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Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems.

Lomnitz JG, Savageau MA - Front Genet (2016)

Bottom Line: We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy.The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach.In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of California, Davis Davis, CA, USA.

ABSTRACT
Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.

No MeSH data available.


Conceptual model for the design of a synthetic gene circuit with 2-, 3-, and 4-state memory. (A) A cartoon of the proposed design for a gene circuit with two autogenously regulated activators, each similar to that in Figure 1. The first is represented in green with a purple dimerization domain and the second is represented in blue with a yellow dimerization domain. Homodimerization of each leads to the active form of the regulator. A repressor, represented by the red capsule, sterically hinders the binding of each activator. (B) Binding of monomers from each of the two activators through complementary dimerization domains leads to a heterodimer that is rapidly degraded by cellular proteases or other machinery. (C) Abstract representation of the synthetic construct. The two activators X1, green in the cartoon, and X2, blue in the cartoon, heterodimerise to create a complex that is degraded, each activates its own expression by binding to target DNA, and this binding is sterically hindered by the common repressor X3, red in the cartoon.
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Figure 4: Conceptual model for the design of a synthetic gene circuit with 2-, 3-, and 4-state memory. (A) A cartoon of the proposed design for a gene circuit with two autogenously regulated activators, each similar to that in Figure 1. The first is represented in green with a purple dimerization domain and the second is represented in blue with a yellow dimerization domain. Homodimerization of each leads to the active form of the regulator. A repressor, represented by the red capsule, sterically hinders the binding of each activator. (B) Binding of monomers from each of the two activators through complementary dimerization domains leads to a heterodimer that is rapidly degraded by cellular proteases or other machinery. (C) Abstract representation of the synthetic construct. The two activators X1, green in the cartoon, and X2, blue in the cartoon, heterodimerise to create a complex that is degraded, each activates its own expression by binding to target DNA, and this binding is sterically hindered by the common repressor X3, red in the cartoon.

Mentions: The design of the synthetic gene circuit, represented in Figure 4, is composed of two transcriptional activators, X1 and X2 that autogenously control expression of their own genes; the result is two seemingly independent positive feedback loops. The X1 and X2 regulators are translationally fused with a dimerization domain that causes X1 monomers to form heterodimers with X2 monomers. The X1–X2 dimers are inactive and targeted for degradation by cellular proteases, which results in a strong thermodynamic potential that makes heterodimer formation essentially irreversible. Transcription of the activator genes is repressed by a third regulator, X3, that binds to the upstream region of the gene for both X1 and X2, sterically hindering the auto-activation. The role of this repressor in the system is to tune the behavior of the system. A cartoon of the proposed construct is shown in Figure 4A, and an abstraction of the gene circuit with the key interactions is shown in Figure 4C.


Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems.

Lomnitz JG, Savageau MA - Front Genet (2016)

Conceptual model for the design of a synthetic gene circuit with 2-, 3-, and 4-state memory. (A) A cartoon of the proposed design for a gene circuit with two autogenously regulated activators, each similar to that in Figure 1. The first is represented in green with a purple dimerization domain and the second is represented in blue with a yellow dimerization domain. Homodimerization of each leads to the active form of the regulator. A repressor, represented by the red capsule, sterically hinders the binding of each activator. (B) Binding of monomers from each of the two activators through complementary dimerization domains leads to a heterodimer that is rapidly degraded by cellular proteases or other machinery. (C) Abstract representation of the synthetic construct. The two activators X1, green in the cartoon, and X2, blue in the cartoon, heterodimerise to create a complex that is degraded, each activates its own expression by binding to target DNA, and this binding is sterically hindered by the common repressor X3, red in the cartoon.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4940394&req=5

Figure 4: Conceptual model for the design of a synthetic gene circuit with 2-, 3-, and 4-state memory. (A) A cartoon of the proposed design for a gene circuit with two autogenously regulated activators, each similar to that in Figure 1. The first is represented in green with a purple dimerization domain and the second is represented in blue with a yellow dimerization domain. Homodimerization of each leads to the active form of the regulator. A repressor, represented by the red capsule, sterically hinders the binding of each activator. (B) Binding of monomers from each of the two activators through complementary dimerization domains leads to a heterodimer that is rapidly degraded by cellular proteases or other machinery. (C) Abstract representation of the synthetic construct. The two activators X1, green in the cartoon, and X2, blue in the cartoon, heterodimerise to create a complex that is degraded, each activates its own expression by binding to target DNA, and this binding is sterically hindered by the common repressor X3, red in the cartoon.
Mentions: The design of the synthetic gene circuit, represented in Figure 4, is composed of two transcriptional activators, X1 and X2 that autogenously control expression of their own genes; the result is two seemingly independent positive feedback loops. The X1 and X2 regulators are translationally fused with a dimerization domain that causes X1 monomers to form heterodimers with X2 monomers. The X1–X2 dimers are inactive and targeted for degradation by cellular proteases, which results in a strong thermodynamic potential that makes heterodimer formation essentially irreversible. Transcription of the activator genes is repressed by a third regulator, X3, that binds to the upstream region of the gene for both X1 and X2, sterically hindering the auto-activation. The role of this repressor in the system is to tune the behavior of the system. A cartoon of the proposed construct is shown in Figure 4A, and an abstraction of the gene circuit with the key interactions is shown in Figure 4C.

Bottom Line: We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy.The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach.In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of California, Davis Davis, CA, USA.

ABSTRACT
Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.

No MeSH data available.