54 research outputs found
Emergent Behaviors from A Cellular Automaton Model for Invasive Tumor Growth in Heterogeneous Microenvironments
Understanding tumor invasion and metastasis is of crucial importance for both
fundamental cancer research and clinical practice. In vitro experiments have
established that the invasive growth of malignant tumors is characterized by
the dendritic invasive branches composed of chains of tumor cells emanating
from the primary tumor mass. The preponderance of previous tumor simulations
focused on non-invasive (or proliferative) growth. The formation of the
invasive cell chains and their interactions with the primary tumor mass and
host microenvironment are not well understood. Here, we present a novel
cellular automaton (CA) model that enables one to efficiently simulate invasive
tumor growth in a heterogeneous host microenvironment. By taking into account a
variety of microscopic-scale tumor-host interactions, including the short-range
mechanical interactions between tumor cells and tumor stroma, degradation of
extracellular matrix by the invasive cells and oxygen/nutrient gradient driven
cell motions, our CA model predicts a rich spectrum of growth dynamics and
emergent behaviors of invasive tumors. Besides robustly reproducing the salient
features of dendritic invasive growth, such as least resistance and intrabranch
homotype attraction, we also predict nontrivial coupling of the growth dynamics
of the primary tumor mass and the invasive cells. In addition, we show that the
properties of the host microenvironment can significantly affect tumor
morphology and growth dynamics, emphasizing the importance of understanding the
tumor-host interaction. The capability of our CA model suggests that
well-developed in silico tools could eventually be utilized in clinical
situations to predict neoplastic progression and propose individualized optimal
treatment strategies.Comment: 30 pages, 10 figures, 4 tables; to be appear in PLoS Comput. Bio
Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy
A systematic understanding of the evolution and growth dynamics of invasive
solid tumors in response to different chemotherapy strategies is crucial for
the development of individually optimized oncotherapy. Here, we develop a
hybrid three-dimensional (3D) computational model that integrates
pharmacokinetic model, continuum diffusion-reaction model and discrete cell
automaton model to investigate 3D invasive solid tumor growth in heterogeneous
microenvironment under chemotherapy. Specifically, we consider the effects of
heterogeneous environment on drug diffusion, tumor growth, invasion and the
drug-tumor interaction on individual cell level. We employ the hybrid model to
investigate the evolution and growth dynamics of avascular invasive solid
tumors under different chemotherapy strategies. Our simulations reproduce the
well-established observation that constant dosing is generally more effective
in suppressing primary tumor growth than periodic dosing, due to the resulting
continuous high drug concentration. In highly heterogeneous microenvironment,
the malignancy of the tumor is significantly enhanced, leading to inefficiency
of chemotherapies. The effects of geometrically-confined microenvironment and
non-uniform drug dosing are also investigated. Our computational model, when
supplemented with sufficient clinical data, could eventually lead to the
development of efficient in silico tools for prognosis and treatment strategy
optimization.Comment: 41 pages, 8 figure
A Cellular Automaton Model for Tumor Dormancy: Emergence of a Proliferative Switch
abstract: Malignant cancers that lead to fatal outcomes for patients may remain dormant for very long periods of time. Although individual mechanisms such as cellular dormancy, angiogenic dormancy and immunosurveillance have been proposed, a comprehensive understanding of cancer dormancy and the “switch” from a dormant to a proliferative state still needs to be strengthened from both a basic and clinical point of view. Computational modeling enables one to explore a variety of scenarios for possible but realistic microscopic dormancy mechanisms and their predicted outcomes. The aim of this paper is to devise such a predictive computational model of dormancy with an emergent “switch” behavior. Specifically, we generalize a previous cellular automaton (CA) model for proliferative growth of solid tumor that now incorporates a variety of cell-level tumor-host interactions and different mechanisms for tumor dormancy, for example the effects of the immune system. Our new CA rules induce a natural “competition” between the tumor and tumor suppression factors in the microenvironment. This competition either results in a “stalemate” for a period of time in which the tumor either eventually wins (spontaneously emerges) or is eradicated; or it leads to a situation in which the tumor is eradicated before such a “stalemate” could ever develop. We also predict that if the number of actively dividing cells within the proliferative rim of the tumor reaches a critical, yet low level, the dormant tumor has a high probability to resume rapid growth. Our findings may shed light on the fundamental understanding of cancer dormancy.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.010993
Mechanical and Systems Biology of Cancer
Mechanics and biochemical signaling are both often deregulated in cancer,
leading to cancer cell phenotypes that exhibit increased invasiveness,
proliferation, and survival. The dynamics and interactions of cytoskeletal
components control basic mechanical properties, such as cell tension,
stiffness, and engagement with the extracellular environment, which can lead to
extracellular matrix remodeling. Intracellular mechanics can alter signaling
and transcription factors, impacting cell decision making. Additionally,
signaling from soluble and mechanical factors in the extracellular environment,
such as substrate stiffness and ligand density, can modulate cytoskeletal
dynamics. Computational models closely integrated with experimental support,
incorporating cancer-specific parameters, can provide quantitative assessments
and serve as predictive tools toward dissecting the feedback between signaling
and mechanics and across multiple scales and domains in tumor progression.Comment: 18 pages, 3 figure
Taking aim at moving targets in computational cell migration
Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs
- …