1 / 25

Cellular Automata Modeling of Signaling and Metabolic Pathways

Cellular Automata Modeling of Signaling and Metabolic Pathways. Danail Bonchev Lemont B. Kier Chao-Kun Cheng. Some Introductory Remarks. Does a Biologist Need Philosophy of Science?. broader horizons. critical thinking. openness to new ideas. Does a Biologist Need Math?.

makya
Télécharger la présentation

Cellular Automata Modeling of Signaling and Metabolic Pathways

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 Modeling of Signaling and Metabolic Pathways Danail Bonchev Lemont B. Kier Chao-Kun Cheng

  2. Some Introductory Remarks • Does a Biologist Need Philosophy of Science? • broader horizons • critical thinking • openness to new ideas • Does a Biologist Need Math? • Hegel • Math is beauty and fun! • Math begins with definitions • The next 10-15 Years Will Be the Most Exciting in the History of Biology and Medicine • If You Don’t Want To Be an Outsider, Be a Forerunner!

  3. Remarks on Cellular Automata Method for Modeling Dynamics of Systems • A method that mirrors the discreteness of systems in space, time, and state in contrast to the continuum created by differential equations. • It provides both temporal and spatial models of systems dynamics, and enables identifying patterns of dynamic behavior. • CA models indicate potential targets for destroying pathogens or protecting human cells, thus leading to pharmaceutical applications. • CA models provide predictions of dynamic behavior that can be verified experimentally. • The technique is incredibly simple, fast, and entertaining.

  4. THE GOAL To Establish Cellular Automata Method As a Basic Method for Modeling Dynamics of Biological Pathways and Networks To Identify Dynamic Patterns That Would Enable Controlling Important Cellular Pathways

  5. The Lysine Biosysnthesis Pathway KEGG release 4.1 (December 1997)

  6. The Yeast Protein-Protein Interaction Network H. Jeong, S. P. Mason, A.-L. Barabasi, Z. N. Oltvai, Nature (2001) 411, 41.

  7. E1 E2 MAPKK MAPKK-P E3 MAPKKK MAPKKK* MAPK-P MAPK-PP E4 MAPKK-PP MAPK THE MAPK CASCADE A signaling pathway, relaying signals from the plasma membrane to targets in the cytoplasm and nucleus L. B. Kier, D. Bonchev, G. A. Buck, Modeling Biochemical Networks: A Cellular Automata Approach, Chem. Biodiversity 2, 233-243 (2005).

  8. Information the CA Models Can Provide • Temporal Models – Variation of Ingredients Concentration With Time • Spatial Models – Effective Concentrations at Steady-State Conditions • Signal Amplification • Specific Dynamics Prediction • Means of Pathway Control

  9. Example of A Temporal Dependence

  10. Example of MAPK-Cascade Spatial Models

  11. MAPK – The Sigmoidal Pattern of Enzymes Cooperative Action

  12. MAPK – The Concentration/Enzyme Activity Contour Plots

  13. Table 1. Effects of modeling enzyme inhibition in the MAPK cascade by decreasing the variable enzyme propensity

  14. Table 2. Inhibiting enzymes E1 to E4 as a tool for controlling the MAPK pathway

  15. The Apoptosis Pathway • Cellular suicide, also known as programmed cell death • A normal method of disposing of damaged, unwanted, or unneeded cells • Eliminate cells that threaten the organism's survival • Some forms of cancer result when this process of cell death • is somehow interrupted, and the cells grow without any control

  16. Cleavage of Caspase Substrates CASP6 DISC CASP10 Heterodimer DFF FADD FAS-L FAS-R CASP3 Death activator DFF45 DFF40 CASP8 Death-Inducing Signaling Complex CASP7 Initiator Caspases Start DNA Fragmentation Executor Caspases The Apoptosis Pathway Membrane protein

  17. The Interactions FAS-L + DISC DISC’ + CASP-8* FAS-L + DISC DISC’’ + CASP-1 CASP-8* + CASP-10 CASP-8* + CASP-10* CASP-8* + CASP-3 CASP-8* + CASP-3* CASP-8* + CASP-6 CASP-8* + CASP-6* CASP-8* + CASP-7 CASP-8* + CASP-7* CASP-10* + CASP-10 2CASP-10* CASP-10* + CASP-3 CASP-10* + CASP-3* CASP-10* + CASP-6 CASP-10* + CASP-6* CASP-10* + CASP-7 CASP-10* + CASP-7* CASP-3* + CASP-3 2CASP-3* CASP-3* + CASP-3 2CASP-3* CASP-3* + CASP-6 CASP-3* + CASP-6* CASP-3* + CASP-7 CASP-3* + CASP-7* CASP-3* + DFF CASP-3* + DFF45 + DFF40 CASP-7* + CASP-7 2CASP-7* CASP-7* + CASP-6 CASP-7* + CASP-6* CASP-7* + DFF CASP-7* + DFF45 + DFF40

  18. The Input Files - 1 The file name is:apopt5_7.inf apopt5_7.str is the Str file on which the prb file is based 100 Num of Columns 100 Num of Rows 1 Torus 1 for yes 0 for no The number of cells per cell types are below: cell type number of cells A 100 B 100 C 0 D 0 D* 0 E 100 E* 0 F 100 F* 0 G 100 G* 0

  19. The Input Files - 2 The file name is:apopt5_6.str 13 number of side types 13 number of cell types Their names are: Their colors are: Their names are: SA Black A SB Blue B SC Green C SD Cyan D SD* Red D* SE Magenta E SE* Yellow E* SF White F SF* DarkBlue F* SG DarkGreen G SG* DarkCyan G* SH DarkRed H SJ Dark Magenta J

  20. The Input Files - 3 apopt5_7.str is the Str file on which the prb file is based 0 Num of SidevsSide (w.r.t symm and anti-symm) 1 1 for symmetric 0 1 for anti-symm. The breaking and joining prb. w.r.t. side vs side are below: side vs side breaking prb. joining prb SA vs SA 1 0 SA vs SB 1 1 SA vs SC 1 0 SA vs SD 1 0 SA vs SD* 1 0 SA vs SE 1 0 SA vs SE* 1 0 SA vs SF 1 0 SA vs SF* 1 0 SA vs SG 1 0 SA vs SG* 1 0 SA vs SH 1 0 SA vs SJ 1 0 SB vs SB 1 0 SB vs SC 1 0 SB vs SD 1 0 SB vs SD* 1 0 SB vs SE 1 0 SB vs SE* 1 0 SB vs SF 1 0 SB vs SF* 1 0 SB vs SG 1 0

  21. 10 rules for *****Paired change after move***** 1 A 0 0 B 0 0 A 0 0 C 0 0 0.02 1 A 0 0 B 0 0 A 0 0 D 0 0 0.02 1 C 0 0 D 0 0 C 0 0 D* 0 0 0.1 1 C 0 0 E 0 0 C 0 0 E* 0 0 0.1 1 C 0 0 F 0 0 C 0 0 F* 0 0 0.1 1 C 0 0 G 0 0 C 0 0 G* 0 0 0.1 1 D* 0 0 D 0 0 D* 0 0 D* 0 0 0.5 1 D* 0 0 E 0 0 D* 0 0 E* 0 0 0.1 1 D* 0 0 F 0 0 D* 0 0 F* 0 0 0.1 1 D* 0 0 G 0 0 D* 0 0 G* 0 0 0.1 1 E* 0 0 E 0 0 E* 0 0 E* 0 0 0.5 1 E* 0 0 F 0 0 E* 0 0 F* 0 0 0.1 1 E* 0 0 G 0 0 E* 0 0 G* 0 0 0.1 1 E* 0 0 H 0 0 E* 0 0 J 0 0 0.05 1 G* 0 0 F 0 0 G* 0 0 F* 0 0 0.1 1 G* 0 0 G 0 0 G* 0 0 G* 0 0 0.5 1 G* 0 0 H 0 0 G* 0 0 J 0 0 0.05

  22. Apoptosis Rate Dependence on Caspase-8 Activation

  23. Apoptosis Rate Dependence on the Activity of the Two Initiator Caspases

  24. Apoptosis Rate Dependence on the Activity of the Two Executor Caspases

  25. 1080 76 1740 102 1300 65 1060 76 3 7 9 B B B 700 80 B 740 82 680 63 A A C A C A C 1 1300 65 C D 2100 99 D 1020 122 1020 48 640 73 1040 110 1160 76 B 860 90 B A B C 1520 72 1500 73 A C A C 5 2 1120 110 B D 10 660 70 D A B C D 2000 228 920 78 A C 1020 166 1180 48 1000 49 B 4 D 8 620 63 A C 1280 143 D 6 700 122 Other Projects Correlated Topology/Dynamics Patterns

More Related