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Keeping up with the 'omics: non-equilibrium models of gene regulation.

Pincus D - BMC Biol. (2015)

Bottom Line: Despite this universally accepted fact, gene regulation is typically formalized into models that assume thermodynamic equilibrium.As experimental evidence expands the repertoire of non-equilibrium genome regulatory mechanisms, theoreticians are challenged to devise general approaches to accommodate and suggest functions for non-equilibrium processes.Ahsendorf et al. provide one such framework, which is discussed in the context of the growing complexity of eukaryotic gene regulation.

View Article: PubMed Central - PubMed

ABSTRACT
Non-equilibrium processes are vital features of biological systems. Despite this universally accepted fact, gene regulation is typically formalized into models that assume thermodynamic equilibrium. As experimental evidence expands the repertoire of non-equilibrium genome regulatory mechanisms, theoreticians are challenged to devise general approaches to accommodate and suggest functions for non-equilibrium processes. Ahsendorf et al. provide one such framework, which is discussed in the context of the growing complexity of eukaryotic gene regulation.

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Related in: MedlinePlus

Representing arbitrarily complex gene regulatory mechanisms with a graph-based framework. (A) Cartoon schematic for a simple transcriptional regulatory mechanism involving equilibrium (binding and dissociation) and non-equilibrium energy consuming (phosphorylation) steps. Each step is a particular molecular arrangement that can be thought of as a ‘microstate’ which has a certain probability of being occupied based on the interactions, rates and concentrations of the molecules. (B) Graph depiction of the cartoon gene regulatory mechanism. Each node represents a microstate, the edges the interactions/reactions, and the edge labels are the rates.
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Fig2: Representing arbitrarily complex gene regulatory mechanisms with a graph-based framework. (A) Cartoon schematic for a simple transcriptional regulatory mechanism involving equilibrium (binding and dissociation) and non-equilibrium energy consuming (phosphorylation) steps. Each step is a particular molecular arrangement that can be thought of as a ‘microstate’ which has a certain probability of being occupied based on the interactions, rates and concentrations of the molecules. (B) Graph depiction of the cartoon gene regulatory mechanism. Each node represents a microstate, the edges the interactions/reactions, and the edge labels are the rates.

Mentions: In this issue of BMC Biology, Ahsendorf et al. [1] describe a general framework to incorporate non-equilibrium events into models of gene regulation. As suggested by the word framework, the approach outlined in the paper is not a computational model that comes as a package of MatLab or Python code. Rather, it is a way of formally abstracting gene regulation as a graph. A graph consists of nodes and edges: the nodes represent ‘microstates’ and the edges represent transitions between microstates. Microstates, in the case of gene regulation, are particular molecular arrangements that can be thought of as discrete steps in the process. For example, imagine the case where there is a transcription factor (TF) that binds to a promoter and must be phosphorylated to be able to recruit RNA polymerase to initiate transcription (Figure 2). In this example, there are four microstates related to the promoter: 1) naked promoter, 2) TF bound to the promoter, 3) phosphorylated TF bound to the promoter and 4) phosphorylated TF bound to the promoter with RNA polymerase. Each of these microstates is a node in the graph; the edges that connect them are the reversible interactions (binding and dissociation) and irreversible reactions (phosphorylation and transcriptional activation) that drive the transcriptional process; and the labels of the edges are the on, off and reaction rates. As the authors show, the probability that each microstate is occupied can then be calculated over time using standard differential equations and linear algebra without any requirement for thermodynamic equilibrium.Figure 2


Keeping up with the 'omics: non-equilibrium models of gene regulation.

Pincus D - BMC Biol. (2015)

Representing arbitrarily complex gene regulatory mechanisms with a graph-based framework. (A) Cartoon schematic for a simple transcriptional regulatory mechanism involving equilibrium (binding and dissociation) and non-equilibrium energy consuming (phosphorylation) steps. Each step is a particular molecular arrangement that can be thought of as a ‘microstate’ which has a certain probability of being occupied based on the interactions, rates and concentrations of the molecules. (B) Graph depiction of the cartoon gene regulatory mechanism. Each node represents a microstate, the edges the interactions/reactions, and the edge labels are the rates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4321706&req=5

Fig2: Representing arbitrarily complex gene regulatory mechanisms with a graph-based framework. (A) Cartoon schematic for a simple transcriptional regulatory mechanism involving equilibrium (binding and dissociation) and non-equilibrium energy consuming (phosphorylation) steps. Each step is a particular molecular arrangement that can be thought of as a ‘microstate’ which has a certain probability of being occupied based on the interactions, rates and concentrations of the molecules. (B) Graph depiction of the cartoon gene regulatory mechanism. Each node represents a microstate, the edges the interactions/reactions, and the edge labels are the rates.
Mentions: In this issue of BMC Biology, Ahsendorf et al. [1] describe a general framework to incorporate non-equilibrium events into models of gene regulation. As suggested by the word framework, the approach outlined in the paper is not a computational model that comes as a package of MatLab or Python code. Rather, it is a way of formally abstracting gene regulation as a graph. A graph consists of nodes and edges: the nodes represent ‘microstates’ and the edges represent transitions between microstates. Microstates, in the case of gene regulation, are particular molecular arrangements that can be thought of as discrete steps in the process. For example, imagine the case where there is a transcription factor (TF) that binds to a promoter and must be phosphorylated to be able to recruit RNA polymerase to initiate transcription (Figure 2). In this example, there are four microstates related to the promoter: 1) naked promoter, 2) TF bound to the promoter, 3) phosphorylated TF bound to the promoter and 4) phosphorylated TF bound to the promoter with RNA polymerase. Each of these microstates is a node in the graph; the edges that connect them are the reversible interactions (binding and dissociation) and irreversible reactions (phosphorylation and transcriptional activation) that drive the transcriptional process; and the labels of the edges are the on, off and reaction rates. As the authors show, the probability that each microstate is occupied can then be calculated over time using standard differential equations and linear algebra without any requirement for thermodynamic equilibrium.Figure 2

Bottom Line: Despite this universally accepted fact, gene regulation is typically formalized into models that assume thermodynamic equilibrium.As experimental evidence expands the repertoire of non-equilibrium genome regulatory mechanisms, theoreticians are challenged to devise general approaches to accommodate and suggest functions for non-equilibrium processes.Ahsendorf et al. provide one such framework, which is discussed in the context of the growing complexity of eukaryotic gene regulation.

View Article: PubMed Central - PubMed

ABSTRACT
Non-equilibrium processes are vital features of biological systems. Despite this universally accepted fact, gene regulation is typically formalized into models that assume thermodynamic equilibrium. As experimental evidence expands the repertoire of non-equilibrium genome regulatory mechanisms, theoreticians are challenged to devise general approaches to accommodate and suggest functions for non-equilibrium processes. Ahsendorf et al. provide one such framework, which is discussed in the context of the growing complexity of eukaryotic gene regulation.

Show MeSH
Related in: MedlinePlus