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Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster.

Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens H, Banga JR, Jaeger J - PLoS Comput. Biol. (2013)

Bottom Line: Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels.Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation.This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

View Article: PubMed Central - PubMed

Affiliation: EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain ; Institute of Genetics, Johannes Gutenberg University, Mainz, Germany.

ABSTRACT
Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

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Related in: MedlinePlus

Parameter correlations.This figure shows correlation matrices for parameter values derived from linear analysis (A), and bootstrapping (B), for Kr (green frame), kni (red frame), and gt (blue frame). Parameter notation:  (production rate),  (decay rate),  (diffusion rate), and  (production delay; see equation 1). Colors indicate sign and strength of correlations. Matrices in (A) are calculated from equation 6 (see Materials and Methods). Matrices in (B) are derived from the singular value decomposition of bootstrap distributions.
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pcbi-1003281-g005: Parameter correlations.This figure shows correlation matrices for parameter values derived from linear analysis (A), and bootstrapping (B), for Kr (green frame), kni (red frame), and gt (blue frame). Parameter notation: (production rate), (decay rate), (diffusion rate), and (production delay; see equation 1). Colors indicate sign and strength of correlations. Matrices in (A) are calculated from equation 6 (see Materials and Methods). Matrices in (B) are derived from the singular value decomposition of bootstrap distributions.

Mentions: Correlation coefficients between parameters can be calculated from the covariance matrix (Figure 5A; see equation 6 in Materials and Methods). In all three models, correlation is high between and . This is expected since high decay rates can compensate for high production rates. Both of these parameters are also correlated to the delays given by . These correlations are highest for gt, and still very substantial for both Kr and kni. Again, this is to be expected since production delay can be mimicked to some degree by low production rates. In contrast, we found that diffusion rates are largely independent of other model parameters, except for a slight negative correlation between and , and between and . This could be due to the extremely low values of diffusion rates in all of our models, or due to the fact that diffusion affects spatial, rather than strictly local, regulatory mechanisms, which could explain the increased degree of decoupling between the two processes.


Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster.

Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens H, Banga JR, Jaeger J - PLoS Comput. Biol. (2013)

Parameter correlations.This figure shows correlation matrices for parameter values derived from linear analysis (A), and bootstrapping (B), for Kr (green frame), kni (red frame), and gt (blue frame). Parameter notation:  (production rate),  (decay rate),  (diffusion rate), and  (production delay; see equation 1). Colors indicate sign and strength of correlations. Matrices in (A) are calculated from equation 6 (see Materials and Methods). Matrices in (B) are derived from the singular value decomposition of bootstrap distributions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814631&req=5

pcbi-1003281-g005: Parameter correlations.This figure shows correlation matrices for parameter values derived from linear analysis (A), and bootstrapping (B), for Kr (green frame), kni (red frame), and gt (blue frame). Parameter notation: (production rate), (decay rate), (diffusion rate), and (production delay; see equation 1). Colors indicate sign and strength of correlations. Matrices in (A) are calculated from equation 6 (see Materials and Methods). Matrices in (B) are derived from the singular value decomposition of bootstrap distributions.
Mentions: Correlation coefficients between parameters can be calculated from the covariance matrix (Figure 5A; see equation 6 in Materials and Methods). In all three models, correlation is high between and . This is expected since high decay rates can compensate for high production rates. Both of these parameters are also correlated to the delays given by . These correlations are highest for gt, and still very substantial for both Kr and kni. Again, this is to be expected since production delay can be mimicked to some degree by low production rates. In contrast, we found that diffusion rates are largely independent of other model parameters, except for a slight negative correlation between and , and between and . This could be due to the extremely low values of diffusion rates in all of our models, or due to the fact that diffusion affects spatial, rather than strictly local, regulatory mechanisms, which could explain the increased degree of decoupling between the two processes.

Bottom Line: Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels.Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation.This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

View Article: PubMed Central - PubMed

Affiliation: EMBL/CRG Research Unit in Systems Biology, Centre de Regulació Genòmica, and Universitat Pombeu Fabra (UPF), Barcelona, Spain ; Institute of Genetics, Johannes Gutenberg University, Mainz, Germany.

ABSTRACT
Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

Show MeSH
Related in: MedlinePlus