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Can I use multiple bicistronic RBS sequences in a synthetic biological circuit?

Can I use multiple bicistronic RBS sequences in a synthetic biological circuit?


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The bicistroninc RBS sequences (BCDs) developed by Mutalik et al. [1] aim to remove context sensitivity from translation and therefore ensure more predictable gene expression. However, I have been told that having multiple transcriptional units, each using BCDs, can make cells sick. The explanation that I was given was that the toxicity is from small peptides that are produced from transcription from the first RBS sequence.

Has anyone demonstrated toxicity from multiple BCDs or is it apocryphal? If it has been demonstrated, is the mechanism of toxicity known and is there a way to use multiple BCDs while maintaining healthy cells?

[1] V. K. Mutalik et al., “Precise and reliable gene expression via standard transcription and translation initiation elements,” Nat. Methods, vol. 10, no. 4, pp. 354-360, Apr. 2013.


That's a great question and has lot of opportunities to explore. I am not sure anyone has followed up on this original BCD work systematically. We did try cloning these elements on a medium copy plasmids, in an operon design, driving multiple genes, and now I think about it, that was not a smart thing to do. Cloning was very challenging, probably due to toxicity you mention. Didn't have time to investigate this in depth (that it self is a cool project), but I think the toxicity is due to too much resources (ribosomes?) titrated out on these units. But that's a hand-wavy explanation. Need mechanistic study to investigate. I think Tom Ellis's group and others have done some beautiful work on toxicity associated with protein expression. Personally I think the toxicity is not related to the inert peptide (1st gene in a BCD operon) that's produced. Another story we tried was to replace this first gene with something functional but not published.

Coming back to using BCDs for driving multiple genes, I would say if you clone these directly on genomes they should work like charm. We did try this and works good. Not published much (oh god, that's so much work unpublished!) Bacterial operons have these junctions and elements of different strengths. Happy to discuss more if you have a follow up question!!

Thanks for your interest in this work. Really proud of our work on this system. Of course happy that we did this work as there were so many early effort on these BCD type elements. And love this open discussion SE platform! Don't know how synbio survived without this system of sharing knowledge and experiences. Best wishes

Vivek Mutalik


In the documentation for this recent cloning method from Richard Murray's group at Caltech (,https://doi.org/10.1021/acssynbio.8b00060) they include a parts library with various BCDs. I haven't tested this directly myself, but the warning they note for multiple BCDs is not that they are toxic, but that because they have long homologous regions that they are prone to recombination. This makes sense since the dummy peptide region is I think around 50bp and shared across all the BCDs. You could imagine making variants in this region to combat recombination issues, but who knows if that would affect the robustness of the BCD expression.


Expected value of second run in a sequence of coin tosses

Let $(X_n)_<>>$ be independent random variables that are equal to $1$ with probability $p$ and to $ with probability $q = 1-p$ . A run is a sequence $(X_, cdots, X_)$ where $k,linmathbb$ such that $X_ = cdots = X_ eq X_$ . Let $L_$ be the length of the $j^$ run ( $l+1$ here).

Calculating the expected value of $L_<2j>$ , for $jinmathbb^<*>$ , will always yield $2$ , whereas the expected value of $L_<2j+1>$ is $frac

+ frac

$ . Is there an intuitive explanation as to why even runs have an expectation that is independent of p? It is a result I find quite suprising.

Outline of proof of my statement for $L_<1>$ and $L_<2>$ :

2) $P((S_1, S_2) = (k,l)) = P(X_<1>=cdots=X_k=0, X_=dots=X_=1, X_=0) + P(X_1=cdots=X_k=1, X_=dots=X_=0, X_=1)$ So $P((S_1, S_2)=(k,l)) = p^lq^ + q^p^$ and summing yields $P(S_2 = k) = p^q^2 + q^p^2$ and $E(S_2) = 2$ .


Can I use multiple bicistronic RBS sequences in a synthetic biological circuit? - Biology

Development of fully synthetic nucleobase pairs that faithfully interact in living cells, and their applications in creating semisynthetic organisms with expanded and orthogonal information-carrying capacity.

Harnessing naturally occurring and mutually orthogonal DNA replication systems to enable replication of target genes. Highly error-prone variations on these systems enable robust directed evolution of biomolecules.

Engineering and directed evolution of mutually orthogonal transcription factors that operate with high dynamic range, low background, and respond to a wide repertoire of stimuli in vivo.

Recent developments in in vivo orthogonal protein translation including: orthogonal RBS–orthogonal anti-RBS pairs, covalently linked rRNA subunits to discover novel enzymatic capabilities, improved incorporation of non-canonical amino acids and decoding quadruplet codons.

Synthetic biology strives to reliably control cellular behavior, typically in the form of user-designed interactions of biological components to produce a predetermined output. Engineered circuit components are frequently derived from natural sources and are therefore often hampered by inadvertent interactions with host machinery, most notably within the host central dogma. Reliable and predictable gene circuits require the targeted reduction or elimination of these undesirable interactions to mitigate negative consequences on host fitness and develop context-independent bioactivities. Here, we review recent advances in biological orthogonalization, namely the insulation of researcher-dictated bioactivities from host processes, with a focus on systematic developments that may culminate in the creation of an orthogonal central dogma and novel cellular functions.


Advances in genetic circuit design: novel biochemistries, deep part mining, and precision gene expression

We review recent advances in the forward design of genetic circuits.

Advanced circuit design concepts pave the way for more complex synthetic cellular regulation.

Part mining and computational design have generated a large regulatory part set.

‘Tuning knobs’ and insulators enable precise control of circuit function.

Genetic circuit engineering is at an inflection point in size and sophistication.

Cells use regulatory networks to perform computational operations to respond to their environment. Reliably manipulating such networks would be valuable for many applications in biotechnology for example, in having genes turn on only under a defined set of conditions or implementing dynamic or temporal control of expression. Still, building such synthetic regulatory circuits remains one of the most difficult challenges in genetic engineering and as a result they have not found widespread application. Here, we review recent advances that address the key challenges in the forward design of genetic circuits. First, we look at new design concepts, including the construction of layered digital and analog circuits, and new approaches to control circuit response functions. Second, we review recent work to apply part mining and computational design to expand the number of regulators that can be used together within one cell. Finally, we describe new approaches to obtain precise gene expression and to reduce context dependence that will accelerate circuit design by more reliably balancing regulators while reducing toxicity.


Using Euler's Totient identity to deduce an equality

However, the question asks you to only use eqref. You don't need to use, nor should you use, eqref. Instead, assume $n$ is any positive integer and set $m = n^$ for any integer $k ge 1$ in eqref to get

There are $2$ basic cases cases to consider. First, if $k = 1$ , then $n^ = 1$ so $gcdleft(n^,n ight) = 1$ and eqref becomes

Note this matches eqref for $k = 1$ . Next, consider $k gt 1$ . Then $gcdleft(n^,n ight) = n$ and eqref becomes

You can see that with $k = 2$ , eqref becomes $phileft(n^2 ight) = nphi(n)$ , which matches eqref. Next, using the previous relation, plus $k = 3$ in eqref, you get $phileft(n^3 ight) = nphileft(n^2 ight) = nleft(nphi(n) ight) = n^2phi(n)$ , which also matches eqref. Using this procedure with mathematical induction on $k$ in eqref, you can quite easily prove eqref is true for all positive integers $k$ and $n$ . I'll leave this last part to you to do.


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Synthetic biology is an engineering discipline that builds on our mechanistic understanding of molecular biology to program microbes to carry out new functions. Such predictable manipulation of a cell requires modeling and experimental techniques to work together. The modeling component of synthetic biology allows one to design biological circuits and analyze its expected behavior. The experimental component merges models with real systems by providing quantitative data and sets of available biological ‘parts’ that can be used to construct circuits. Sufficient progress has been made in the combined use of modeling and experimental methods, which reinforces the idea of being able to use engineered microbes as a technological platform.

Paolo Vicini – Pfizer Global Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, San Diego, CA, USA


Synthetic biology is a design-driven discipline centered on engineering novel biological functions through the discovery, characterization, and repurposing of molecular parts. Several synthetic biological solutions to critical biomedical problems are on the verge of widespread adoption and demonstrate the burgeoning maturation of the field. Here, we highlight applications of synthetic biology in vaccine development, molecular diagnostics, and cell-based therapeutics, emphasizing technologies approved for clinical use or in active clinical trials. We conclude by drawing attention to recent innovations in synthetic biology that are likely to have a significant impact on future applications in biomedicine.

These authors contributed equally


Background

Synthetic Biology is defined as the engineering of biology: the deliberate (re)design and construction of novel biological and biologically based parts, devices and systems to perform new functions for useful purposes [1]. As an engineering discipline, it emphasizes engineering principles and methodology in designing, constructing and characterizing biological systems to be applied in industrial, environmental and other applications. Currently, there still is a disparity between the ability to design systems and the one to synthesize them. This disparity can partly be attributed to a lack of well-characterized parts and methods for reliably and robustly composing parts into devices [2].

From the very beginning of Synthetic Biology, efforts have been made in order to characterize standard biological parts –i.e. DNA sequences encoding a function that can be assembled with other standard parts to form devices [3]. Yet, the roadmap to engineering biological systems is determined not by the biological parts but rather by how they interact [4]. Thus, both precise characterization and predictable part composition are essential for the efficient creation of sophisticated genetic circuits [5, 6]. In this context, developing frameworks for functional composition is a current challenge, the solution of which will allow biological components to be systematically, reliably, and predictably assembled into a functional device or system [2].

The systematic design of complex bio-circuits from libraries of standard parts relies on mathematical models describing the circuit dynamics. In this regard, modular modeling tools facilitate the mathematical representation of biological parts and their combinations, providing the description of the reactions which take place inside the different parts and the interfaces that connect them [7, 8]. Computer-aided (model based) methods and tools can be used to guide the design of synthetic biochemical pathways [9–11].

Several problems arise when building up biological devices by combining parts. First, composing different biological parts and devices together can be difficult, even if assuming a synthetic circuit structure has been properly designed to have a pre-specified dynamic behavior, because the desired input and output levels of a module are often unknown, difficult to measure quantitatively, or difficult to compare. Additionally, the ratio part/device performance may be altered due to the interaction of loads in the combined system, the so-called retroactivity [12]. Along with this, there is an ever-growing appreciation for biological complexity, which requires new circuit modeling and design principles to overcome barriers such as metabolic load, cross-talk, resource sharing, and gene expression noise [5, 13–15]. Finally, one must never forget the gap between computational (dry-lab) design, and wet-lab implementation. In practice, biological parts are subject to uncertainty. Circuit structure design and parameters tuning methods must cope with this uncertainty in the biological parts and context to narrow the gap.

To this end, the modular and systematic design of biocircuits, i.e. the systematic way of finding combinations of components from a library of standard parts allowing to optimally perform a pre-defined function, can be formulated using an optimization framework [16–18]. Indeed, it has been argued that Synthetic Biology is less like highly modular (or ‘switch-like’) electrical engineering and computer science, and more like civil and mechanical engineering in its use of models optimization of whole system-level stresses and traffic flow [5].

Advanced optimization-based methods, capable of handling high levels of complexity and multiple design criteria have been proposed for the modular and systematic structural design of biocircuits [19]. These new approaches combine the efficiency of global Mixed Integer Nonlinear Programming solvers with multi-objective optimization techniques [20, 21].

On the other hand, a natural approach to model-based tuning of synthetic circuits consists of the analysis of the effect of key parameters that can be used as tuning knobs in the experimental implementation. In this approach, selection of biological parts is understood as choice of the range of values of key parameters of the device that yield the desired dynamical behavior. A current challenge is to devise methods to provide the set of circuit parameters that satisfies a specified circuit behavior in a way that can be readily used for their wet-lab implementation [22]. Thus, for instance, in [23], the authors synthesize regulatory promoter libraries, characterize key parameters, and use them to guideline the construction of synthetic networks with different predicted input-output characteristics. Global sensitivity analysis is used in [16]. The sensitivity information is used to guide the selection of circuit components and thereby reduce the wet-lab implementation effort. In [24] the authors express the desired behavior as a functional cost index of the desired circuit trajectories. Then, the inverse sensitivity of the mapping between parameters and cost index is obtained after linearising the functional cost index around an initial value of the model parameters. This local inverse mapping is used to map a region of specifications into a one of parameters.

Although the specification of the desired dynamic of the circuit is most often naturally expressed as a multi-objective global optimization problem, this approach has not been used so far. Instead, current approaches define independent thresholds set a priori for each of the functional goals characterizing the desired behavior of the circuit. Then, global Monte Carlo-like approaches are used, sampling the parameters space and simulating the circuit time response. The result of these simulations is used to assess the circuit behavior, so as to profile the subset of the parameters space that result in circuit behavior fulfilling all thresholds. After this, some statistical post-treatment of the results is used, like clustering or correlation analysis or global sensitivity analysis, to draw conclusions between the distribution of the parameters, and the circuit behavior [25]. This Monte Carlo based approach has a huge computational cost. Given a defined search space in the parameters space, the Monte Carlo sampling does not ensure that a solution will be found, thus requiring a large number of samples to find solutions. This problem increases as the thresholds defining the acceptable circuit behavior are more stringent. On the other hand, the solution space obtained weighs, either equally or ad hoc, all the functional goals of the circuit. Thus, besides missing many possible optimal solutions, there may be little variability among the different solutions in the parameters space, making the statistical post-treatment less sensitive.

Feed-forward circuits have been used within this context as an important case-study. In [26] all three-node possible network topologies that present adaptive dynamical behavior are analyzed using function-topology maps based on Monte Carlo sampling in the parameters space. Using a simple enzymatic model, the authors draw design principles of adaptation circuits. They show that there are only two core solutions that achieve robust adaptation: negative feedback loops and incoherent feed-forward ones. In [27], the incoherent feed-forward adaptive enzyme network structure derived in [26], is used as case study. A method is proposed to make inferences on the contribution of individual parameters to specific components of the system. Classes of kinetic parameters are obtained that may correspond to varying strengths of enzymatic reactions that can be measured and classified experimentally. The authors show that, for a given network structure, certain types of values, or motifs, also exist for kinetic parameters in order to achieve specific system dynamics. Clustering in the parameters space to detect kinetic motifs, i.e. sets of parameters yielding desired circuit dynamics, is used in [25].

In this paper, to build a given functional device with desired dynamic behavior, we study the application of a multi-objective optimization design (MOOD) framework [28] to obtain a model-based set of guidelines for the selection of its biological parts. In MOOD all objectives are important, so all of them are optimized simultaneously. Thus, the solution rarely is unique, but a set of solutions called the Pareto Front. In this sense all solutions are Pareto-optimal and differ from each other in the trade-off of objectives that each one represents. Then, the design reduces to encode carefully the desired dynamics into the objectives and optimization problem itself in the MOOD [28]. As a result, the designer obtains qualitative regions/intervals of parameters along the Pareto Front giving rise to the predefined behavior of the circuit. Contrarily to the passive search for solutions of Monte Carlo-based approaches, the multi-objective optimization approach actively searches for all the optimal solutions as a first step. The MOOD framework also naturally provides a classification of the parameters along the Pareto front, by taking into account their effect on each of the goals. Moreover, this framework makes easy to analyze the impact of context on the synthetic devices to be designed. This can be done by just incorporating information about the relationship between the device and the context. In general, this means we only need to know where do we connect the device which is being designed and how we are connecting it. Including this information in the optimization problem, we obtain a qualitative region of parameters taking into account the effect of the context on the device.

The remaining of the paper is organized as follows. In Methods, the general framework, and the type-1 incoherent feed-forward (I1-FFL) circuit that will be used as case study, are presented. Next, in Results, the proposed methodology is applied to the I1-FFL case study, and the main findings for the circuit are described. Two typical application scenarios of the methodology are also considered. Finally, some discussion and general conclusions, both on the methodology and its results on the I1-FFL case study are drawn in Discussion and Conclusion sections.


Figure 5

Figure 5. Influence of sequence composition between the ribosome binding site and the start codon on expression levels. The corresponding RNA sequence for the region of interest of each construct is shown below the bar graph. Ref indicates the reference construct RL027A. Scar 1 is the standard BioBrick scar sequence. Scar 2 is the shorter, alternate scar sequence. −3 A indicates the introduction of an A three positions upstream of the start codon. pET21b is the same spacer sequence found in the expression vector pET21b (Novagen). RBS +1, RBS +2, and RBS +1+2 indicate RBS expansions. Each introduced feature is underlined in the corresponding sequence. Note that only half of the NdeI restriction site is shown since the remaining half overlaps with the start codon. Each bicistrionic construct was expressed in vitro with the PUREsystem at 37 °C for 6 h. Gene 1 encoded mCherry, and gene 2 encoded mVenus. Data are plotted in reference to RL027A.


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Keywords: synthetic biology, environmental microbiology, biogeochemistry, biosensor, cell-free sensors, marine, soil, wastewater

Citation: Del Valle I, Fulk EM, Kalvapalle P, Silberg JJ, Masiello CA and Stadler LB (2021) Translating New Synthetic Biology Advances for Biosensing Into the Earth and Environmental Sciences. Front. Microbiol. 11:618373. doi: 10.3389/fmicb.2020.618373

Received: 16 October 2020 Accepted: 17 December 2020
Published: 04 February 2021.

Christopher Bagwell, Pacific Northwest National Laboratory (DOE), United States

Sang Jun Lee, Chung-Ang University, South Korea
Tetsuhiro Harimoto, Columbia University, United States

Copyright © 2021 Del Valle, Fulk, Kalvapalle, Silberg, Masiello and Stadler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


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