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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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The systems biology modeling cycle.This cycle illustrates the interplay of experiment and modeling in modern systems biology (adapted from [26]). Expression data are acquired and quantified. A model is formulated based on a regulatory hypothesis intended to explain the observed expression patterns. The model is solved and fit to data (reverse engineering). Model output and parameter values are then analysed to yield predictions and interpretations of the biological data. If necessary, the process is repeated—acquiring new data and improving the model—until a satisfactory explanation of the observed phenomena is achieved. Model fits are shown on the left. The panel describing the model depicts the processes of protein production, diffusion, and decay within and between nuclei (energids; lower panel). The upper panel shows the mitotic schedule (M: mitosis, red; otherwise: interphase, blue background), with those time points indicated for which we have data. See text for details.
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pcbi-1003281-g001: The systems biology modeling cycle.This cycle illustrates the interplay of experiment and modeling in modern systems biology (adapted from [26]). Expression data are acquired and quantified. A model is formulated based on a regulatory hypothesis intended to explain the observed expression patterns. The model is solved and fit to data (reverse engineering). Model output and parameter values are then analysed to yield predictions and interpretations of the biological data. If necessary, the process is repeated—acquiring new data and improving the model—until a satisfactory explanation of the observed phenomena is achieved. Model fits are shown on the left. The panel describing the model depicts the processes of protein production, diffusion, and decay within and between nuclei (energids; lower panel). The upper panel shows the mitotic schedule (M: mitosis, red; otherwise: interphase, blue background), with those time points indicated for which we have data. See text for details.

Mentions: Systems biology is characterised by the tight integration of experiments and computational modeling. One way to achieve such integration is through reverse-engineering approaches, where dynamical models of regulatory or biochemical reaction networks are fit to quantitative data [1]–[9]. Reverse engineering has been successfully used for systems analysis in many contexts, from microbial metabolic, signaling and regulatory networks (see, for example, [10]–[20]) to pattern-forming developmental processes in animals (e.g. [21]–[25]). The approach is illustrated by the systems biology modeling cycle shown in Figure 1 (adapted from [26]). As a first step, a mathematical model is formulated that incorporates the basic assumptions and hypotheses we have about the regulatory process under study. The model is then tested by fitting it to metabolic or expression data. This is achieved by repeatedly altering its parameters and selecting suitable (mainly better-fitting) solutions. A successful fit will yield a unique set of parameter estimates that cause the model to reproduce the data accurately. In this case, model output and estimated parameter values can be analysed to gain biological insight. For instance, regulatory parameters contain information on the strength and type of regulatory interactions in a network. If the model fails to produce a unique solution—predicting a large set of variant networks instead—it is underdetermined and more data need to be collected. If the model cannot fit the data, the underlying hypothesis needs to be adjusted, or additional mechanisms and factors need to be incorporated. Successive model-fitting/data-acquisition cycles yield an increasingly accurate quantitative picture of the underlying regulatory network.


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)

The systems biology modeling cycle.This cycle illustrates the interplay of experiment and modeling in modern systems biology (adapted from [26]). Expression data are acquired and quantified. A model is formulated based on a regulatory hypothesis intended to explain the observed expression patterns. The model is solved and fit to data (reverse engineering). Model output and parameter values are then analysed to yield predictions and interpretations of the biological data. If necessary, the process is repeated—acquiring new data and improving the model—until a satisfactory explanation of the observed phenomena is achieved. Model fits are shown on the left. The panel describing the model depicts the processes of protein production, diffusion, and decay within and between nuclei (energids; lower panel). The upper panel shows the mitotic schedule (M: mitosis, red; otherwise: interphase, blue background), with those time points indicated for which we have data. See text for details.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003281-g001: The systems biology modeling cycle.This cycle illustrates the interplay of experiment and modeling in modern systems biology (adapted from [26]). Expression data are acquired and quantified. A model is formulated based on a regulatory hypothesis intended to explain the observed expression patterns. The model is solved and fit to data (reverse engineering). Model output and parameter values are then analysed to yield predictions and interpretations of the biological data. If necessary, the process is repeated—acquiring new data and improving the model—until a satisfactory explanation of the observed phenomena is achieved. Model fits are shown on the left. The panel describing the model depicts the processes of protein production, diffusion, and decay within and between nuclei (energids; lower panel). The upper panel shows the mitotic schedule (M: mitosis, red; otherwise: interphase, blue background), with those time points indicated for which we have data. See text for details.
Mentions: Systems biology is characterised by the tight integration of experiments and computational modeling. One way to achieve such integration is through reverse-engineering approaches, where dynamical models of regulatory or biochemical reaction networks are fit to quantitative data [1]–[9]. Reverse engineering has been successfully used for systems analysis in many contexts, from microbial metabolic, signaling and regulatory networks (see, for example, [10]–[20]) to pattern-forming developmental processes in animals (e.g. [21]–[25]). The approach is illustrated by the systems biology modeling cycle shown in Figure 1 (adapted from [26]). As a first step, a mathematical model is formulated that incorporates the basic assumptions and hypotheses we have about the regulatory process under study. The model is then tested by fitting it to metabolic or expression data. This is achieved by repeatedly altering its parameters and selecting suitable (mainly better-fitting) solutions. A successful fit will yield a unique set of parameter estimates that cause the model to reproduce the data accurately. In this case, model output and estimated parameter values can be analysed to gain biological insight. For instance, regulatory parameters contain information on the strength and type of regulatory interactions in a network. If the model fails to produce a unique solution—predicting a large set of variant networks instead—it is underdetermined and more data need to be collected. If the model cannot fit the data, the underlying hypothesis needs to be adjusted, or additional mechanisms and factors need to be incorporated. Successive model-fitting/data-acquisition cycles yield an increasingly accurate quantitative picture of the underlying regulatory network.

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