Seminar in Bioinformatics, Winter Network Motifs. An Introduction to Systems Biology Uri Alon Chapters 5-6. Meirav Zehavi
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1 Seminar in Bioinformatics, Winter 2010 Network Motifs An Introduction to Systems Biology Uri Alon Chapters 5-6 Meirav Zehavi
2 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
3 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
4 SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
5 SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
6 SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
7 SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
8 SIM - SIMs can generate temporal expression programs: LIFO - Example
9 SIM - SIMs can generate temporal expression programs: LIFO - Example
10 Multi-Output FFL - Definition - Functions
11 Multi-Output FFL - Definition - Functions
12 Multi-Output FFL - Definition - Functions
13 Multi-Output FFL - Definition - Functions
14 Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
15 Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
16 Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example X = flhdc, Y = flia, Z 1 = flil, Z 2 = flie, etc. K 1 < K 2 < < K n, K 1 > K 2 > > K n.
17 Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
18 DOR - Definition - Function E. coli stress response and stationary phase system
19 DOR - Definition - Function E. coli stress response and stationary phase system
20 Further Discussion - Four main network motif families cascade X Y Z, etc. - The global structure of sensory transcription networks Auto-Regulation X SIM Multi-Output FFL DOR
21 Further Discussion - Four main network motif families cascade X Y Z, etc. - The global structure of sensory transcription networks Auto-Regulation X SIM Multi-Output FFL DOR
22 Further Discussion - Four main network motif families - The global structure of sensory transcription networks Part of E. coli transcription network
23 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
24 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element Double-positive feedback loop Double-negative feedback loop X Y X Y Z Z
25 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element Double-positive feedback loop Double-negative feedback loop X Y X Y Z Z
26 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element X Y X Y Z Z
27 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element X Y X Y Z Z X Y Z Steady-state 1 ON ON ON Steady-state 2 OFF OFF OFF X Y Z Steady-state 1 ON OFF OFF Steady-state 2 OFF ON ON
28 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element + X Z + Y X Y - Z - X Y Z Steady-state 1 ON ON ON Steady-state 2 OFF OFF OFF X Y Z Steady-state 1 ON OFF OFF Steady-state 2 OFF ON ON
29 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element An example of double negative feedback loop
30 Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element
31 Long Transcription Cascades - The response time of each stage in cascades is governed by the degradation/dilution rate of the protein at that stage of the cascade. For stable proteins, this response time is on the order of cell generation time. - Developmental networks work on this timescale.
32 Long Transcription Cascades - The response time of each stage in cascades is governed by the degradation/dilution rate of the protein at that stage of the cascade. For stable proteins, this response time is on the order of cell generation time. - Developmental networks work on this timescale.
33 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
34 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
35 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
36 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
37 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
38 FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
39 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
40 Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
41 Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
42 Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
43 Protein Kinase Perceptrons - Rate of phosphorylation = vxy o - Y o + Y p = Y - dy p /dt = v 1 X 1 Y o + v 2 X 2 Y o ay p - At steady state: dy p /dt = 0 - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a
44 Protein Kinase Perceptrons - Rate of phosphorylation = vxy o - Y o + Y p = Y - dy p /dt = v 1 X 1 Y o + v 2 X 2 Y o ay p - At steady state: dy p /dt = 0 - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a
45 Protein Kinase Perceptrons - Rate of phosphorylation = vxy o - Y o + Y p = Y - dy p /dt = v 1 X 1 Y o + v 2 X 2 Y o ay p - At steady state: dy p /dt = 0 - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a
46 Protein Kinase Perceptrons - Rate of phosphorylation = vxy o - Y o + Y p = Y - dy p /dt = v 1 X 1 Y o + v 2 X 2 Y o ay p - At steady state: dy p /dt = 0 - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a
47 Protein Kinase Perceptrons - Rate of phosphorylation = vxy o - Y o + Y p = Y - dy p /dt = v 1 X 1 Y o + v 2 X 2 Y o ay p - At steady state: dy p /dt = 0 - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a
48 Protein Kinase Perceptrons - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a - Threshold of activation: w 1 X 1 + w 2 X 2 = 1 = = = =
49 Protein Kinase Perceptrons - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a - Threshold of activation: w 1 X 1 + w 2 X 2 = 1 = = = =
50 Protein Kinase Perceptrons - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a - Threshold of activation: w 1 X 1 + w 2 X 2 = 1 = = = =
51 Protein Kinase Perceptrons - Y p /Y = f(w 1 X 1 + w 2 X 2 ), where f(u) = u/(u+1), w 1 = v 1 /a, w 2 = v 2 /a - Threshold of activation: w 1 X 1 + w 2 X 2 = 1 = = = = AND OR
52 Protein Kinase Perceptrons
53 Protein Kinase Perceptrons negative weight negative weight XOR
54 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
55 Neuronal Networks - Definition - C. elegans
56 Neuronal Networks - Definition - C. elegans
57 Multi-Input FFL - FFL significance 3-node feedback loop - Definition - Example
58 Multi-Input FFL - FFL significance - Definition - Example
59 Multi-Input FFL - FFL significance - Definition - Example
60 Multi-Input FFL - dy/dt = bq(w 1 X 1 +w 2 X 2 > K y ) ay voltages strength of the synaptic connections step function leakage of current through the cell membrane - dz/dt = b q(w 1 X 1 +w 2 X 2 +w 3 Y > K z ) a Z
61 Multi-Input FFL - dy/dt = bq(w 1 X 1 +w 2 X 2 > K y ) ay - dz/dt = b q(w 1 X 1 +w 2 X 2 +w 3 Y > K z ) a Z
62 Multi-Input FFL - dy/dt = bq(w 1 X 1 +w 2 X 2 > K y ) ay - dz/dt = b q(w 1 X 1 +w 2 X 2 +w 3 Y > K z ) a Z persistence detector
63 Multi-Input FFL - dy/dt = bq(w 1 X 1 +w 2 X 2 > K y ) ay - dz/dt = b q(w 1 X 1 +w 2 X 2 +w 3 Y > K z ) a Z coincidence detector
64 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
65 Composite Networks - Signaling networks and transcription networks - Two-color motif example X Y Z transcription interaction protein-protein interaction
66 Composite Networks - Signaling networks and transcription networks - Two-color motif example X Y Z transcription interaction protein-protein interaction
67 Two-Protein Feedback - Definition - Thermostat explanation X Y Power supply Heater Slow Temperature Thermostat Fast
68 Two-Protein Feedback - Definition - Thermostat explanation X Y Power supply Heater Slow Temperature Thermostat Fast
69 Contents Network Motifs in - Sensory Transcription Networks - SIM - Multi-Output FFL - DORs - Further Discussion - Developmental Transcription Networks - Signal Transduction Networks - Neuronal Networks Composite Network Motifs Summary
70 Thank You
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