Colorectal tumour simulation using agent based modelling and high performance computing

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High- End Computing (ICHEC).Peer ReviewedPostprint (author's final draft

Computational modelling of the behaviour of biomarker particles of colorectal cancer in fecal matter

Colorectal adenocarcinoma is one of the carcinogenic diseases that is increasing the morbidity and mortality rates worldwide. The disease initially occurs through the segregation of biomarker substances in the human system without manifesting symptoms that affect the health of the carrier. Early detection would allow the application of more effective treatments, less invasive procedures and reduce the development of cancer. The purpose of this investigation was the elaboration of a mathematical model and the development of computational simulations to visualize the behavior of biomarker particles in transit through the colon. The flow conditions, properties of the viscous medium and biological regions of interest were established. Constitutive models, numerical conditions and solution strategies were determined. A numerical grid was used to represent the model of the colon and the human feces that carry the bioparticles (biomarkers). The results indicated the trajectories of the bioparticles in the fecal mass and the interactive movement with the natural contractions of the colon. The analysis of the movement of the biomarker particles can provide future less invasive alternatives for the detection in real time of the cancer by means of the implantation of biosensors in the walls of the colon

CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS

Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Bevacizumab (bvz) is a first choice anti-angiogenic drug in oncology and is primarily administered in combination with chemotherapy. It has been hypothesized that anti-angiogenic drugs enhance efficacy of cytotoxic drugs by “normalizing” abnormal tumor vessels and improving drug penetration. Nevertheless, the clinical relevance of this phenomenon is still unclear with several studies over recent years suggesting an opposing relationship. Herein, we sought to develop a new computational tool to interrogate anti-angiogenic drug scheduling with particular application in the setting of colorectal cancer (CRC). Specifically, we have employed a mathematical model of vascular tumour growth which interrogates the impact of anti-angiogenic treatment and chemotherapeutic treatment on tumour volume. Model predictions were validated using CRC xenografts which underwent treatment with a clinically relevant combinatorial anti-angiogenic regimen. Bayesian model selection revealed the most appropriate term for capturing the effect of treatments on the tumour size, and provided insights into a switch-like dependence of FOLFOX delivery on the tumour vasculature. Our experimental data and mathematical model suggest that delivering chemotherapy prior to bvz may be optimal in the colorectal cancer setting

An Agent-Based Model of Cryoprotectant Equilibration in Secondary Stage Preantral Ovarian Follicles

Young cancer patients have limited options for fertility treatment when facing gonadotoxic treatment. One promising fertility treatment for young cancer patients is the cryopreservation of immature ovarian follicles followed by maturation and subsequent reimplantation. However, preantral ovarian follicles currently have lower post-thaw success rates compared to mature oocytes and embryos. Previous research suggests that damage to vital intercellular connections, Transzonal Projections (TZPs), occurs during the cryopreservation process and may account for the observed lower post-thaw success rate in this tissue. It is likely that cryoprotective agent (CPA) equilibration is the cryopreservation step during which TZP damage occurs. Constructing a biologically relevant model of CPA equilibration and the associated damage may allow for improved protocols as measured by increased post-thaw success rates. Agent-based models are a promising technique to capture steps in the cryopreservation process, such as CPA equilibration. In this thesis, I conducted a series of experiments with typical CPAs and nonpermeating solutes at different temperatures using preantral ovarian follicles from a non-human primate (Rhesus monkeys) to measure TZP damage. In these experiments, I also estimated relevant permeability parameters within the tissue. I found that the majority of TZP damage was likely the result of mechanical forces that occurred during the cell volume reduction phase of CPA equilibration. Furthermore, through these experiments, I demonstrate that for this tissue type, parameters collected either during monolayer or single-cell experiments can be used to construct full tissue models. Using the derived experimental parameters and available literature values, I constructed and validated a 3-D agent-based model to capture CPA equilibration in preantral ovarian follicles. My agent-based model utilizes parallel computing on an average desktop computer and allows for the rapid design and testing of CPA equilibration protocols. The model I constructed can account for both mechanical and toxic damage. Importantly, my model accurately captures the experimental damage to TZPs in the majority of simulations. Lastly, I propose several theoretically improved cryopreservation protocols for preantral ovarian follicles. The research presented in this thesis demonstrates that agent-based models can be utilized to capture steps in the cryopreservation in silico and represents a non-invasive, less costly means to test and improve CPA equilibration protocols

Colorectal tumour simulation using agent based modelling and high performance computing

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High- End Computing (ICHEC).Peer Reviewe