Limits...
From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits.

Shao B, Liu X, Zhang D, Wu J, Ouyang Q - PLoS ONE (2015)

Bottom Line: In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model.Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions.Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

View Article: PubMed Central - PubMed

Affiliation: The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China; The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.

ABSTRACT
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

No MeSH data available.


The SOS network and its dynamics.(A) Regulatory network of the SOS response of E. coli. The nodes represent the signal and the essential proteins. The green lines represent activation and the red lines represent inhibition. (B) Dynamics of the SOS response in the Boolean network model. (C) Three criteria and their representations in the discrete and continuous model. The first criteria addresses degradation time of ssDNA, i.e., ssDNA should be down-regulated to zero at the end of the simulation. In the second criteria, the final state of the system should return to the initial state, except for the degradation of ssDNA. The third criteria requires that the dynamics of each node in the ODE model should be in accordance with those in Boolean trajectory. (D) Example of a successful response in the ODE model.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4464762&req=5

pone.0128630.g001: The SOS network and its dynamics.(A) Regulatory network of the SOS response of E. coli. The nodes represent the signal and the essential proteins. The green lines represent activation and the red lines represent inhibition. (B) Dynamics of the SOS response in the Boolean network model. (C) Three criteria and their representations in the discrete and continuous model. The first criteria addresses degradation time of ssDNA, i.e., ssDNA should be down-regulated to zero at the end of the simulation. In the second criteria, the final state of the system should return to the initial state, except for the degradation of ssDNA. The third criteria requires that the dynamics of each node in the ODE model should be in accordance with those in Boolean trajectory. (D) Example of a successful response in the ODE model.

Mentions: In this paper, we present a two-step method that combines the discrete model and the continuous model to generate a novel design of functional circuits. In our approach, first, a Boolean network model is applied to generate candidate networks that are better capable of executing the target functions. Then, continuous simulation is used to quantitatively assess the robustness of these candidate networks. Here, we focus on one critical biological behavior, the SOS response in E. coli., wherein DNA repair is induced in response to the existence of a single stranded DNA (ssDNA). The desired function of this network is as follows: Upon accumulation of ssDNA, RecA is recruited to the single stranded regions of DNA and becomes activated. Activation of RecA releases the inhibition of SOS genes by facilitating self-cleavage of their repressor LexA. The main activator of SOS gene is σ70, which belongs to a family of transcription initiation factors responsible for stress response. Its downstream genes can be simplified into two genes, SSB and UmuDC, which are responsible for the repair of DNA damage and inhibition of RecA. When the DNA repair is completed, LexA is activated and the expression of SOS genes is down-regulated [19, 20]. The natural network performing this function is presented in Fig 1A. This DNA damage response may represent a large class of response pathways and we use our approach to design functional circuits underpinning this function. The analysis of functional circuits obtained by our approach allows us to discover core motifs responsible for robust response.


From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits.

Shao B, Liu X, Zhang D, Wu J, Ouyang Q - PLoS ONE (2015)

The SOS network and its dynamics.(A) Regulatory network of the SOS response of E. coli. The nodes represent the signal and the essential proteins. The green lines represent activation and the red lines represent inhibition. (B) Dynamics of the SOS response in the Boolean network model. (C) Three criteria and their representations in the discrete and continuous model. The first criteria addresses degradation time of ssDNA, i.e., ssDNA should be down-regulated to zero at the end of the simulation. In the second criteria, the final state of the system should return to the initial state, except for the degradation of ssDNA. The third criteria requires that the dynamics of each node in the ODE model should be in accordance with those in Boolean trajectory. (D) Example of a successful response in the ODE model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4464762&req=5

pone.0128630.g001: The SOS network and its dynamics.(A) Regulatory network of the SOS response of E. coli. The nodes represent the signal and the essential proteins. The green lines represent activation and the red lines represent inhibition. (B) Dynamics of the SOS response in the Boolean network model. (C) Three criteria and their representations in the discrete and continuous model. The first criteria addresses degradation time of ssDNA, i.e., ssDNA should be down-regulated to zero at the end of the simulation. In the second criteria, the final state of the system should return to the initial state, except for the degradation of ssDNA. The third criteria requires that the dynamics of each node in the ODE model should be in accordance with those in Boolean trajectory. (D) Example of a successful response in the ODE model.
Mentions: In this paper, we present a two-step method that combines the discrete model and the continuous model to generate a novel design of functional circuits. In our approach, first, a Boolean network model is applied to generate candidate networks that are better capable of executing the target functions. Then, continuous simulation is used to quantitatively assess the robustness of these candidate networks. Here, we focus on one critical biological behavior, the SOS response in E. coli., wherein DNA repair is induced in response to the existence of a single stranded DNA (ssDNA). The desired function of this network is as follows: Upon accumulation of ssDNA, RecA is recruited to the single stranded regions of DNA and becomes activated. Activation of RecA releases the inhibition of SOS genes by facilitating self-cleavage of their repressor LexA. The main activator of SOS gene is σ70, which belongs to a family of transcription initiation factors responsible for stress response. Its downstream genes can be simplified into two genes, SSB and UmuDC, which are responsible for the repair of DNA damage and inhibition of RecA. When the DNA repair is completed, LexA is activated and the expression of SOS genes is down-regulated [19, 20]. The natural network performing this function is presented in Fig 1A. This DNA damage response may represent a large class of response pathways and we use our approach to design functional circuits underpinning this function. The analysis of functional circuits obtained by our approach allows us to discover core motifs responsible for robust response.

Bottom Line: In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model.Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions.Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

View Article: PubMed Central - PubMed

Affiliation: The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China; The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.

ABSTRACT
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

No MeSH data available.