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Computational design of digital and memory biological devices.

Rodrigo G, Jaramillo A - Syst Synth Biol (2008)

Bottom Line: Summary.We show how to use an automated procedure to design logic and sequential transcription circuits.This methodology will allow advancing the rational design of biological devices to more complex systems, and we propose the first design of a biological JK-latch memory device.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologia Molecular y Celular de Plantas, CSIC-Universidad Politecnica de Valencia, Valencia, Spain.

ABSTRACT
The use of combinatorial optimization techniques with computational design allows the development of automated methods to design biological systems. Automatic design integrates design principles in an unsupervised algorithm to sample a larger region of the biological network space, at the topology and parameter levels. The design of novel synthetic transcriptional networks with targeted behaviors will be key to understand the design principles underlying biological networks. In this work, we evolve transcriptional networks towards a targeted dynamics, by using a library of promoters and coding sequences, to design a complex biological memory device. The designed sequential transcription network implements a JK-Latch, which is fully predictable and richer than other memory devices. Furthermore, we present designs of transcriptional devices behaving as logic gates, and we show how to create digital behavior from analog promoters. Our procedure allows us to propose a scenario for the evolution of multi-functional genetic networks. In addition, we discuss the decomposability of regulatory networks in terms of genetic modules to develop a given cellular function. Summary. We show how to use an automated procedure to design logic and sequential transcription circuits. This methodology will allow advancing the rational design of biological devices to more complex systems, and we propose the first design of a biological JK-latch memory device.

No MeSH data available.


We plot the scores of circuit II (see Fig. 7) by computing them using AND (solid line), OR (dashed line) and NOR (dotted line) behaviors. We have performed a parameter scan of (a) the transcription–translation rate of gene c (α) taking the activation coefficient of promoter of gene a (K) equal to 1.2 μM, and (b) the activation coefficient of promoter of gene a (K) taking the transcription–translation rate of gene c (α) equal to 10 μM/min. Behavior of the system versus the input concentrations when α is (c) 0.1 (the value of the optimum for AND in (a)), (d) 0.15, and (e) 0.4 μM/min (the value of the optimum for OR in (a)), remaining K constant
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Fig9: We plot the scores of circuit II (see Fig. 7) by computing them using AND (solid line), OR (dashed line) and NOR (dotted line) behaviors. We have performed a parameter scan of (a) the transcription–translation rate of gene c (α) taking the activation coefficient of promoter of gene a (K) equal to 1.2 μM, and (b) the activation coefficient of promoter of gene a (K) taking the transcription–translation rate of gene c (α) equal to 10 μM/min. Behavior of the system versus the input concentrations when α is (c) 0.1 (the value of the optimum for AND in (a)), (d) 0.15, and (e) 0.4 μM/min (the value of the optimum for OR in (a)), remaining K constant

Mentions: On the other hand, as it is difficult to map a given network topology to a function, we have studied the parameter sensitivities for the circuit II from Fig. 7. Figure 9 allows us to show this fact by performing a functional evolution just by changing one kinetic parameter. In Fig. 9a we show how varying the transcription–traslation rate of gene c we could modify the circuit behavior between OR and AND. In this case, the OR state is more robust as we can observe in the figure. In Fig. 9b, the evolution is between OR and NOR playing with the self-activation coefficient of gene a. In this case, we observe that the NOR state is more robust. We could also perform a functional evolution between AND and NOR by modifying the two considered parameters at the same time. Notice that we could then engineer an AND function from the OR device by simply doing directed mutagenesis on the ribosome binding site of gene c, decreasing then its expression rate. Or engineer an NOR function by decreasing the transcription factor affinity of a on its promoter region.Fig. 9


Computational design of digital and memory biological devices.

Rodrigo G, Jaramillo A - Syst Synth Biol (2008)

We plot the scores of circuit II (see Fig. 7) by computing them using AND (solid line), OR (dashed line) and NOR (dotted line) behaviors. We have performed a parameter scan of (a) the transcription–translation rate of gene c (α) taking the activation coefficient of promoter of gene a (K) equal to 1.2 μM, and (b) the activation coefficient of promoter of gene a (K) taking the transcription–translation rate of gene c (α) equal to 10 μM/min. Behavior of the system versus the input concentrations when α is (c) 0.1 (the value of the optimum for AND in (a)), (d) 0.15, and (e) 0.4 μM/min (the value of the optimum for OR in (a)), remaining K constant
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Related In: Results  -  Collection

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Fig9: We plot the scores of circuit II (see Fig. 7) by computing them using AND (solid line), OR (dashed line) and NOR (dotted line) behaviors. We have performed a parameter scan of (a) the transcription–translation rate of gene c (α) taking the activation coefficient of promoter of gene a (K) equal to 1.2 μM, and (b) the activation coefficient of promoter of gene a (K) taking the transcription–translation rate of gene c (α) equal to 10 μM/min. Behavior of the system versus the input concentrations when α is (c) 0.1 (the value of the optimum for AND in (a)), (d) 0.15, and (e) 0.4 μM/min (the value of the optimum for OR in (a)), remaining K constant
Mentions: On the other hand, as it is difficult to map a given network topology to a function, we have studied the parameter sensitivities for the circuit II from Fig. 7. Figure 9 allows us to show this fact by performing a functional evolution just by changing one kinetic parameter. In Fig. 9a we show how varying the transcription–traslation rate of gene c we could modify the circuit behavior between OR and AND. In this case, the OR state is more robust as we can observe in the figure. In Fig. 9b, the evolution is between OR and NOR playing with the self-activation coefficient of gene a. In this case, we observe that the NOR state is more robust. We could also perform a functional evolution between AND and NOR by modifying the two considered parameters at the same time. Notice that we could then engineer an AND function from the OR device by simply doing directed mutagenesis on the ribosome binding site of gene c, decreasing then its expression rate. Or engineer an NOR function by decreasing the transcription factor affinity of a on its promoter region.Fig. 9

Bottom Line: Summary.We show how to use an automated procedure to design logic and sequential transcription circuits.This methodology will allow advancing the rational design of biological devices to more complex systems, and we propose the first design of a biological JK-latch memory device.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Biologia Molecular y Celular de Plantas, CSIC-Universidad Politecnica de Valencia, Valencia, Spain.

ABSTRACT
The use of combinatorial optimization techniques with computational design allows the development of automated methods to design biological systems. Automatic design integrates design principles in an unsupervised algorithm to sample a larger region of the biological network space, at the topology and parameter levels. The design of novel synthetic transcriptional networks with targeted behaviors will be key to understand the design principles underlying biological networks. In this work, we evolve transcriptional networks towards a targeted dynamics, by using a library of promoters and coding sequences, to design a complex biological memory device. The designed sequential transcription network implements a JK-Latch, which is fully predictable and richer than other memory devices. Furthermore, we present designs of transcriptional devices behaving as logic gates, and we show how to create digital behavior from analog promoters. Our procedure allows us to propose a scenario for the evolution of multi-functional genetic networks. In addition, we discuss the decomposability of regulatory networks in terms of genetic modules to develop a given cellular function. Summary. We show how to use an automated procedure to design logic and sequential transcription circuits. This methodology will allow advancing the rational design of biological devices to more complex systems, and we propose the first design of a biological JK-latch memory device.

No MeSH data available.