Modelling Tumour Growth and Progression (original) (raw)
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Computer simulations and modeling in oncology: Methods and applications
WIT Transactions on Biomedicine and Health, 2009
Computational models and simulations can be powerful tools for gaining an insight into the extremely complex mechanisms governing tumoral growth. In order to be relied upon, however, they must be validated by comparison with sufficiently long strings of experimental or observational data. For obvious ethical reasons it is virtually impossible to obtain such data "in vivo". It may be, therefore, expedient to study the growth of tumoral lines "in vitro" or "ex vivo", i.e. by transplanting them into lab animals (e.g., mice). In fact, experiments with as many as 900 successive transplants into new healthy mice have been performed. Using a recently proposed technique for the analysis of experimental datasets (the Phenomenological Universalities Approach), we have succeeded to reproduce, to an excellent level of reliability, the results of such "multipassage" growth and to explain quantitatively why the growth curves become progressively steeper at each new transplant. We believe that our method could also be applied to study metastatic diffusion and suggest new experiments to further validate our approach and results.
Computational Modeling and Simulations in Life Sciences
Annals of Information Systems, 2014
Billions of math operations per second may be performed by computers anymore. Obviously, a human lifetime would be needed to do the same number of computations. When used in medication, the groundbreaking potential of the mathematical modeling approach is obvious. In Medicine, mathematical modeling is able to vastly improve both drug creation and clinic technology. Progress in technology and the development of new experimental methods has had a noteworthy effect on the study of disease. This has raised new researching opportunities, such as: gathering in-depth 'molecular fingerprints' from patients carrying information, for example, on genotype, gene or protein expression, or metabolism levels; studying intracellular processes in living and diseased tissue through the control of gene activity inside the cells; and creating understandable illness-specific databases that include both patients' medical history with laboratory and clinical data in addition to storing useful tissue samples. In this article, the authors attempt to provide the readers with a view of current and future use of mathematical modeling in medicine.
COMPUTER SIMULATION: A THINKING TOOL FOR EDUCATION AND RESEARCH IN BIOMEDICINE
Proc. Symp. Computer Applications in the Health Sciences, 1985
This paper gives a general description of computer simulation and how it is used in biomedicine. Mathematical models, the basis for computer simulation, began to be used in biology toward the end of th e 19th century and more extensively in the 1940's. Several of the pioneer mathematical models are described briefly. Two methods for writing equations to represent a biological system are described: the ad hoc method for analysis of unknown systems and the mechanistic approach for testing concepts when more information is av ail able. Compu ter solution of the mod el equations is described nex t, particularly the recent developments in hardware and software that have made this step more accessible to biologists. Several examples of recent simulation programs or papers are cited, some developed for educational purposes and some for research. Finally, a brief description of t he National Biomedical Simulation Resource is given, along with a summary of its goals for supporting computer simulation in biomedical research.
Role of simulation in understanding biological systems
Computers & Structures, 2003
Recent advances in computer performance and affordability have accelerated their application to understanding biological phenomena. Computational models are now an integral part of biological and biomedical research. This paper presents a range of examples that illustrate the important role that computational simulation plays in research in the biological and biomedical sciences.
De Gruyter eBooks, 2020
Traditional medicine has, since its inception, registered numerous examples of treatment resulting in positive or negative outcomes, depending on the patient. This observation was reinforced after the completion of the human genome sequencing project. As it turns out, individual humans exhibit genetic differences despite possessing the same genome. The identification of so-called single nucleotide polymorphisms confirms and explains the familiar phenomenon of variable reaction to treatment [1, 2]. Given that even siblings differ in terms of their chromosomal material, the genetic variability of the general human population should come as no surprise. Recent research has also revealed differences in the composition of gut bacterial flora resulting from diverse dietary habits [3]. In light of such specificities, the need for individual, personalized therapy becomes evident. Fortunately, many high-tech tools can be used in medical practice (Chapter 1). The most direct applications of personalized medicine involve individualized pharmacotherapy. Drugs designed to interact with a specific target may help improve therapeutic outcomes while remaining affordable, particularly in the presence of bioinformatic technologies. Identifying links between molecular chemistry and pathological processes is among the goals of system biology [4]. Access to computer software that simulates the complete proteome may help discover causal reactions-not just in the scope of a particular disease, but between seemingly unconnected processes occurring in the organism [4]. Harnessing the power of modern computers in an objective, dispassionate therapeutic process will enhance the capabilities of medical practitioners, for example, by offering access to vast databases of biological and medical knowledge (Chapter 2). Moreover, processing data with the use of artificial intelligence algorithms may lead to conclusions which a human would not otherwise be able to reach (Chapter 2). Closer collaboration between communication system experts and biologists should help identify promising research directions and explain the methods by which organisms-the most complex biological systems known to man-identify and process information (Chapter 3). Gaining insight into the molecular phenomena will help resolve some long-standing fundamental questions. Even before this happens, however, medical science can reap benefits by exploiting existing solutions and models (Chapter 4). Eliminating transplant rejection is of critical importance in individualized therapy. Three-dimensional bioprinting technologies represent an important milestone on this path (Chapter 5). They can be used to build arbitrarily complex objects, with local variations in the applied materials. An advanced printing environment may enable introduction of biological material (e.g., cells harvested from the patient for whom the implant is being created) directly at the printing stage (Chapter 5). Similarly, the shape of the printed tissue may accurately reflect the patient's needs, which
Engineering Simulations for Cancer Systems Biology
Current Drug Targets, 2012
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions.
Modeling and simulation in biomedicine
Proceedings / the ... Annual Symposium on Computer Application [sic] in Medical Care. Symposium on Computer Applications in Medical Care, 1991
A group of researchers and educators in The Netherlands, Germany and Czechoslovakia have developed and adapted mathematical computer models of phenomena in the field of physiology and biomedicine for use in higher education. The models are graphical and highly interactive, and are all written in TurboPascal or the mathematical simulation language PSI. An educational shell has been developed to launch the models. The shell allows students to interact with the models and teachers to edit the models, to add new models and to monitor the achievements of the students. The models and the shell have been implemented on a MS-DOS personal computer. This paper describes the features of the modeling package and presents the modeling and simulation of the heart muscle as an example.
Mathematical Modeling and Computer Simulation in Cancer Dynamics
Development of cancer is a complex multi-step process that requires wide range of understanding from different fi elds of science and technology. Mathematical modeling is one such fi eld that comes to the advanced research in cancer progression and treatment. Mathematical models of cancer have been extensively developed with the aim to predict tumor growth and therapeutic strategies. In this review, we discuss some of the important mathematical contributions to the study of solid tumor growth. Several complex multi scale models have been set up to address questions in the prediction of usefulness and toxicity of chemotherapy and radiotherapy and mathematical modeling is a highly associative fi eld integrating the real time problem with the various simulative models based on mathematical tools.