Nding of the interplay between these mechanisms is essential for the development of therapies to limit metastatic progression. However, the complexity of this system and its multiple redundancies limits the ability of traditional reductionist biological experiments to predict downstream effects of a perturbation of a single mechanism. Complex, dynamic systems such as circulating tumor cell adhesion can be explored through the use of computer models. Incorporating these models with traditional experimentation allows for hypotheses to be discarded or advanced for further examination in a less resource-intensive manner. Significant efforts have been made in computational modeling the multiple steps of tumor growth, with the majority of efforts examining the local invasion and growth seen in primary tumors [3,6-9]. Computational modeling of metastases has examined cell population dynamics based on a combination of proliferation, mutation and metastasis rates derived from clinical data to predict the number and time distribution of metastases from primary tumors, [72-76]. Others use mathematical models of cell growth and specific biologic processes such as matrix modeling and migration to predict metastasis growth over time [77-80]. Agent-based modeling of metastases has been employed to characterize the selective forces involved in the generation of circulating, potentially metastatic tumor cells [81,82], implicate the role of host immunity in the generation of satellite metastases [1,2,4,10-14], and examine the interactions of metastatic tumor cells with the host immune system for optimizing tumor vaccine delivery [15-18]. Other models of individual tumor cell interactions with host environment, employing knowledge of biomechanics and enzyme kinetics in a system of differential equations that describe integrin interactions and tumor cell growth patterns [83,84]. In addition, several models of single circulating cell adhesion to vasculature have been developed incorporating biomechanic principles, fluid dynamics and knowledge of integrin activation (Reviewed in [22-25]). However, 9-diazaspiro[5.5]undecane Ethyl 5-cyclopropyl-1H-pyrazole-3-carboxylate Hydroxy-PEG2-(CH2)2-Boc 7-Chloro-1 these models were designed to examine single cell adhesion, not the complex processes of tumor cell interaction with other cell types. Given the multiple interactions shown to be necessary for tumor cell adhesion, a computational PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/7780048 model incorporating these various interactions can lead to a more detailed understanding of these early adhesion events. This ABMEM is tert-Butyl (7-bromoheptyl)carbamate the first computational model to examine the multiple cell-cell interactions specifically related to adhesion of circulating tumor cells to vascular endothelium. This allows for an examination of population dynamics not possible with current single-cell models biomechanical models. Incorporation of intra-cellular pathways allows for a higher-resolution understanding of the functional signaling andUppal et al. Theoretical Biology and Medical Modelling 2014, 11:17 http://www.tbiomed.com/content/11/1/Page 11 ofreceptor-binding events 3-(2,4-Dichlorophenoxy)azetidine leading to stable tumor cell adhesion. The ABMEM, as an ABM, has a modular structure, both with respect to the agents/cells included, as well as the agents’ rules. This modular design allows for the addition of PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/14445666 further cell types (as was done with the addition of tumor cells to the ABMEM) or molecular mechanisms as necessary to validate the model against prior experimental findings. This property of ABMs is consistent with the general iterative refinement protocol described in the.