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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.


Simulation of the counter following stimulation of the positive and negative channels. Simulation of system behavior following a series of transient stimulations at regular intervals of 20 time units (dashed vertical lines). (A) Lines represent the concentrations of the reporter corresponding to the counter X4; (B) the positive channel X1; and (C) the negative channel X2. Transient stimulation of the positive channel, green vertical lines in (B), results in an increase in the counter state, green background in (A). Transient stimulation of the negative channel, red vertical lines in (C), results in a decrease in the counter, red background in (A). Time intervals without stimulation through either channel show that the count is stable, as shown by the white background in (A).
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Figure 8: Simulation of the counter following stimulation of the positive and negative channels. Simulation of system behavior following a series of transient stimulations at regular intervals of 20 time units (dashed vertical lines). (A) Lines represent the concentrations of the reporter corresponding to the counter X4; (B) the positive channel X1; and (C) the negative channel X2. Transient stimulation of the positive channel, green vertical lines in (B), results in an increase in the counter state, green background in (A). Transient stimulation of the negative channel, red vertical lines in (C), results in a decrease in the counter, red background in (A). Time intervals without stimulation through either channel show that the count is stable, as shown by the white background in (A).

Mentions: These traits show that the system has two distinct channels that enable two sequences of transitions between the same three states but in the opposite order. A positive channel for (−, +) → (+, +) → (+, −) and a negative channel for (+, −) → (+, +) → (−, +). By coupling the module with the reporter gene, we show that the system is capable of counting between three levels of reporter concentration and can perform basic arithmetic using values 0, 1, and 2. An example showing a sequence of additions and subtractions following transient addition of X1 and X2, respectively, is shown in Figure 8.


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)

Simulation of the counter following stimulation of the positive and negative channels. Simulation of system behavior following a series of transient stimulations at regular intervals of 20 time units (dashed vertical lines). (A) Lines represent the concentrations of the reporter corresponding to the counter X4; (B) the positive channel X1; and (C) the negative channel X2. Transient stimulation of the positive channel, green vertical lines in (B), results in an increase in the counter state, green background in (A). Transient stimulation of the negative channel, red vertical lines in (C), results in a decrease in the counter, red background in (A). Time intervals without stimulation through either channel show that the count is stable, as shown by the white background in (A).
© Copyright Policy
Related In: Results  -  Collection

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Figure 8: Simulation of the counter following stimulation of the positive and negative channels. Simulation of system behavior following a series of transient stimulations at regular intervals of 20 time units (dashed vertical lines). (A) Lines represent the concentrations of the reporter corresponding to the counter X4; (B) the positive channel X1; and (C) the negative channel X2. Transient stimulation of the positive channel, green vertical lines in (B), results in an increase in the counter state, green background in (A). Transient stimulation of the negative channel, red vertical lines in (C), results in a decrease in the counter, red background in (A). Time intervals without stimulation through either channel show that the count is stable, as shown by the white background in (A).
Mentions: These traits show that the system has two distinct channels that enable two sequences of transitions between the same three states but in the opposite order. A positive channel for (−, +) → (+, +) → (+, −) and a negative channel for (+, −) → (+, +) → (−, +). By coupling the module with the reporter gene, we show that the system is capable of counting between three levels of reporter concentration and can perform basic arithmetic using values 0, 1, and 2. An example showing a sequence of additions and subtractions following transient addition of X1 and X2, respectively, is shown in Figure 8.

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.