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Cited by 267Gene Regulatory Network AnalysisBioinformatics and Genomic NetworksMicrobial Metabolic Engineering and Bioproduction

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Network motifs: Structure does not determine function.

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Ingram, Piers J, Stumpf, Michael PH, Stark, Jaroslav·2006·BMC Genomics
DOI 10.1186/1471-2164-7-108PMC PMC1488845
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Abstract

Background: A number of publications have recently examined the occurrence and properties of the feed-forward motif in a variety of networks, including those that are of interest in genome biology, such as gene networks. The present work looks in some detail at the dynamics of the bi-fan motif, using systems of ordinary differential equations to model the populations of transcription factors, mRNA and protein, with the aim of extending our understanding of what appear to be important building blocks of gene network structure. Results: We develop an ordinary differential equation model of the bi-fan motif and analyse variants of the motif corresponding to its behaviour under various conditions. In particular, we examine the effects of different steady and pulsed inputs to five variants of the bifan motif, based on evidence in the literature of bifan motifs found in Saccharomyces cerevisiae (commonly known as baker's yeast). Using this model, we characterize the dynamical behaviour of the bi-fan motif for a wide range of biologically plausible parameters and configurations. We find that there is no characteristic behaviour for the motif, and with the correct choice of parameters and of internal structure, very different, indeed even opposite behaviours may be obtained. Conclusion: Even with this relatively simple model, the bi-fan motif can exhibit a wide range of dynamical responses. This suggests that it is difficult to gain significant insights into biological function simply by considering the connection architecture of a gene network, or its decomposition into simple structural motifs. It is necessary to supplement such structural information by kinetic parameters, or dynamic time series experimental data, both of which are currently difficult to obtain.

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Figure 1 Common motifs in transcription regulation networks, (a) the feed-forward motif, in which the product of gene X regulates both genes Y and Z, whilst Y also regulates gene Z, (b) the bi-fan motif, in which the products of genes X and Y regulate both genes Z and W.
Figure 1 Common motifs in transcription regulation networks, (a) the feed-forward motif, in which the product of gene X regulates both genes Y and Z, whilst Y also regulates gene Z, (b) the bi-fan motif, in which the products of genes X and Y regulate both genes Z and W.CC BY · PubMed Central
Figure 2 Examplars of bi-fan behaviour observed in the TRANSFAC database for S. cerevisiae. Inhibitory interactions are shown in red, while promoting interactions are shown in green.
Figure 2 Examplars of bi-fan behaviour observed in the TRANSFAC database for S. cerevisiae. Inhibitory interactions are shown in red, while promoting interactions are shown in green.CC BY · PubMed Central
Figure 3 This table shows the variation in the outputs of the four different motifs identified in the literature for a variety of steady inputs. The details of the model used can be found in the methods section, and the values of the parameters used are in Table 2. In the input column, the motif is first exposed to two high inputs, IX and IY take the value of 100. This is indicated as two green squares. The outputs of the two second tier proteins are then measured after the system has attained equilibrium (after 10,000 seconds), and the outputs are characterized as High, Low or Off, denoted by Green, Yellow or Red squares respectively. On the second row the inputs are changed to be IX high and IY low (values used were 100 and 10 respectively). In the final two rows the situation is considered in which one of the inputs is turned off (denoted by a red square). What is of most interest is the variation in the pattern of outputs along each row, indicating that the characteristic output of the motif varies qualitatively according to the detailed structure, in addition to the quantitative variation in the outputs.
Figure 3 This table shows the variation in the outputs of the four different motifs identified in the literature for a variety of steady inputs. The details of the model used can be found in the methods section, and the values of the parameters used are in Table 2. In the input column, the motif is first exposed to two high inputs, IX and IY take the value of 100. This is indicated as two green squares. The outputs of the two second tier proteins are then measured after the system has attained equilibrium (after 10,000 seconds), and the outputs are characterized as High, Low or Off, denoted by Green, Yellow or Red squares respectively. On the second row the inputs are changed to be IX high and IY low (values used were 100 and 10 respectively). In the final two rows the situation is considered in which one of the inputs is turned off (denoted by a red square). What is of most interest is the variation in the pattern of outputs along each row, indicating that the characteristic output of the motif varies qualitatively according to the detailed structure, in addition to the quantitative variation in the outputs.CC BY · PubMed Central
Figure 4 This table shows the variation in the outputs of the different motifs when both inputs are turned on to a high level for 3600 seconds and then abruptly turned off. This is denoted in the inputs as green then red. The outputs are then categorised and colour coded as either off, low or high, corresponding to red, yellow or green. This scenario corresponds to relatively short term exposure to a transcription factor up the transcription network. Again, the details of the model can be found in the methods section, and the parameters used are given in Table 2
Figure 4 This table shows the variation in the outputs of the different motifs when both inputs are turned on to a high level for 3600 seconds and then abruptly turned off. This is denoted in the inputs as green then red. The outputs are then categorised and colour coded as either off, low or high, corresponding to red, yellow or green. This scenario corresponds to relatively short term exposure to a transcription factor up the transcription network. Again, the details of the model can be found in the methods section, and the parameters used are given in Table 2CC BY · PubMed Central
Figure 5 In these two plots we again see the dynamical response of motifs A and B when the motif is excited by simultaneous step functions for 3600 seconds. The two plots share their y-axes, with the scale for the response on the leftmost axis, and the strength of the input signal on the far right axis. Note not only the difference between the two in peak expression, but also the marked difference in total expression, time of peak expression, and behaviour after the peak between the two motifs. The curves in (a) have been separated to enhance readability. To produce the simultaneous steps, both IX and IY are initially at 0 at t = 0, are then set to 100 for 3600 seconds, then again returned to 0. The kinetic parameters used are given in Table 2.
Figure 5 In these two plots we again see the dynamical response of motifs A and B when the motif is excited by simultaneous step functions for 3600 seconds. The two plots share their y-axes, with the scale for the response on the leftmost axis, and the strength of the input signal on the far right axis. Note not only the difference between the two in peak expression, but also the marked difference in total expression, time of peak expression, and behaviour after the peak between the two motifs. The curves in (a) have been separated to enhance readability. To produce the simultaneous steps, both IX and IY are initially at 0 at t = 0, are then set to 100 for 3600 seconds, then again returned to 0. The kinetic parameters used are given in Table 2.CC BY · PubMed Central
Figure 6 In this table we can see the responses of the different motifs to successive inputs. The first two rows show the effect of first perturbing IX and then IY with a step function. In the third and fourth rows this is reversed. Again the main feature to notice in this table is the variety in the responses of the motifs modelled. For the purposes of this diagram there were assumed to be two phases of behaviour corresponding to "after" the first perturbation and "after" the second perturbation. The strength of the response is again indicated by Red, Yellow and Green, corresponding to Off, Low or High response. As in the previous cases, the details of the model can be found in the methods section, and the parameters used are given in Table 2.
Figure 6 In this table we can see the responses of the different motifs to successive inputs. The first two rows show the effect of first perturbing IX and then IY with a step function. In the third and fourth rows this is reversed. Again the main feature to notice in this table is the variety in the responses of the motifs modelled. For the purposes of this diagram there were assumed to be two phases of behaviour corresponding to "after" the first perturbation and "after" the second perturbation. The strength of the response is again indicated by Red, Yellow and Green, corresponding to Off, Low or High response. As in the previous cases, the details of the model can be found in the methods section, and the parameters used are given in Table 2.CC BY · PubMed Central
Figure 7 Here we see again the details of the dynamical response of motifs A and B to the inputs. Again, the two plots share y-axes, with the response scale on the left, and input signal strength of the far right axis. In this case, two sucessive step functions of 3600 seconds were used to model activation by first one transcription factor and then the other immediately after. In these figures, IX is increased from 0 to 100 for 3600 seconds, and then at t = 3600, IY is set to 100 for 3600 seconds and IX is turned off. Again the kinetic parameters used in the model can be found in Table 2. Note the significant differences not only in the absolute values of peak expression, but in the shape of the curves.
Figure 7 Here we see again the details of the dynamical response of motifs A and B to the inputs. Again, the two plots share y-axes, with the response scale on the left, and input signal strength of the far right axis. In this case, two sucessive step functions of 3600 seconds were used to model activation by first one transcription factor and then the other immediately after. In these figures, IX is increased from 0 to 100 for 3600 seconds, and then at t = 3600, IY is set to 100 for 3600 seconds and IX is turned off. Again the kinetic parameters used in the model can be found in Table 2. Note the significant differences not only in the absolute values of peak expression, but in the shape of the curves.CC BY · PubMed Central
Figure 8 We see the effect of assuming that the production rate of PW is sensitive to the order of binding of PX and PY to the DNA of gene Z, see text for details. As before, the y-axes are shared. The two expression profiles for PW correspond to inverting the inputs, showing that the motif may able to differentiate between signal combinations. IX and IY are again modelled as step functions which switch to 0 after 3600 seconds.
Figure 8 We see the effect of assuming that the production rate of PW is sensitive to the order of binding of PX and PY to the DNA of gene Z, see text for details. As before, the y-axes are shared. The two expression profiles for PW correspond to inverting the inputs, showing that the motif may able to differentiate between signal combinations. IX and IY are again modelled as step functions which switch to 0 after 3600 seconds.CC BY · PubMed Central

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Metadata

Authors
Ingram, P. J., Stumpf, M. P., & Stark, J.
Affiliations
Imperial College London (GB)
Corresponding author
Ingram, Piers J
ORCID
Stumpf, Michael PH
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2006-05-05
Volume
7
Issue
1
Pages
108-108
ISSN
1471-2164 (ISSN-L)
DOI
10.1186/1471-2164-7-108
PMC
PMC1488845
OpenAlex
W1808967201
Cited by
267
References
42
Citations by year
2026: 13 · 2025: 5 · 2024: 8 · 2023: 5 · 2022: 4 · 2021: 13 · 2020: 10 · 2019: 11 · 2018: 16 · 2017: 9 · 2016: 20 · 2015: 12 · 2014: 19 · 2013: 29 · 2012: 15
FWCI
6.53
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Genomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
Wellcome Trust
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
network motifs, gene regulatory networks, transcription factors, ordinary differential equation model, network dynamics, Saccharomyces cerevisiae, kinetic parameters, structure-function relationship
MeSH terms
Genes, Fungal, Models, Biological, Saccharomyces cerevisiae, Transcription, Genetic, Gene Expression Regulation, Fungal, Computational Biology, Amino Acid Motifs

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