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Signal duration and the time scale dependence of signal integration in biochemical pathways.

Locasale JW - BMC Syst Biol (2008)

Bottom Line: We compute the dynamic, frequency dependent gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales.We show that multi-staged cascades are effective in integrating signals of long duration whereas multi-staged cascades that operate in the presence of negative feedback are effective in integrating signals of short duration.Our studies suggest principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous motif in biochemical systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Jlocasal@bidmc.harvard.edu

ABSTRACT

Background: Signal duration (e.g. the time over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation.

Results: Here we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic, frequency dependent gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties.

Conclusion: We show that multi-staged cascades are effective in integrating signals of long duration whereas multi-staged cascades that operate in the presence of negative feedback are effective in integrating signals of short duration. Our studies suggest principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous motif in biochemical systems.

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Filtering of high frequency signals. Time dependence of signal integration in a linear biochemical cascade. a.) the sequential activation of multiple stages in a signaling cascade. Superscripts (I) and (A) denote inactive and active forms of each chemical species and are dropped from the equations in the text. b.) same kinetic constants, all kinetic constants are taken to be:  c.) a positive gradient of activation/deactivation rates keeping  fixed.  = 1.0,  = 3.3,  = 6.6,  = 10.0. c.) plots of gn(ω) for n = 1, 2, 3, 4 with successively different values of  while keeping  fixed ( = 1.0,  = 3.3,  = 6.6,  = 10.0).
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Figure 2: Filtering of high frequency signals. Time dependence of signal integration in a linear biochemical cascade. a.) the sequential activation of multiple stages in a signaling cascade. Superscripts (I) and (A) denote inactive and active forms of each chemical species and are dropped from the equations in the text. b.) same kinetic constants, all kinetic constants are taken to be: c.) a positive gradient of activation/deactivation rates keeping fixed. = 1.0, = 3.3, = 6.6, = 10.0. c.) plots of gn(ω) for n = 1, 2, 3, 4 with successively different values of while keeping fixed ( = 1.0, = 3.3, = 6.6, = 10.0).

Mentions: where the first species is activated at a rate f(t); and . This scheme is depicted in Fig. 2a.


Signal duration and the time scale dependence of signal integration in biochemical pathways.

Locasale JW - BMC Syst Biol (2008)

Filtering of high frequency signals. Time dependence of signal integration in a linear biochemical cascade. a.) the sequential activation of multiple stages in a signaling cascade. Superscripts (I) and (A) denote inactive and active forms of each chemical species and are dropped from the equations in the text. b.) same kinetic constants, all kinetic constants are taken to be:  c.) a positive gradient of activation/deactivation rates keeping  fixed.  = 1.0,  = 3.3,  = 6.6,  = 10.0. c.) plots of gn(ω) for n = 1, 2, 3, 4 with successively different values of  while keeping  fixed ( = 1.0,  = 3.3,  = 6.6,  = 10.0).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2663553&req=5

Figure 2: Filtering of high frequency signals. Time dependence of signal integration in a linear biochemical cascade. a.) the sequential activation of multiple stages in a signaling cascade. Superscripts (I) and (A) denote inactive and active forms of each chemical species and are dropped from the equations in the text. b.) same kinetic constants, all kinetic constants are taken to be: c.) a positive gradient of activation/deactivation rates keeping fixed. = 1.0, = 3.3, = 6.6, = 10.0. c.) plots of gn(ω) for n = 1, 2, 3, 4 with successively different values of while keeping fixed ( = 1.0, = 3.3, = 6.6, = 10.0).
Mentions: where the first species is activated at a rate f(t); and . This scheme is depicted in Fig. 2a.

Bottom Line: We compute the dynamic, frequency dependent gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales.We show that multi-staged cascades are effective in integrating signals of long duration whereas multi-staged cascades that operate in the presence of negative feedback are effective in integrating signals of short duration.Our studies suggest principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous motif in biochemical systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Jlocasal@bidmc.harvard.edu

ABSTRACT

Background: Signal duration (e.g. the time over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation.

Results: Here we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic, frequency dependent gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties.

Conclusion: We show that multi-staged cascades are effective in integrating signals of long duration whereas multi-staged cascades that operate in the presence of negative feedback are effective in integrating signals of short duration. Our studies suggest principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous motif in biochemical systems.

Show MeSH
Related in: MedlinePlus