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Synthetic biology outside the cell: linking computational tools to cell-free systems.

Lewis DD, Villarreal FD, Wu F, Tan C - Front Bioeng Biotechnol (2014)

Bottom Line: Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems.At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems.We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

View Article: PubMed Central - PubMed

Affiliation: Integrative Genetics and Genomics, University of California Davis , Davis, CA , USA ; Department of Biomedical Engineering, University of California Davis , Davis, CA , USA.

ABSTRACT
As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

No MeSH data available.


Related in: MedlinePlus

Stochastic simulation of a compartmentalized oscillator. (A) A linear construct (T21A1) transcribes a short RNA oligo (rI2) that base-pairs with a nicked region of another linear DNA promoter (T12A2). Base pairing of rI2 with the nicked DNA region A2 deactivates T12A2 because T7 promoter region of the switch becomes single stranded, referred to as T12. Deactivation of the T12 construct reduces transcription of RNA oligo rA1, which base pairs with nicked DNA region A1 of the T21A1 construct. Base pairing of rA1 with dl1 causes A1, a quencher labeled-strand of DNA, to separate from T21. T21 also has a fluorescent labeled strand of DNA. This reaction activates the fluorescent signal, but deactivates transcription of RNAP because the T7 promoter of T21 becomes single stranded. Essentially, the oscillator is formed by the T21A1 module (SW21) that inhibits the T12A2 module (SW12), which in turn activates SW21. The delayed negative feedback loop causes a temporal delay between ON and OFF cycles of T21A1. (B) The panels show concentrations of T21 in the OFF position (low fluorescence, high transcriptional activity) over time according to the Gillespie algorithm, Poisson Distribution, and Gamma Distribution, respectively. Droplet radius is always 2 μm. (C) The oscillator period is plotted against the radius of the compartment housing the in vitro circuit. The results suggest that only a small percentage of the variability experienced by the system can be attributed to the intrinsic noise of biochemical reactions. (D) Fluctuations of the oscillator period with increasing radius of the compartment. Panels show partitioning variance (error caused by incomplete distribution of reactants) of period following a Poisson distribution, Gamma distribution, or a Gamma distribution that accounts for loss of enzyme activity. Dark green shading corresponds to a scale factor β = 10, and light green shading corresponds to β = 100. (E) Phase diagrams that indicate period, amplitude, and dampening coefficient as a function of RNAP and RNase H concentrations. Solution space that achieves sustained oscillations, dampened oscillations, and severely dampened oscillations are referred to in the diagram as s, d, and sd, respectively. The white arrows represent behavioral trends experienced by the circuit when it loses RNAP and/or RNAse H. Figure modified with permission from Weitz et al. (2014).
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Figure 3: Stochastic simulation of a compartmentalized oscillator. (A) A linear construct (T21A1) transcribes a short RNA oligo (rI2) that base-pairs with a nicked region of another linear DNA promoter (T12A2). Base pairing of rI2 with the nicked DNA region A2 deactivates T12A2 because T7 promoter region of the switch becomes single stranded, referred to as T12. Deactivation of the T12 construct reduces transcription of RNA oligo rA1, which base pairs with nicked DNA region A1 of the T21A1 construct. Base pairing of rA1 with dl1 causes A1, a quencher labeled-strand of DNA, to separate from T21. T21 also has a fluorescent labeled strand of DNA. This reaction activates the fluorescent signal, but deactivates transcription of RNAP because the T7 promoter of T21 becomes single stranded. Essentially, the oscillator is formed by the T21A1 module (SW21) that inhibits the T12A2 module (SW12), which in turn activates SW21. The delayed negative feedback loop causes a temporal delay between ON and OFF cycles of T21A1. (B) The panels show concentrations of T21 in the OFF position (low fluorescence, high transcriptional activity) over time according to the Gillespie algorithm, Poisson Distribution, and Gamma Distribution, respectively. Droplet radius is always 2 μm. (C) The oscillator period is plotted against the radius of the compartment housing the in vitro circuit. The results suggest that only a small percentage of the variability experienced by the system can be attributed to the intrinsic noise of biochemical reactions. (D) Fluctuations of the oscillator period with increasing radius of the compartment. Panels show partitioning variance (error caused by incomplete distribution of reactants) of period following a Poisson distribution, Gamma distribution, or a Gamma distribution that accounts for loss of enzyme activity. Dark green shading corresponds to a scale factor β = 10, and light green shading corresponds to β = 100. (E) Phase diagrams that indicate period, amplitude, and dampening coefficient as a function of RNAP and RNase H concentrations. Solution space that achieves sustained oscillations, dampened oscillations, and severely dampened oscillations are referred to in the diagram as s, d, and sd, respectively. The white arrows represent behavioral trends experienced by the circuit when it loses RNAP and/or RNAse H. Figure modified with permission from Weitz et al. (2014).

Mentions: One notable cause of stochastic variation in cell-free expression is the process of encapsulation, which could be simulated using stochastic models. For instance, during the compartmentalization of the PURE system in small liposomes (580 nm > diameter > 35 nm), the distribution of reactants between compartments was shown to follow power law statistics instead of a Poisson distribution as previously assumed (Lazzerini-Ospri et al., 2012). A subsequent study of in vitro systems encapsulated in larger liposomes (575 nm and 2.67 μm, respectively) predicted resulting reactant concentrations via a stochastic model following the Gillespie algorithm (Calviello et al., 2013). Another example of stochastic variation in an encapsulated in vitro system comes from a recent work detailing the behavior of a compartmentalized transcriptional oscillator (Weitz et al., 2014). The performance of the circuit within an emulsion was highly variable and was originally assumed to be the result of intrinsic noise of the system acting stochastically at small volumes (Weitz et al., 2014). However, the model of the reaction demonstrated that intrinsic noise was insufficient to describe the variability exhibited by the system; instead, the dominant cause of the deviation from the deterministic model was more likely to be extrinsic noise caused by heterogeneous distribution of reactants within the emulsion (Figure 3) (Weitz et al., 2014). This kind of discrepancy from the deterministic model is also observed during replication when cytoplasmic components are unequally distributed between daughter cells (Huh and Paulsson, 2011a,b; Weitz et al., 2014). The significant impact of extrinsic noise on this minimal system suggests that reactant distribution is an important factor in encapsulated in vitro reactions, which could be overlooked when considering the source of stochastic variation within in vivo systems (Huh and Paulsson, 2011a,b; Weitz et al., 2014).


Synthetic biology outside the cell: linking computational tools to cell-free systems.

Lewis DD, Villarreal FD, Wu F, Tan C - Front Bioeng Biotechnol (2014)

Stochastic simulation of a compartmentalized oscillator. (A) A linear construct (T21A1) transcribes a short RNA oligo (rI2) that base-pairs with a nicked region of another linear DNA promoter (T12A2). Base pairing of rI2 with the nicked DNA region A2 deactivates T12A2 because T7 promoter region of the switch becomes single stranded, referred to as T12. Deactivation of the T12 construct reduces transcription of RNA oligo rA1, which base pairs with nicked DNA region A1 of the T21A1 construct. Base pairing of rA1 with dl1 causes A1, a quencher labeled-strand of DNA, to separate from T21. T21 also has a fluorescent labeled strand of DNA. This reaction activates the fluorescent signal, but deactivates transcription of RNAP because the T7 promoter of T21 becomes single stranded. Essentially, the oscillator is formed by the T21A1 module (SW21) that inhibits the T12A2 module (SW12), which in turn activates SW21. The delayed negative feedback loop causes a temporal delay between ON and OFF cycles of T21A1. (B) The panels show concentrations of T21 in the OFF position (low fluorescence, high transcriptional activity) over time according to the Gillespie algorithm, Poisson Distribution, and Gamma Distribution, respectively. Droplet radius is always 2 μm. (C) The oscillator period is plotted against the radius of the compartment housing the in vitro circuit. The results suggest that only a small percentage of the variability experienced by the system can be attributed to the intrinsic noise of biochemical reactions. (D) Fluctuations of the oscillator period with increasing radius of the compartment. Panels show partitioning variance (error caused by incomplete distribution of reactants) of period following a Poisson distribution, Gamma distribution, or a Gamma distribution that accounts for loss of enzyme activity. Dark green shading corresponds to a scale factor β = 10, and light green shading corresponds to β = 100. (E) Phase diagrams that indicate period, amplitude, and dampening coefficient as a function of RNAP and RNase H concentrations. Solution space that achieves sustained oscillations, dampened oscillations, and severely dampened oscillations are referred to in the diagram as s, d, and sd, respectively. The white arrows represent behavioral trends experienced by the circuit when it loses RNAP and/or RNAse H. Figure modified with permission from Weitz et al. (2014).
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Related In: Results  -  Collection

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Figure 3: Stochastic simulation of a compartmentalized oscillator. (A) A linear construct (T21A1) transcribes a short RNA oligo (rI2) that base-pairs with a nicked region of another linear DNA promoter (T12A2). Base pairing of rI2 with the nicked DNA region A2 deactivates T12A2 because T7 promoter region of the switch becomes single stranded, referred to as T12. Deactivation of the T12 construct reduces transcription of RNA oligo rA1, which base pairs with nicked DNA region A1 of the T21A1 construct. Base pairing of rA1 with dl1 causes A1, a quencher labeled-strand of DNA, to separate from T21. T21 also has a fluorescent labeled strand of DNA. This reaction activates the fluorescent signal, but deactivates transcription of RNAP because the T7 promoter of T21 becomes single stranded. Essentially, the oscillator is formed by the T21A1 module (SW21) that inhibits the T12A2 module (SW12), which in turn activates SW21. The delayed negative feedback loop causes a temporal delay between ON and OFF cycles of T21A1. (B) The panels show concentrations of T21 in the OFF position (low fluorescence, high transcriptional activity) over time according to the Gillespie algorithm, Poisson Distribution, and Gamma Distribution, respectively. Droplet radius is always 2 μm. (C) The oscillator period is plotted against the radius of the compartment housing the in vitro circuit. The results suggest that only a small percentage of the variability experienced by the system can be attributed to the intrinsic noise of biochemical reactions. (D) Fluctuations of the oscillator period with increasing radius of the compartment. Panels show partitioning variance (error caused by incomplete distribution of reactants) of period following a Poisson distribution, Gamma distribution, or a Gamma distribution that accounts for loss of enzyme activity. Dark green shading corresponds to a scale factor β = 10, and light green shading corresponds to β = 100. (E) Phase diagrams that indicate period, amplitude, and dampening coefficient as a function of RNAP and RNase H concentrations. Solution space that achieves sustained oscillations, dampened oscillations, and severely dampened oscillations are referred to in the diagram as s, d, and sd, respectively. The white arrows represent behavioral trends experienced by the circuit when it loses RNAP and/or RNAse H. Figure modified with permission from Weitz et al. (2014).
Mentions: One notable cause of stochastic variation in cell-free expression is the process of encapsulation, which could be simulated using stochastic models. For instance, during the compartmentalization of the PURE system in small liposomes (580 nm > diameter > 35 nm), the distribution of reactants between compartments was shown to follow power law statistics instead of a Poisson distribution as previously assumed (Lazzerini-Ospri et al., 2012). A subsequent study of in vitro systems encapsulated in larger liposomes (575 nm and 2.67 μm, respectively) predicted resulting reactant concentrations via a stochastic model following the Gillespie algorithm (Calviello et al., 2013). Another example of stochastic variation in an encapsulated in vitro system comes from a recent work detailing the behavior of a compartmentalized transcriptional oscillator (Weitz et al., 2014). The performance of the circuit within an emulsion was highly variable and was originally assumed to be the result of intrinsic noise of the system acting stochastically at small volumes (Weitz et al., 2014). However, the model of the reaction demonstrated that intrinsic noise was insufficient to describe the variability exhibited by the system; instead, the dominant cause of the deviation from the deterministic model was more likely to be extrinsic noise caused by heterogeneous distribution of reactants within the emulsion (Figure 3) (Weitz et al., 2014). This kind of discrepancy from the deterministic model is also observed during replication when cytoplasmic components are unequally distributed between daughter cells (Huh and Paulsson, 2011a,b; Weitz et al., 2014). The significant impact of extrinsic noise on this minimal system suggests that reactant distribution is an important factor in encapsulated in vitro reactions, which could be overlooked when considering the source of stochastic variation within in vivo systems (Huh and Paulsson, 2011a,b; Weitz et al., 2014).

Bottom Line: Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems.At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems.We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

View Article: PubMed Central - PubMed

Affiliation: Integrative Genetics and Genomics, University of California Davis , Davis, CA , USA ; Department of Biomedical Engineering, University of California Davis , Davis, CA , USA.

ABSTRACT
As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

No MeSH data available.


Related in: MedlinePlus