network motifs - part of ``graphen und netzwerke in der ... · 8 ×3 different feedforward network...
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Network motifspart of “Graphen und Netzwerke in der Biologie”
Sonja Prohaska
Computational EvoDevoUniversity Leipzig
Leipzig, SS 2011
Sonja Prohaska Network Motifs
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Simple regulation
transcription factor (TF) Y regulates gene Xno additional interactionsY is activated by a signal SY other than trascriptional (e.g.ligand binding, posttranslational modification)when transcription begins, [X ] rises and converges to asteady-state level Xst
steady-state level Xst : is equal to the ratio of theproduction and degradation ratedegradation here is active degradation and dilution (by cellgrowth)when transcription stops, [X ] decays exponentially[X ](t) = Xst2−t/t1/2 and t1/2 = ln 2/λthe response time is the time it takes to reach Xst/2response time: is equal to the half-life t1/2 of Xnoise causes fluctuations of [X ] from 10 to 20%
Sonja Prohaska Network Motifs
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Add the decay to the simple regulation above!Sonja Prohaska Network Motifs
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Negative autoregulation (NAR)
TF X represses its own transcription
X has a strong promotor which leads to a rapid initial rise
followed by a sudden locking into the steady state
the steady state Xst is close to the repression threshold
NAR speeds up the response time
NAR can reduce cell-cell variation in protein levelslow [X ] enhanced productionhigh [X ] reduced productionnerrower distribution of protein levels
Sonja Prohaska Network Motifs
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Simpel regulation and NAR in experiments
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Positive autoregulation (PAR)
TF X activates its own transcription
S-shaped curve with inflection point[X ] lower than activation threshold below inflection point[X ] higher than activation threshold above inflection point
PAR slowes the response timePAR can enhance cell-cell variation in protein levelslow [X ] slow productionhigh [X ] rapid production
high variability in [X ] among cells
can lead to bimodal distributionsome cells have high [X ]others have low [X ]
differentiation-like partitioning of cells into two populations
helps populations to maintain a mixed phenotype andrespond better to fluctuations in the environment
Sonja Prohaska Network Motifs
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Variation in Cell Populations
Sonja Prohaska Network Motifs
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Feedforward loops (FLL)
Eight types of loops in motifs with three genes.
Sonja Prohaska Network Motifs
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Input functions
Two inputs for Z can be integrates as:
AND gate (requires both inputs)
OR gate (requires either input)
additive (inputs add up)
In combination with the 8 types of motif topologies this makes8 × 3 different feedforward network motifs of size 3.Input functions for transcription factor networks are usually notknown.
Sonja Prohaska Network Motifs
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Coherent type 1 feedforward loop – AND
delay after stimulation, no delay whenstimulation stops
dynamic behavior is called “sign sensitivedelay”sign: elevation or reduction of stimulus Sx
levelelevation → delayreduction → no delay
persistence detectorthe higher the activation threshold of Z by Ythe longer the delayshort stimulation does not increase Y fastenough to activate Zonly persistent stimulation activates Z
Sonja Prohaska Network Motifs
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Coherent type 1 feedforward loop – AND
Sonja Prohaska Network Motifs
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Sonja Prohaska Network Motifs
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Coherent type 1 feedforward loop – OR
no delay after stimulation, delay whenstimulation stops
when X is no longer active, still enough Y toactivate Z
the lower the decay rate of Y the longer thedelay
short loss of stimulation does not decrease Yfast to shut the production of Z
smoothes the fluctuation of Sx
Sonja Prohaska Network Motifs
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Sonja Prohaska Network Motifs
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Incoherent type 1 feedforward loop
X activates Z, but also represses Z (byactivating the repressor Y)
Z increases until Y reaches its repressorthreshold
this results in puls-like dynamics
if Y does not completely repress Z
the motif acts as a response accelerator
[Z ] reaches a certain non-zero steady statelevel
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puls-like dynamics
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response accelerator
Sonja Prohaska Network Motifs
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Sonja Prohaska Network Motifs
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single-input module (SIM)
coordinated expression of a group of genesdifferent activation thresholds for Z1, ...,Zn result in atemporally ordered activation of Z1, ...,Zn, an “expressionprogram”activation is often in functional order
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dense overlapping regulon (DOR)
carry out a computation by which multiple inputs are translatedinto multiple outputs
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Memory in transcription factor networks
double positive feedback loop (a): X and Y can remain“ON” even when activator Z disappears
double negative feedback loop (b): Z switches thesteady state of X and Y, the state persists even after Z isdeactivated
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Two incoherent type-1 feedforward loops generate pulses of Z1
and Z2, one coherent type-1 feedforward loops generates adelayed Z3 step
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Literature
Network motifs: theory and experimental approaches. by UriAlon; Nature Review Genetics (2007), 8, p450-461
Network motifs in integrated cellular networks oftranscription-regulation and protein-protein interaction. EstiYeger-Lotem, Shmuel Sattath, Nadav Kashtan, Shalev Itzkovitz,Ron Milo, Ron Y. Pinter and Uri Alon; PNAS (2004), 101 (16),p5934-5939
Sonja Prohaska Network Motifs