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

Background The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silcio 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. Results We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether. Conclusions Our in silico results indicate that, in NSCLC, in the presence of a strong extrinsic chemotactic stimulus, and depending on the cell's location, downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.Comment: 37 pages, 7 figure

Professional Networks in the Life Sciences: Linking the Linked

The world wide web has furthered the emergence of a multitude of online expert communities. Continued progress on many of the remaining complex scientific questions requires a wide ranging expertise spectrum with access to a variety of distinct data types. Moving beyond peer-to-peer to community-to-community interaction is therefore one of the biggest challenges for global interdisciplinary Life Sciences research, including that of cancer. Cross-domain data query, access, and retrieval will be important innovation areas to enable and facilitate this interaction in the coming years

Life Sciences and the web: a new era for collaboration

The World Wide Web has revolutionized how researchers from various disciplines collaborate over long distances. This is nowhere more important than in the Life Sciences, where interdisciplinary approaches are becoming increasingly powerful as a driver of both integration and discovery. Data access, data quality, identity, and provenance are all critical ingredients to facilitate and accelerate these collaborative enterprises and it is here where Semantic Web technologies promise to have a profound impact. This paper reviews the need for, and explores advantages of as well as challenges with these novel Internet information tools as illustrated with examples from the biomedical community

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research. © 2008 Springer-Verlag

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Simulating non-small cell lung cancer with a multiscale agent-based model-2

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p>increases from 2.65 × 1.0 to 2.65 × 31.1, 2.65 × 31.2, and finally, to 2.65 × 50.0 nM. (From to ) plotted are the absolute change of PLC, rate of change of PLC, and rate of change of ERK. Note that the number of proliferations is decreasing gradually and finally disappears at a phase transition between the EGF concentrations of 2.65 × 31.1 and 2.65 × 31.2 nM. (For phenotype labeling see Fig. 4)

Shows the multicellular patterns that emerge through rule A and rule B, respectively

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p> Describes the numeric evolution () of each cell phenotype as well as of the [total] cell population () over time () for rule A () and rule B (), respectively. Note: proliferative tumor cells are labeled in , migratory cells in , quiescent cells in and dead cells in

Simulating non-small cell lung cancer with a multiscale agent-based model-1

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p>g the corresponding rule (see Fig. 3). The line indicates rule A-mediated migrations in , while the line denotes rule B-mediated proliferations in Fitting curves in are calculated using a standard linear least squares method. Slopes of the fitting curves are 1.40 cells/step in and 0.03 cells/step in , respectively. Note: The drop of the dashed red line in the of is caused by the termination of the simulation when a cell reached the source (in this case, no further computation on remaining cells will be performed)