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Specialized or flexible feed-forward loop motifs: a question of topology.

Macía J, Widder S, Solé R - BMC Syst Biol (2009)

Bottom Line: Network motifs are recurrent interaction patterns, which are significantly more often encountered in biological interaction graphs than expected from random nets.The distribution probability distributions are linked to the degree of specialization or flexibility of the given network topology.The implications for the emergence of different motif topologies in complex networks are outlined.

View Article: PubMed Central - HTML - PubMed

Affiliation: Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB), 08003 Barcelona, Spain. javier.macia@upf.edu

ABSTRACT

Background: Network motifs are recurrent interaction patterns, which are significantly more often encountered in biological interaction graphs than expected from random nets. Their existence raises questions concerning their emergence and functional capacities. In this context, it has been shown that feed forward loops (FFL) composed of three genes are capable of processing external signals by responding in a very specific, robust manner, either accelerating or delaying responses. Early studies suggested a one-to-one mapping between topology and dynamics but such view has been repeatedly questioned. The FFL's function has been attributed to this specific response. A general response analysis is difficult, because one is dealing with the dynamical trajectory of a system towards a new regime in response to external signals.

Results: We have developed an analytical method that allows us to systematically explore the patterns and probabilities of the emergence for a specific dynamical response. The method is based on a rather simple, but powerful geometrical analysis of the system's clines complemented by an appropriate formalization of the response probability.

Conclusion: Our analysis allows to determine unambiguously the relationship between motif topology and the set of potentially implementable functions. The distribution probability distributions are linked to the degree of specialization or flexibility of the given network topology. The implications for the emergence of different motif topologies in complex networks are outlined.

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Related in: MedlinePlus

Input effects on the clines. The change of the clines upon input is illustrated in these examples. The green lines are the clines (5) and (6) without input crossing at point ϕX = 0. In absence of input, the system is stable in this point. Upon input the clines change and are depicted as blue lines, shifting the stable point to ϕX > 0. The change of the fixed point, forces the system to evolve towards the new regime. The red dashed line represents its trajectory in the phase space. The inset shows the corresponding time course of the system's output. The parameters of the simulations are: γY = 1, αX = 0,  = 100, dY = 0.1, γZ = 1, βX = 3,  = 100, βY = 0,  = 1, βXY = 0,  = 100 and dZ = 0.1. In absence of input X = 0 and in presence of input we consider X = 1.
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Figure 2: Input effects on the clines. The change of the clines upon input is illustrated in these examples. The green lines are the clines (5) and (6) without input crossing at point ϕX = 0. In absence of input, the system is stable in this point. Upon input the clines change and are depicted as blue lines, shifting the stable point to ϕX > 0. The change of the fixed point, forces the system to evolve towards the new regime. The red dashed line represents its trajectory in the phase space. The inset shows the corresponding time course of the system's output. The parameters of the simulations are: γY = 1, αX = 0, = 100, dY = 0.1, γZ = 1, βX = 3, = 100, βY = 0, = 1, βXY = 0, = 100 and dZ = 0.1. In absence of input X = 0 and in presence of input we consider X = 1.

Mentions: Furthermore, expression (6) shows a single inflection point, i.e. a point where curvature changes from concave to convex or viceversa, in the biological domain defined by Y ≥ 0 and Z ≥ 0, but no extrema (local maximum or minimum). In order to understand how the input triggers the dynamical response, we study the configuration of the two clines with X = 0, i.e. no input, versus X > 0, i.e. with input. In figure (2) we show a numerical example. In absence of input, the system is governed by a single stable fixed point, denoted by ϕX = 0, located at the crossing of the clines (5) and (6). On addition of input, the fixed point moves, because the cline configuration changes. Now the system shifts from ϕx = 0 to the new stable fixed point ϕx > 0. The point ϕX > 0 is determined by the crossing of the new location of the clines for X > 0. The dynamical response of the system, which is represented by the trajectory of the FFL in phase space, is generated by this change of the stable regime. A subset of four parameters, i.e. {αX, βX, βY, βXY}, describing the interactions of the proteins, classify the type of the circuits into coherent and incoherent according to [28]. Without input, only βY is relevant for the geometry of the cline Ż = 0. If βY > 1 the cline rises, because ZHA > Z0, whereas for βY < 1 the cline decreases as shown in figure (3a). Under the presence of an external input the other parameters become relevant (figure (3b)):


Specialized or flexible feed-forward loop motifs: a question of topology.

Macía J, Widder S, Solé R - BMC Syst Biol (2009)

Input effects on the clines. The change of the clines upon input is illustrated in these examples. The green lines are the clines (5) and (6) without input crossing at point ϕX = 0. In absence of input, the system is stable in this point. Upon input the clines change and are depicted as blue lines, shifting the stable point to ϕX > 0. The change of the fixed point, forces the system to evolve towards the new regime. The red dashed line represents its trajectory in the phase space. The inset shows the corresponding time course of the system's output. The parameters of the simulations are: γY = 1, αX = 0,  = 100, dY = 0.1, γZ = 1, βX = 3,  = 100, βY = 0,  = 1, βXY = 0,  = 100 and dZ = 0.1. In absence of input X = 0 and in presence of input we consider X = 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2749051&req=5

Figure 2: Input effects on the clines. The change of the clines upon input is illustrated in these examples. The green lines are the clines (5) and (6) without input crossing at point ϕX = 0. In absence of input, the system is stable in this point. Upon input the clines change and are depicted as blue lines, shifting the stable point to ϕX > 0. The change of the fixed point, forces the system to evolve towards the new regime. The red dashed line represents its trajectory in the phase space. The inset shows the corresponding time course of the system's output. The parameters of the simulations are: γY = 1, αX = 0, = 100, dY = 0.1, γZ = 1, βX = 3, = 100, βY = 0, = 1, βXY = 0, = 100 and dZ = 0.1. In absence of input X = 0 and in presence of input we consider X = 1.
Mentions: Furthermore, expression (6) shows a single inflection point, i.e. a point where curvature changes from concave to convex or viceversa, in the biological domain defined by Y ≥ 0 and Z ≥ 0, but no extrema (local maximum or minimum). In order to understand how the input triggers the dynamical response, we study the configuration of the two clines with X = 0, i.e. no input, versus X > 0, i.e. with input. In figure (2) we show a numerical example. In absence of input, the system is governed by a single stable fixed point, denoted by ϕX = 0, located at the crossing of the clines (5) and (6). On addition of input, the fixed point moves, because the cline configuration changes. Now the system shifts from ϕx = 0 to the new stable fixed point ϕx > 0. The point ϕX > 0 is determined by the crossing of the new location of the clines for X > 0. The dynamical response of the system, which is represented by the trajectory of the FFL in phase space, is generated by this change of the stable regime. A subset of four parameters, i.e. {αX, βX, βY, βXY}, describing the interactions of the proteins, classify the type of the circuits into coherent and incoherent according to [28]. Without input, only βY is relevant for the geometry of the cline Ż = 0. If βY > 1 the cline rises, because ZHA > Z0, whereas for βY < 1 the cline decreases as shown in figure (3a). Under the presence of an external input the other parameters become relevant (figure (3b)):

Bottom Line: Network motifs are recurrent interaction patterns, which are significantly more often encountered in biological interaction graphs than expected from random nets.The distribution probability distributions are linked to the degree of specialization or flexibility of the given network topology.The implications for the emergence of different motif topologies in complex networks are outlined.

View Article: PubMed Central - HTML - PubMed

Affiliation: Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB), 08003 Barcelona, Spain. javier.macia@upf.edu

ABSTRACT

Background: Network motifs are recurrent interaction patterns, which are significantly more often encountered in biological interaction graphs than expected from random nets. Their existence raises questions concerning their emergence and functional capacities. In this context, it has been shown that feed forward loops (FFL) composed of three genes are capable of processing external signals by responding in a very specific, robust manner, either accelerating or delaying responses. Early studies suggested a one-to-one mapping between topology and dynamics but such view has been repeatedly questioned. The FFL's function has been attributed to this specific response. A general response analysis is difficult, because one is dealing with the dynamical trajectory of a system towards a new regime in response to external signals.

Results: We have developed an analytical method that allows us to systematically explore the patterns and probabilities of the emergence for a specific dynamical response. The method is based on a rather simple, but powerful geometrical analysis of the system's clines complemented by an appropriate formalization of the response probability.

Conclusion: Our analysis allows to determine unambiguously the relationship between motif topology and the set of potentially implementable functions. The distribution probability distributions are linked to the degree of specialization or flexibility of the given network topology. The implications for the emergence of different motif topologies in complex networks are outlined.

Show MeSH
Related in: MedlinePlus