral

“…Com base no modelo apresentado em [5], um sistema de equações diferenciais ordinárias modela a sinalização envolvendo 20 moléculas/compostos distintos. A taxa de variação de cada compostoé dada pela lei de conservação de massa entre as reações cinéticas de produção e consumo.…”

“…O fenótipo celularé orquestrado por uma rede complexa de sinalização molecular que integra estímulos extracelulares que culmina com uma resposta regulatória. Assumimos que em cada célula a proliferação e migraçãoé controlada pelo mecanismo de sinalização do fator de crescimento epidérmico EGF (Epidermal Growth Factor ) [5]. Este mecanismo está hiperativado em aproximadamente 30% de todos os cânceres [2] e aquié modelado por um sistema de equações diferenciais ordinárias.…”

Quantitative modeling perspectives on the ErbB system of cell regulatory processes

“…These authors consider the multi-cellular tissue to be an essentially continuous field for molecular processes, permitting description in terms of spatio-temporal differential equations without requiring discrete cells as explicit entities. The Deisboeck laboratory, on the other hand, has used an "agent-based" framework in which individual cells exhibit particular behaviors, to model tumor cell proliferation and migration in glioma [49] and non-small cell lung cancer [50]. Although only a limited set of signaling pathways were incorporated in all these models, their explicit presence in governing phenotypic behaviors of multi-cellular populations within a tissue environment represents an important advance.…”