Multiscale agent-based modelling of ovarian cancer progression under the stimulation of the STAT 3 pathway.

This research is developed to simulate ovarian cancer progression with signal transducers and activators of the transcription 3 (STAT 3) pathway. The main focus is on studying how the STAT 3 pathway affects the cancer cells' biomechanical phenotype under the stimulation of the interleukin-6 (IL-6) cytokine and various well-known microscopic factors. The simulated results agreed with recent experimental evidence that ovarian cancer cells with a stimulated STAT 3 pathway have high survival rates and drug resistance. And we discussed how the IL6 and these well-known microscopic factors impacted the cancer progression.

Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: incorporating EGFR signaling pathway and angiogenesis.

BACKGROUND: The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment. RESULTS: The novel angiogenesis module integrated into the agent-based tumor model is based on a set of reaction-diffusion equations that describe the spatio-temporal evolution of the distributions of micro-environmental factors such as glucose, oxygen, TGFα, VEGF and fibronectin. These molecular species regulate tumor growth during angiogenesis. Each tumor cell is equipped with an EGFR signaling pathway linked to a cell-cycle pathway to determine its phenotype. EGFR TKIs are delivered through the blood vessels of tumor microvasculature and the response to treatment is studied. CONCLUSIONS: Our simulations demonstrated that entire tumor growth profile is a collective behaviour of cells regulated by the EGFR signaling pathway and the cell cycle. We also found that angiogenesis has a dual effect under TKI treatment: on one hand, through neo-vasculature TKIs are delivered to decrease tumor invasion; on the other hand, the neo-vasculature can transport glucose and oxygen to tumor cells to maintain their metabolism, which results in an increase of cell survival rate in the late simulation stages.

Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units.

Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.

Indicator dilution models for the quantification of microvascular blood flow with bolus administration of ultrasound contrast agents.

Indicator dilution methods have a long history in the quantification of both macro- and microvascular blood flow in many clinical applications. Various models have been employed in the past to isolate the primary pass of an indicator after an intravenous bolus injection. The use of indicator dilution techniques allows for the estimation of hemodynamic parameters of a tumor or organ and thus may lead to useful diagnostic and therapy monitoring information. In this paper, we review and discuss the properties of the lognormal function, the gamma variate function, the diffusion with drift models, and the lagged normal function, which have been used to model indicator dilution curves in different fields of medicine. We fit these models to contrast-enhanced ultrasound time-intensity curves from liver metastases and the ovine corpora lutea. We evaluate the models' performance on the image data and compare their predictions for hemodynamic-related parameters such as the area under the curve, the mean transit time, the full-width at half-maximum, the time to the peak intensity, and wash-in time. The models that best fit the experimental data are the lognormal function and the diffusion with drift.

Quantification of tumor microvascularity with respiratory gated contrast enhanced ultrasound for monitoring therapy.

The aim of this feasibility study was to evaluate the response to cytotoxic and antiangiogenic treatment of colorectal liver metastasis using respiratory gated contrast enhanced ultrasonography. Seven patients were monitored with contrast enhanced ultrasound. Sulfur hexafluoride filled microbubbles (SonoVue; Bracco S.P.A., Milan, Italy) were used as contrast agent and the scans were performed with a nonlinear imaging technique (power modulation) at low transmit power (MI=0.06). The mean image intensity in the metastatic lesion and in the normal liver parenchyma were measured as a function of time and time-intensity curves from linearized image data were formed. A novel respiratory gating technique was utilized to minimize the effects of respiratory motion on the images. A reference position of the diaphragm (or other echogenic interface) was selected and all frames where the diaphragm deviated from that position were rejected. The wash-in time (start of enhancement to peak) of metastasis and adjacent normal liver parenchyma was measured from time-intensity curves. The ratio of wash-in time of the lesion to that of the normal parenchyma (WITR) was used to compare the perfusion rate. In a reproducibility study (five patients), the average deviation of WITR was found to be 9%. There was an increase in the WITR for patients responding to treatment (mean WITR increase of 17% after first dose of treatment and 75% at the end of the therapy). In four out of five patients (80%) responding to therapy WITR predicted their response from the first treatment. All six patients that responded to therapy by the end of the therapy cycle (6-9 doses) were correctly predicted by using WITR. The WITR may be a new surrogate marker indicative of early tumor response for colorectal cancer patients undergoing cytotoxic and antiangiogenic therapy. (E-mail: maverk@ucy.ac.cy).

Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate.

We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an implicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.

Failure of mean field theory at large N.

We study strongly coupled lattice QCD with N colors of staggered fermions in 3+1 dimensions. While mean field theory describes the low temperature behavior of this theory at large N, it fails in the scaling region close to the finite temperature second order chiral phase transition. The universal critical region close to the phase transition belongs to the 3D XY universality class even when N becomes large. This is in contrast to Gross-Neveu models where the critical region shrinks as N (the number of flavors) increases and mean field theory is expected to describe the phase transition exactly in the limit of infinite N. Our work demonstrates that infrared fluctuations can be important close to second order phase transitions even when N is strictly infinite.