Computational Models
Generally speaking, a computational (or mathematical) model is a set of equations that describes a biological system. In the case of the ICBP, models are developed to describe and simulate the complex process of cancer. Depending on the biological system studied, it could encompass the description of gene transcription to the scale of a whole organism.
Although the process of modeling is tightly linked to mathematics, programming and software development, the end product available to experimentalists is usually visual simulations. This computational power allows running ‘virtual’ experiments over few hours or days where the same experiments at the bench would carry over weeks or months. By itself, this feature of computational biology is very appealing to experimentalists. Yet, the main benefit of modeling resides in the fact that those simulations will help researchers to orientate their next set of experiments by uncovering importance in their biological system of some processes over others.
To understand the importance of computational biology in the ICBP Research Centers, here are few examples among many others.
 | Research Data Analysis
Migrating cells can be automatically tracked, using a motion tracking algorithm. In this example, three different cells followed by time-lapse videomicroscopy are tracked. Their separate paths and current positions are displayed over time, merged to the original time-lapse movie (green, red and yellow tracks). courtesy of Heiko Enderling, Ph.D., Tufts University School of Medecine, Boston MA |
 | Research Data Extrapolation
Software could be developed to meet experimentalists needs. This is the case of MedView, developed to visualize cell patterns using automaticly generated stacks of images. MedView is an application to solve medical problems using a graphical representation. You can read several sets of images and display them in different views. The user has the possibility to mark interesting points and get information about the brightness values at these points. He is able to select and cut interesting areas. In the stack view, all selected points will be shown as axises through all images to recognize coherences easier. Standard methods to manipulate images like zooming and contrast changing are implemented, too. courtesy of Heiko Enderling, Ph.D., Tufts University School of Medecine, Boston MA |
 | Data Simulation
Tumor growth can be simulated, based on biology data. In this example, you see the simulation of tumor growth in a heterogeneous, irregularly shaped domain. The tumor shown in red is initially located close to the nearby domain surface which is shown in dark yellow. Different tissue densities are shown in dark blue (lower density) and bright blue (higher density). Different tumor densities (high densities in bright pink and lower densities in dark pink) are displayed as the tumor grows in the domain. extracted from Visualisation of the Numerical Solution of Partial Differential Equation Systems in Three Space Dimensions and its Importance for Mathematical Models in Biology, H Enderling, ARA Anderson, MAJ Chaplain, GW Rowe, Mathematical Biosciences and Engineering 3(4), 571-582, 2006.
courtesy of Heiko Enderling, Ph.D., Tufts University School of Medecine, Boston MA |
 | Virtualization of Biological Process
It is probably the most challenging development of computational biology. It requires the expertises of biology and mathematics to develop and validate this kind of models. This example shows a simulation describing the development of a typical epithelial acinus over time. Different elements (inter- and intracellular) are computed: nuclei (blue), dead cells (red), cell-cell junctions (green), lateral cell membrane domains (white), tight junctions (yellow) and basal membrane domains (pink). courtesy of Katarzyna Rejniak, Ph.D., University of Dundee, Scotland, U.K. |
All material reproduced with the acknowledgements of the authors.
section maintained by J.Jourquin
last modified
2007-05-31 02:40
