1 / 10

Algorithm for bending structure of Myxobacteria Nan Chen and Yilin Wu

kenton
Download Presentation

Algorithm for bending structure of Myxobacteria Nan Chen and Yilin Wu

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. In this presentation, we developed an algorithm for describing the bending structure of myxobacteria. In this algorithm, cell structure is presented by key nodes and connection bonds. System potential energy includes stretching energy and bonding energy. Cell collision is implemented by a MC algorithm. Further development is still in progress.

  2. Algorithm for bending structure of Myxobacteria Nan Chen and Yilin Wu

  3. Algorithm Description Key nodes are used to describe the flexibility of myxobacteria

  4. Stretching and bending Energy Stretching Energy Bending Energy

  5. MonteCarlo Algorithm for cells movement Algorithm for one MC step For I = 1 to n-1 Randomly choose one node Randomly choose one moving direction and distance If (Collision happen) Not accepted and next Calculate energy change Calculate acceptance probability if (Accpted) update configuration

  6. Algorithm for cells collision Turn to this direction

  7. Why we choose MC algorithm, not deterministic algorithm (MD)? • Advantage • MC Algorithm could easily handle verycomplicated cell configurations • MC algorithm allows cells still to move when cell lock happens • Disadvantage • MC algorithm is slower than deterministic algorithm

  8. Algorithm of Adding slime field • The cell “searches” the slime field in front of S-end. Sum all the slimes in the N areas (as shown in next slide) respectively, denoting by S(i), i= 1,…N; • The probability to choose one of the N directions (as shown in next slide) is proportional to the slime concentrations in the N areas: • The 2D surface was already mapped into 2D lattice. I create a slime field on this lattice; • In every time step, each cell deposits slimes by adding 1 slime to each lattice site occupied by the A-end; • In every time step, before moving a cell, specify the position O(x, y) of the its S-end or head;

  9. 3 Y 2 1 Search area Y’ S end N-1 X’ n A end Moving direction X Slime distribution f(x) 90 -90

  10. Summary • Further work to integrate both the cell-cell interactions and slime field.

More Related