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Models for synthetic biology.

Kaznessis YN - BMC Syst Biol (2007)

Bottom Line: The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules.What is new is the emphasis on system behavior.The objective is the design and construction of new biological devices and systems to deliver useful applications.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA. yiannis@cems.umn.edu

ABSTRACT
Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior. The objective is the design and construction of new biological devices and systems to deliver useful applications. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates, and possible applications abound, including biofuels, detectors for biochemical and chemical weapons, disease diagnosis, and gene therapies. More than fifty years after the discovery of the molecular structure of DNA, molecular biology is mature enough for real quantification that is useful for biological engineering applications, similar to the revolution in modeling in chemistry in the 1950s. With the excitement that synthetic biology is generating, the engineering and biological science communities appear remarkably willing to cross disciplinary boundaries toward a common goal.

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A major challenge in synthetic biology is to rationally select DNA sequences that result in targeted dynamic phenotypes. For example, with simulations using Hy3S [29] we are experimenting with multiple alternative promoter sequences to identify the optimal AND gate synthetic gene network, with tetracycline (atc) and IPTG as inputs and green fluorescence protein (GFP) as output.
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Figure 1: A major challenge in synthetic biology is to rationally select DNA sequences that result in targeted dynamic phenotypes. For example, with simulations using Hy3S [29] we are experimenting with multiple alternative promoter sequences to identify the optimal AND gate synthetic gene network, with tetracycline (atc) and IPTG as inputs and green fluorescence protein (GFP) as output.

Mentions: In the 1950s Oppenheim and McQuarrie, among others, explored stochasticity in kinetic models, developing the chemical Master equation formalism to capture discrete interaction events that occur with certain probability in time [14,15]. A numerical stochastic simulation algorithm (SSA) to calculate these probabilistic trajectories was described by Gillespie [16]. Gillespie's algorithm uses the system dynamics to simulate the occurrence of each individual reaction event. In general, given the current state of the system, the SSA seeks the time until the next reaction occurs. It then executes that reaction, updates the state of the system, and increments the simulation time to the new value. Although accurate in capturing the dynamic of biomolecular interaction systems, SSA becomes computationally intractable, if the time scales of involved interaction events are disparate, because it simulates every single biomolecular interaction event, spending inordinate amounts on fast reactions for very few simulated occurrences of slow reactions. The modeling community was up to the challenge and in the last decade there have been numerous attempts to improve the efficiency of the SSA [17-23]. As a result, recently algorithms have appeared that successfully tackle biomolecular interaction phenomena with disparate time scales [24-29] (see Figure 1). Although work is still underway, there are now exciting developments that the synthetic biology community can benefit from.


Models for synthetic biology.

Kaznessis YN - BMC Syst Biol (2007)

A major challenge in synthetic biology is to rationally select DNA sequences that result in targeted dynamic phenotypes. For example, with simulations using Hy3S [29] we are experimenting with multiple alternative promoter sequences to identify the optimal AND gate synthetic gene network, with tetracycline (atc) and IPTG as inputs and green fluorescence protein (GFP) as output.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194732&req=5

Figure 1: A major challenge in synthetic biology is to rationally select DNA sequences that result in targeted dynamic phenotypes. For example, with simulations using Hy3S [29] we are experimenting with multiple alternative promoter sequences to identify the optimal AND gate synthetic gene network, with tetracycline (atc) and IPTG as inputs and green fluorescence protein (GFP) as output.
Mentions: In the 1950s Oppenheim and McQuarrie, among others, explored stochasticity in kinetic models, developing the chemical Master equation formalism to capture discrete interaction events that occur with certain probability in time [14,15]. A numerical stochastic simulation algorithm (SSA) to calculate these probabilistic trajectories was described by Gillespie [16]. Gillespie's algorithm uses the system dynamics to simulate the occurrence of each individual reaction event. In general, given the current state of the system, the SSA seeks the time until the next reaction occurs. It then executes that reaction, updates the state of the system, and increments the simulation time to the new value. Although accurate in capturing the dynamic of biomolecular interaction systems, SSA becomes computationally intractable, if the time scales of involved interaction events are disparate, because it simulates every single biomolecular interaction event, spending inordinate amounts on fast reactions for very few simulated occurrences of slow reactions. The modeling community was up to the challenge and in the last decade there have been numerous attempts to improve the efficiency of the SSA [17-23]. As a result, recently algorithms have appeared that successfully tackle biomolecular interaction phenomena with disparate time scales [24-29] (see Figure 1). Although work is still underway, there are now exciting developments that the synthetic biology community can benefit from.

Bottom Line: The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules.What is new is the emphasis on system behavior.The objective is the design and construction of new biological devices and systems to deliver useful applications.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA. yiannis@cems.umn.edu

ABSTRACT
Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior. The objective is the design and construction of new biological devices and systems to deliver useful applications. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates, and possible applications abound, including biofuels, detectors for biochemical and chemical weapons, disease diagnosis, and gene therapies. More than fifty years after the discovery of the molecular structure of DNA, molecular biology is mature enough for real quantification that is useful for biological engineering applications, similar to the revolution in modeling in chemistry in the 1950s. With the excitement that synthetic biology is generating, the engineering and biological science communities appear remarkably willing to cross disciplinary boundaries toward a common goal.

Show MeSH
Related in: MedlinePlus