503 research outputs found
Mathematical modeling of the metastatic process
Mathematical modeling in cancer has been growing in popularity and impact
since its inception in 1932. The first theoretical mathematical modeling in
cancer research was focused on understanding tumor growth laws and has grown to
include the competition between healthy and normal tissue, carcinogenesis,
therapy and metastasis. It is the latter topic, metastasis, on which we will
focus this short review, specifically discussing various computational and
mathematical models of different portions of the metastatic process, including:
the emergence of the metastatic phenotype, the timing and size distribution of
metastases, the factors that influence the dormancy of micrometastases and
patterns of spread from a given primary tumor.Comment: 24 pages, 6 figures, Revie
Information Processing and Distributed Computation in Plant Organs
The molecular networks plant cells evolved to tune their development in response to the environment are becoming increasingly well understood. Much less is known about how these programs function in the multicellular context of organs and the impact this spatial embedding has on emergent decision-making. Here I address these questions and investigate whether the computational control principles identified in engineered information processing systems also apply to plant development. Examples of distributed computing underlying plant development are presented and support the presence of shared mechanisms of information processing across these domains. The coinvestigation of computation across plant biology and computer science can provide novel insight into the principles of plant development and suggest novel algorithms for use in distributed computing
Mathematical models of avascular cancer
This review will outline a number of illustrative mathematical models describing the growth of avascular tumours. The aim of the review is to provide a relatively comprehensive list of existing models in this area and discuss several representative models in greater detail. In the latter part of the review, some possible future avenues of mathematical modelling of avascular tumour development are outlined together with a list of key questions
Mathematical models of avascular cancer
This review will outline a number of illustrative mathematical models describing the growth of avascular tumours. The aim of the review is to provide a relatively comprehensive list of existing models in this area and discuss several representative models in greater detail. In the latter part of the review, some possible future avenues of mathematical modelling of avascular tumour development are outlined together with a list of key questions
Dynamic Load Balancing Strategy for Parallel Tumor Growth Simulations
In this paper, we propose a parallel cellular automaton tumor growth model that includes load balancing of
cells distribution among computational threads with the introduction of adjusting parameters. The obtained
results show a fair reduction in execution time and improved speedup compared with the sequential tumor
growth simulation program currently referenced in tumoral biology. The dynamic data structures of the model
can be extended to address additional tumor growth characteristics such as angiogenesis and nutrient intake
dependencies
Theoretical Modeling Techniques and Their Impact on Tumor Immunology
Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field
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