1 / 12

Cellular Automata Model of Cell Seeding and Growth into a Three Dimensional Extracellular Matrix

Cellular Automata Model of Cell Seeding and Growth into a Three Dimensional Extracellular Matrix. Lyndsey Schutte ~ Independent Research Spring 2005. Why you, too, should love Tissue Engineering.

masao
Télécharger la présentation

Cellular Automata Model of Cell Seeding and Growth into a Three Dimensional Extracellular Matrix

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cellular Automata Model of Cell Seeding and Growth into a Three Dimensional Extracellular Matrix Lyndsey Schutte ~ Independent Research Spring 2005

  2. Why you, too, should love Tissue Engineering A goal of TE is grow cells into healthy tissue, in vitro, to replace that which will not grow back. • organ transplantation • cartilage replacement • skin and blood vessel graphs • blood transfusions

  3. Materials: it takes two to tango To grow a tissue, both the right cells and extracellular matrix (ECM) are needed. Cells become sick when left in an unnatural feeling environment. And a matrix has no purpose if not to be a home for cells. Photos courtesy of U. of Western Ontario BME dept

  4. -“Cell… what?” -“Cell seeding” -“… Cell what?” Cell seeding = placing cells onto the matrix The cells will grow into the matrix, multiply, differentiate with the help of growth factors, and hopefully grow into a healthy normal piece of tissue, (as opposed to a very expensive pink lump) . All tissues systems in humans are 3D.

  5. Why model? • Put together the results of hundreds of different labs • See trends • Predict/extrapolate results • Find characteristics of missing pieces • Predict important questions

  6. The Cellular Automata Model Game of Life- John Conway Each unit or cell of the grid follows the same exact set of rules, and the state of a cell depends on the state of its neighbors. The gosper glider gun -Pattern curtsey of bitstorm.org and Edwinn Martin

  7. My program- E-Tissues Has two types of cells/automata: the biological cells and the environment. One by one, starting from one corner and moving to the opposite corner, each individual cell is called into the same function. The biological cells change states based upon just the environment and information the environment passes to it.

  8. Step 1: Initializing the Model 1a) The user enters in the number of cells and the program creates an array of biological cell structs filled with standard fibroblast data 1b) In the computer, a block of ECM is declared as a environment struct 1c) The computer randomly places cells onto the top of the matrix. 1d) A global array that keeps track of each cell’s location and ID is created.

  9. Steps 2, 3, 4, …n: EnviroWorks 2a) Depending on the conditions of the environment, it modifies each cell’s health 2b) Series of if/then and case statements to determine what other functions should be called • Cell walking • Cell reproduction • Fiber production

  10. End Results The repetition of calling the cells should produce a realistic simulation of tissue growth.

  11. Next Steps • Reset different variables to be more biologically accurate. (time, speed of cell crawling, cell density, etc) • Let the user change environment properties during the simulation • Add functions to handle cell-cell binding and fiber production • Add if statements to handle molecular signals

  12. Future Goals and Pipedreams • Be able to load in saved files of different matrices and cells, and then save results of an experiment to continue later. • Make files for different cell types, filled with constants to define it’s characteristics • Continue to add data as it is discovered • Create an interface so other labs can add data to an online database to create a more accurate model.

More Related