Seminar in Bioinformatics, Winter 2010 Network Motifs An Introduction to Systems Biology Uri Alon Chapters 5-6 Meirav Zehavi
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
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
SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
SIM - Definition - Why are SIMs motifs? - What is the function of SIMs? - How did SIMs evolve?
SIM - SIMs can generate temporal expression programs: LIFO - Example
SIM - SIMs can generate temporal expression programs: LIFO - Example
Multi-Output FFL - Definition - Functions
Multi-Output FFL - Definition - Functions
Multi-Output FFL - Definition - Functions
Multi-Output FFL - Definition - Functions
Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
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.
Multi-Output FFL - Multi-Output FFL can generate FIFO temporal order - Example
DOR - Definition - Function E. coli stress response and stationary phase system
DOR - Definition - Function E. coli stress response and stationary phase system
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
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
Further Discussion - Four main network motif families - The global structure of sensory transcription networks Part of E. coli transcription network
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
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
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
Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element X Y X Y Z Z
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
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
Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element An example of double negative feedback loop
Regulating and Regulated Feedback - Regulating feedback for decision making - Regulated feedback as a memory element
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.
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.
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
FFLs - Form parts of larger and more complex circuits - Example: Bacillus subtilis
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
Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
Signal Transduction Networks - General information - Model: protein kinase cascades - Multi-layer perceptrons
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
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
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
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
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
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 = = = =
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 = = = =
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 = = = =
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
Protein Kinase Perceptrons
Protein Kinase Perceptrons negative weight negative weight XOR
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
Neuronal Networks - Definition - C. elegans
Neuronal Networks - Definition - C. elegans
Multi-Input FFL - FFL significance 3-node feedback loop - Definition - Example
Multi-Input FFL - FFL significance - Definition - Example
Multi-Input FFL - FFL significance - Definition - Example
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
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
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
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
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
Composite Networks - Signaling networks and transcription networks - Two-color motif example X Y Z transcription interaction protein-protein interaction
Composite Networks - Signaling networks and transcription networks - Two-color motif example X Y Z transcription interaction protein-protein interaction
Two-Protein Feedback - Definition - Thermostat explanation X Y Power supply Heater Slow Temperature Thermostat Fast
Two-Protein Feedback - Definition - Thermostat explanation X Y Power supply Heater Slow Temperature Thermostat Fast
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
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