In this paper we propose a new method of eliciting market research information. Instead of asking respondents for their personal choices and preferences, we ask respondents to predict the choices of other respondents to the survey. Such predictions tap respondents’ knowledge of peers, whether based on direct social contacts or on more general cultural information. The effectiveness of this approach has already been demonstrated in the context of political polling. Here we extend it to market research, specifically, to conjoint analysis. An advantage of the new approach is that it can elicit reliable responses in situations where people are not comfortable with disclosing their true preferences, but may be willing to give information about people around them. A theoretical argument demonstrates that predictions should yield utility estimates that are more accurate. These theoretical results are confirmed in four online experiments. Traditionally, market research begins by asking potential customers for their personal preferences and intentions, and then projects sample data to the entire market. In this paper we propose a different approach: instead of asking respondents about their own likely choices, we ask them to predict the market. This approach has already been successful in political polling, as shown by , who demHere we extend the general approach of tapping peer information to market research, and we use conjoint analysis as our methodological test bed. Conjoint analysis is a method of measuring consumer’s preferences about a product, service, project or policy –20. It iprediction of peer choices instead of their personal choices. The closest to our study is [Although traditional conjoint analysis requests respondents to make personal choices, recently some studies introduced the role of others in choice-making. The authors in  achieve study is , in whicUnlike  which usOur hypothesis is that peer-choices yield improved preference share predictions (i.e. market share predictions), relative to predictions based on personal choices. In addition, an important advantage of peer-choice conjoint analysis is that it can elicit reliable responses in situations where people are not comfortable with disclosing their true preferences, but may be willing to give information about people around them. For example, this may happen in research related to health, lifestyle, habits and behavior, etc.The theoretical argument for our method extrapolates the reasoning of  which asThis approach is related to the Bayesian respondent approach in . In thatOur method can be easily combined with traditional conjoint approach in the same survey, by simple addition of peer-prediction questions. In this way, features of traditional conjoint such as segmentation by own preferences can be kept, while adding all the advantages of peer-choice conjoint.To summarize, in this paper we hope to contribute to the body of research in the following way:We introduce peer-choice in binary choice conjoint analysis instead of personal choice,We empirically test the new approach in four experiments and demonstrate better predictive ability for peer-choice in preference shares.Four experiments test the hypothesis that peer-choices have better predictive ability. Experiments 1 and 2 use the same type of product (streaming media stick). To make sure results are not dependent on the choice of stimuli, Experiment 3 is performed on a different product (fitness/activity tracker). While Experiments 1, 2, and 3 are within-subjects studies, Experiment 4 is a between-subjects study, where one group of subjects was used for estimation and the other group for validation. All experiments were performed with online panels. In each experiment we compare traditional choice based conjoint (which we refer to as the own-choice model) with the new approach based on peer-choice. We show that statistically the peer-choice conjoint has higher predictive accuracy compared to the traditional conjoint model.In this paper we focus on binary choice based conjoint analysis, where respondents are asked to choose between two presented alternatives. The conceptual framework is the familiar discrete choice model, with respondents choosing the alternative with higher utility.own-choice model. In the second model, the utility structure is derived from peer-choices that are determined in the following way: (1) each respondent gives peer-predictions for both alternatives in every choice set , (2) if the peer-prediction for alternative A is larger than the peer-prediction for alternative B, we infer that the respondent thinks that A would be chosen by the majority, and therefore we define the peer-choice to be A. In short, the own-choice answers the question “which alternative would you choose?”, while the peer-choice answers the question “which alternative will the majority of your peers choose?”.Within this general setting, we compare two models. The first model is based on the traditional conjoint approach, where utility structure is derived from personal choices that respondents make. We refer to this as the k = 1,2, denote the two alternatives in the choice set r. The utility for an alternative is expressed as a linear combination of features and utilities for each feature (i.e. part-worths). More precisely, for a respondent i, the utility can be represented as βil are utilities for respondent i and xrlk are dummies designating levels of attributes present in the alternative k, which is in the given choice set r. Let i chooses in the set r. With this convention, the probability of choosing alternative 1 can be written as:F, the link function, is the logit.Let i is sampled from a multivariate normal distribution, i.e. b is normally distributed, b ~ N with very large variance. In other words, respondents’ preferences are linked by a common multivariate normal distribution. The covariance matrix W is assumed to come from an inverse Wishart distribution with a scale matrix that is diagonal with equal diagonal entries. Gibbs sampling or Metropolis-Hastings algorithms are used for drawing posteriors. The resulting values are individual estimates of utilities The Hierarchical Bayesian logit model estimates the vector of individual utilities βi=(βli) –27. Briei. For a choice set r, we define the peer-predicted choice to be alternative 1 if the respondent i believes that more than 50% of others will choose the alternative 1. Apart from that change, the above model remains the same, except that the meaning of the notation changes. In this model i in the choice set r. Notice that the utility i thinks that others have for the alternative k, where k = 1,2. The Hierarchical Bayesian model can be used in the same way as in the own-choice case. The difference is that estimated utilities now reflect the average utility of others as perceived by the respondent i, instead of the individual utility of the respondent i. We can think about these as expressing the utility structure of the “average, representative, or typical respondent” from the perspective of respondent i. We will elaborate on that in the next section.We focus again on respondent Assume that we have conducted a conjoint survey where each choice set was accompanied by two questions: the first question was about one’s personal choice within the set (i.e. own-choice question), while the second question was about the probability of how others would choose (i.e. peer-prediction question). While the first question provides data for the derivation of an individual’s personal utility, the second question allows us to infer the predicted utility structure. .r which consists of two alternatives {A,B}. Our respondent i reports their own choice, and their predictions for the percentage of people who will choose each of the alternatives. Respondents form this latter estimate on the basis of information from their social circle, in the way described by [m people, then they will mentally poll these m people.Let us consider a choice set ribed by : when pei assigns to the alternative A in the choice set r on the basis of the social circle of size m. We define the associated peer-choice i in the following way: r where r, by Tr we denote the true choice made by the majority of the population, then this means that we can expect peer-choices to be better approximations of Tr than own-choices. The theoretical support for that is provided in the By research , 28 showi across all choice sets allows us to estimate the utilities for the typical respondent, as seen by the respondent i. We denote these utilities by vector Tr, for all choice sets r. As peer-choices are expected to be a better approximation of the majority choices than own-choices, we would expect the utilities estimated from peer-choices to be closer to the real utilities Recall that collecting the peer-choices provided by the person To investigate if that is true, we conduct four experiments where we compare the realized shares of preferences with those predicted by the peer-choice model and with those predicted by the traditional own-choice model.The experimental work in this study was approved by the Committee on the Use of Humans as Experimental Subjects of the Massachusetts Institute of Technology (protocol number E-1340). Subjects were shown a written consent statement before the start of the online experiment. They expressed their consent by clicking on the YES icon.All experiments were conducted with the Mechanical Turk community. Subjects were based in the US, they expressed interest in the category, and they were screened to represent potential buyers. To achieve the latter, in all the experiments we excluded people who reported that they neither owned the product nor wanted to buy it in the future, as we judged them not to be in the market.In Experiment 1 we used streaming media players. In Experiment 2 we repeated the same procedure one month later with a different group of people. In Experiment 3 we repeated the conjoint experiment on fitness/activity trackers. The first three experiments represent a within-subjects study, so we conducted a between-subjects study in Experiment 4 for additional validation. That last experiment was performed on smartwatches.For all four experiments, the attributes and levels were sourced from the trade press and verified for comprehension with several product users in face-to-face interviews. We selected 5 attributes in the case of streaming media players, 8 attributes in the case of fitness/activity trackers, and 5 attributes in the case of smartwatches. Each attribute had at least two and at most 4 levels, making this a good case for the application of choice based conjoint analysis . Finished surveys were then pre-tested with a small group of potential users.The first three experiments have a similar setup. Subjects saw twenty binary choice sets each. Fifteen of these sets were used to compute utilities, three were holdout sets reserved for validation, and two were repetitions of previously shown choice sets that were intended to screen for lack of attention. The three holdout sets were choice sets number 6, 12, and 18. The position of these sets was fixed for every respondent. Other choice sets were randomized. After holdout choice set 18, we repeated sets 6 and 18 to check for attention. Respondents who gave inconsistent answers across the two presentations of set 18 were deemed inattentive and excluded.In Experiment 4 the survey was shorter, as it contained 15 binary choice sets in total compared to 20 in the first three experiments, and almost all non-conjoint questions were omitted. For that reason, we did not expect fatigue to be a big problem and we did not screen for attention. The main differing feature of Experiment 4 was that validation was performed on a separate sample consisting of different respondents.We recorded the overall time that respondents spent on the survey, and we introduced a threshold below which we concluded that subjects could not give reliable answers. For the first three experiments that threshold is 300 seconds, and for the fourth experiment it is 150 seconds. This latter threshold is lower than in the previous three experiments because the survey was shorter and required less effort to complete.After exclusion of subjects on all the criteria described above , in Experiment 1 we retained 70% of the sample (56 subjects), in Experiment 2 we retained 68% of the sample (55 subjects), and in Experiment 3 we retained 83% of the sample (66 subjects). In Experiment 4 we used only the first two criteria (not being in the market and time threshold), which left us with 86.5% of the sample (225 subjects).Each of the four experiments started with a short description of the testing procedure, explaining to subjects that they would be asked to make repeated choices between two alternatives. We also gave a very short description of the product (explaining what it was and how it was used). Each choice task consisted of the visual and text descriptions of the two offered alternatives followed by: (1) the question about which alternative the respondent would choose, and (2) the question that asked the respondent to make a peer-prediction. The exact wording of the second question was “What percentage of your peers would choose the same alternative as you?”, which was sufficient to infer the binary distribution across alternatives. As for possible order effects of these two questions, we relied on the doctoral thesis  which shFurther details of methods and materials for experiments necessary to reproduce experiments (such as descriptions of choice sets) are provided in the In all experiments we used the Bayesian Truth Serum (BTS) scoring rule invented in , which iThe partial orthogonal design for the choice based conjoint analysis was produced by JMP Pro 14 SAS software. The same software was used for estimation of utilities via the Hierarchical Bayes model. A brief outline of the HB approach in conjoint is given earlier in this paper. SAS JMP employs a version of the Train algorithm that incorporates Adaptive Bayes and Metropolis-Hastings approaches. All Hierarchical Bayesian models were estimated with 300,000 iterations, 150,000 of them for burn-in. We allowed respondents to report any percentage between 0% and 100% as their peer-prediction. In those cases when respondents’ peer prediction was equal to 50%, we used the JMP “no-choice feature” which enters the respondent’s choice indicator as missing .The product chosen was a streaming media player, which can be plugged into any HDTV and connected to the existing WiFi network, thus enabling streaming of video and music content from online services. Respondents were informed that the profiles we showed them differed only in the following attributes: (1) price , (2) resolution , (3) remote control , (4) ability to use the product in hotels, dorms, etc. , and (5) inclusion of extra popular channels .Experiment 2 used the same product and attributes as Experiment 1. It was performed about a month after the first one, with a different set of respondents. We added one extra holdout set that consisted of three real products to further test predictive ability.As explained before, we used the same three holdout sets as in Experiment 1. The additional holdout set consisted of three real products that were available on the market at the time of testing: ROKU, Chromecast Ultra, and ROKU Premiere. All three products come in the form of USB sticks, and were the three most popular choices in that category and format at the time of testing.The third experiment used a fitness/activity tracker, a different type of product. While streaming media sticks are used by the majority of the population (as watching TV is a favorite pastime), the fitness tracker is aimed at fitness enthusiasts. The instructions made it clear that only respondents interested in fitness should participate in the MTurk task.The fitness trackers that we tested were bracelets made of elastomer material. Each product automatically tracked steps, calories burned and distance travelled. Each synced wirelessly with users’ mobile phone and/or computer, using a free app to display the stats and activity data. All tested products had a sleep tracking system and rechargeable battery. The products were differentiated on the following attributes: (1) price , (2) heart monitor included , (3) GPS included , (4) workout tracking in real time , (4) water resistance , (5) display and notifications , and (6) battery life . The setup of the experiment was the same as in the first two cases. There were three holdout sets, which were placed in the same positions as in the previous experiments . Furthermore, as in Experiment 2, there was an additional holdout set containing three different real fitness trackers.For additional validity, we designed a between-subjects experiment. We recruited 260 respondents from the MTurk panel to perform a conjoint exercise. The product was a smartwatch, with differentiating attributes: product shape , brand , fitness tracking , heart/health tracking , and price . Fifteen binary choice sets were presented in random order.A total of 225 subjects remained after exclusion of those who were not in the market and those who were considered inattentive due to too little time spent on the survey. They were randomly divided into two groups.Further, we recruited a different group of 80 respondents a week after the first survey . After we excluded answers from subjects who said they did not own a smartwatch and they were not planning to buy it in the future, we were left with 73 respondents . The validation sample subjects were showed eight sets of three real smartwatches chosen from the following products: Apple 2, Apple 3, Apple 4, Samsung Gear S3 Classic, Samsung Galaxy, and Garmin Fenix 5. To make the task easier and more salient, every photograph of a real product was accompanied with a list of attribute levels that were not visible from the picture , which were described the same way as in the first part of the experiment. In each 3-alternative choice set, subjects were asked to choose their preferred product and report the percentages of others who would choose each of the alternatives. Subjects were informed that their answers would be used to compute Bayesian Truth Serum scores, that the scores reward honest answers and knowledge of others, and that they would be rewarded according to their scores.We use the data from the four experiments to test our theoretical argument that the peer-choice model provides a more accurate overall share of preference than the traditional approach. We test this by comparing prediction errors from both models (own-choice and peer-choice). Part-worths estimated from own-choice and part-worths estimated from peer-choice models are utilized to calculate the predicted share of alternatives.i’s part-worth for attribute/level l, then by βl we denote the mean taken over all the respondents. In a two-alternative set r consisting of alternatives A and B, the preference share for alternative A is computed asrlAx and rlBx are dummies designating levels of attributes present in the alternatives A and B. In a three-alternative set consisting of alternatives A, B and C, the preference share of A isWe compute preference shares using average values of individual respondents’ part-worths. More precisely, if βl) For each draw, the latter are used to produce probabilities that alternatives are chosen (i.e. prediction shares). To arrive at the overall prediction for preference shares, we average these probabilities over the 150,000 draws.For estimation purposes we utilize 300,000 draws in the MCMC algorithm, out of which the last 150,000 are retained. Each draw produces a vector of individual part-worths  and the predicted share . Then we examine ordered couples where the first coordinate is the error obtained from the own-choice model, while the second coordinate is the error obtained from the peer-choice model, and we use matched pairs test to check whether peer-choice errors are significantly smaller.Practically, in a two-alternative choice set each alternative gives rise to such an ordered couple, so we use only the error associated with one of them as the other one is symmetric. In the three-alternative sets we report the ordered pairs for only two of the alternatives, since the third one is dependent on them.In these three within-subjects experiments there are three holdout sets  which were set aside and not used for estimation of part-worths. In Experiment 2 and Experiment 3 there is also one additional set each containing three real products. We consider these eleven sets from the first three experiments together, and we compare prediction errors from both models (from both own-choice and peer-choice). This gives us 13 data points . As explained above, in the three-alternative choice sets we exclude the third alternative as redundant: these excluded alternatives are ROKU Premiere in Experiment 2, and Fitbit Charge in Experiment 3. The data is presented in We obtain that the average error from the peer-choice model is 0.12, while the average error from the own-choice model is 0.21. The Wilcoxon signed rank test is significant with p = 0.0002 for the two-tailed test. The study was performed on a sample of 225 subjects (after data cleaning), which was randomly divided into two equal groups. We computed both own-choice part-worths and peer-choice part-worths for both groups. First we used own-choice part-worths from Group 1 and peer-choice part-worths from Group 2 to compute overall share predictions for the eight holdout sets consisting of real products. Both were compared to the realized shares from the third independent group , which was shown only the eight holdout sets. After that, Groups 1 and 2 were switched and the same procedure was repeated, so that Group 1 was used for computation of peer-choice predictions, while Group 2 was used for calculation of own-choice predictions .We computed absolute errors associated with own-choice predictions and with peer-choice predictions. We pooled the absolute errors from both computations and applied matched pairs t-test and Wilcoxon signed rank test, which were highly significant with p = 0.001 and p = 0.003 for the two-tailed test, respectively. The pairs of own and peer errors are presented in In summary, data from all four experiments shows that statistically the peer-choice model produces a significantly smaller error compared to the own-choice model.Having said that, as this is the first study on the topic of peer-choice conjoint, it has limitations that will be resolved by future research. One limitation of our dataset is that realized shares that are used as a validation benchmark happen to be statistically closer to a chance model , compared with the own-choice predictions. Consequently, as the peer-choice model better approximates real choices, peer-choice predictions are also closer to chance compared to own-choice predictions. However, it is known that if respondents mistakenly choose alternatives that do not represent their true utility, the magnitude of their part-worths will decrease, and their choice predictions for different alternatives will likely be similar . AlthougWe propose a new method of eliciting market research information, based on asking respondents to predict the choices of their peers. Peer predictions expand the virtual sample size, and tap individuals’ knowledge of their local crowd preferences.We focus on conjoint analysis to illustrate the viability of the new approach. Although there has been much work on the estimation in conjoint analysis, up until now the main paradigm was to elicit respondents’ personal choice as a source of preference structure. With this paper we open up another perspective by proposing to consider respondents’ opinion of what others would choose, instead of eliciting their personal choice.First, we develop a theoretical argument that shows that relying on peer-knowledge yields improvements: namely, larger social circles allow for more accurate utility estimates and for better predictions of preference share. Our theoretical model was confirmed in four online experiments: three experiments performed validation within subjects and the fourth one validated the method between subjects.In engaging respondents’ meta-knowledge, our approach is similar in spirit to Security Trading of Concepts (STOC) by , where pWe can envision some additional benefits that the new approach may deliver. First, by drawing on people’s social circles we collect information about a larger number of people, so in a way we involve a larger number of subjects. Another advantage of the peer-choice model is that it is particularly helpful when people are reluctant to report personal choices that may reveal potentially sensitive private information or preferences.Our method can be combined with the traditional conjoint approach. Our approach does not preclude the use of the personal choice question: instead we advocate the addition of peer-choice to the personal choice question. An additional advantage is that those two questions together allow for easy survey incentivization through the use of Bayesian Truth Serum.Although larger circles should lead to better prediction, the accuracy is moderated by the level of choice dependence within the social circles: information increases as social circle members become mutually more independent. Smaller independent circles can impart as much information as larger circles where members are more dependent in their choices. Our model should perform best in situations where social circles are large and diverse. Future research may investigate if the model accuracy can be improved by screening for respondents with large and diverse circles.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.S4 Data(XLSX)Click here for additional data file.S1 Questionnaire(PDF)Click here for additional data file.S2 Questionnaire(PDF)Click here for additional data file.S3 Questionnaire(PDF)Click here for additional data file.S4 Questionnaire(PDF)Click here for additional data file.S5 Questionnaire(PDF)Click here for additional data file.S1 Code(DOCX)Click here for additional data file.


Orthocurvulavirus within the family Orthocurvulaviridae. The purpose of the study was to determine the biochemical requirements for RNA synthesis catalyzed by HetRV6 RNA-dependent RNA polymerase (RdRp). HetRV6 RdRp was expressed in Escherichia coli and isolated to near homogeneity using liquid chromatography. The enzyme activities were studied in vitro using radiolabeled UTP. The HetRV6 RdRp was able to initiate RNA synthesis in a primer-independent manner using both virus-related and heterologous single-stranded (ss)RNA templates, with a polymerization rate of about 46 nt/min under optimal NTP concentration and temperature. NTPs with 2′-fluoro modifications were also accepted as substrates in the HetRV6 RdRp-catalyzed RNA polymerization reaction. HetRV6 RdRp transcribed viral RNA genome via semi-conservative mechanism. Furthermore, the enzyme demonstrated terminal nucleotidyl transferase (TNTase) activity. Presence of Mn2+ was required for the HetRV6 RdRp catalyzed enzymatic activities. In summary, our study shows that HetRV6 RdRp is an active replicase in vitro that can be potentially used in biotechnological applications, molecular biology, and biomedicine.Heterobasidion RNA virus 6 (HetRV6) is a double-stranded (ds)RNA mycovirus and a member of the recently established genus  RNA-dependent RNA polymerases (RdRp) catalyze RNA synthesis using an RNA template, and they are indispensable for the life cycle of viruses with single-stranded (ss) and double-stranded (ds)RNA genomes. Apart from several short, conserved sequence motifs , RdAll viral RdRps employ the universal two-metal-ion mechanism of nucleotide addition , in whichttps://www.wwpdb.org/ accessed on 30 June 2021) -UTP . To study transcription mechanism, 90 nM of HetRV6 dsRNA1 was used as a template in polymerase reaction. All other components were kept the same. The TNTase activity was measured in the presence of only a single NTP type which was either 0.3 µM UTP supplemented with 0.25 mCi/mL of [α-33P]-UTP  or the same amounts of ATP and [γ-33P]-ATP . Reactions were initiated by addition of the HetRV6 RdRp to a final concentration of 0.8–1.2 μM. In the control reactions, enzyme was replaced with an equal volume of the RdRp storage buffer  glycerol, 0.05% bromophenol blue and 0.05% xylene cyanol FF -UTP we would expect release of unlabeled (+)RNA strand at the end of the first round of transcription and label incorporation to the (–) strand of the dsRNA. After 45 min reaction, the dsRNA1 became labeled, and the label intensity was increasing within two hours RNA on the (+)RNA template and production of a (+)RNA strand on the dsRNA template. The former activity is generally referred to as replication, and the latter as transcription. Recombinant HetRV6 RdRp can efficiently replicate ssRNA templates in vitro  and doesBirnaviridae, which use self-priming, i.e., the RdRp functions as a protein-primer [Reoviridae) can synthesize a 5′-end phosphorylated dinucleotide pGpG or ppGpG, which is then apparently used as a short primer to produce a full-length RNA product [Our results suggest that HetRV6 RdRp initiates ssRNA replication de novo, i.e., it does not require primer or a template enabling back-priming for this activity see . Primer-n-primer ,67. Rota product .The polymerization rate of a (-)RNA synthesis catalyzed by HetRV6 RdRp is about 46 nt/min under optimal conditions, which is rather slow compared to other previously described RdRps. For example, phi6 RdRp incorporates 7200 nt/min at 28 °C  while thHetRV6 RdRp demonstrated activity on variety of ssRNA templates . Viral pThe relative concentration of nucleotides is crucial for the optimal activity of HetRV6 RdRp. We discovered that elevated concentrations of UTP, CTP, and GTP in the reaction mixture, negatively affected replication, and their optimal concentration was 0.2−0.25 mM since a significant decrease in dsRNA yield was observed with higher NTP concentrations . We did 2+ ion [2+ ions, whereas Mg2+ was not absolutely required for its activity. However, Mg2+ ions did stimulate RNA synthesis. It has been demonstrated that non-catalytic Mn2+ ions are essential for the efficient template binding, initiation, and elongation via increasing structural flexibility of RdRp and establishing correct coordination of a catalytic aspartate needed to properly interact with the triphosphates of the incoming NTPs [2+ ions in the reaction buffer reflecting structural changes that result in Mn2+ ion dissociation from the RdRp and the requirement to reload the binding site with Mn2+ to continue elongation [2+ ions to the Mg2+-containing reaction buffer stimulates the activity of a number of viral RdRps, including RdRp from phi6 and related bacteriophages, poliovirus, HCV, and reovirus [2+ also supports polymerization in the absence of Mg2+ ions suggesting that it can serve as a catalytic ion [2+ is very similar to Mg2+ in its physical properties, but it is more polarizable and binds more tightly to the carboxylates of AspA and AspC, as well as to triphosphate moiety of NTPs [In addition to NTPs, divalent metal ions considerably affected RNA synthesis, which is related to their paramount role in the catalysis of the phosphodiester bond formation. According to a two-metal-ion mechanism of nucleotide addition , two cat2+ ion ,72. Our ing NTPs . Furtherongation . Accordireovirus ,56,65,74ytic ion ,56. Mn2+ of NTPs , which c2+ exceeding 1 mM inhibited polymerization despite the presence of both Mg2+ and Mn2+, meaning that Ca2+ can outcompete them in interaction with catalytic aspartate residues RNA was not detected (In addition to replication of (-)RNA, HetRV6 RdRp catalyzes transcription on dsRNA1 template to produce (+)RNA molecules. Similar to the most dsRNA viruses, with the exception of those from the family detected . HoweverFlaviridae , Nodaviridae , Caliciviridae , and Picornaviridae (poliovirus) [Cystoviridae) [Picobirnaviridae) [Birnaviridae) [TNTase activity was described for a number of (+)RNA viruses, including representatives of the families iovirus) ,80,81,82viridae) , human Pviridae) , and IPNviridae)  were repviridae) . The TNT2+ cations. The ability of HetRV6 RdRp to include modified NTPs into a nascent RNA chain makes it a potential tool for biotechnological applications. The TNTase activity of the enzyme might be useful in terms of non-templated enzymatic RNA synthesis.To sum up, our study is the first experimental proof that HetRV6 RdRp possesses replicase activity in vitro and is able to initiate a complementary strand synthesis on the native and heterologous ssRNA templates in a primer-independent manner. We also suggested semi-conservative mechanism of transcription for HetRV6. The catalytic activities are strongly dependent on the presence of Mn


KCNJ6-201 transcript encoding for potassium inwardly rectifying channel subfamily J member 6 and DSCR4-201 transcript encoding Down syndrome critical region 4. Two transcripts recently identified by massive RNA-sequencing (RNA-Seq) and automatically annotated on Ensembl database reveal that the HR-DSCR seems to be partially crossed by KCNJ6-202 and DSCR4-202 isoforms. KCNJ6-202 shares the coding sequence with KCNJ6-201 which is involved in many physiological processes, including heart rate in cardiac cells and circuit activity in neuronal cells. DSCR4-202 transcript has the first two exons in common with DSCR4-201, the only experimentally verified gene uniquely present in Hominidae. In this study, we performed in silico and in vitro analyses of the HR-DSCR. Bioinformatic data, obtained using Sequence Read Archive (SRA) and SRA-BLAST software, were confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Sanger sequencing on a panel of human tissues. Our data demonstrate that the HR-DSCR cannot be defined as an intergenic region. Further studies are needed to investigate the functional role of the new transcripts, likely involved in DS phenotypes.Down syndrome (DS) is caused by trisomy of chromosome 21 and it is the most common genetic cause of intellectual disability (ID) in humans. Subjects with DS show a typical phenotype marked by facial dysmorphisms and ID. Partial trisomy 21 (PT21) is a rare genotype characterized by the duplication of a delimited chromosome 21 (Hsa21) portion and it may or may not be associated with DS diagnosis. The highly restricted Down syndrome critical region (HR-DSCR) is a region of Hsa21 present in three copies in all individuals with PT21 and a diagnosis of DS. This region, located on distal 21q22.13, is 34 kbp long and does not include characterized genes. The HR-DSCR is annotated as an intergenic region between  However, it is still unclear how the specific alterations of Hsa21 genes are linked to the phenotypic features of subjects with DS.Human chromosome 21 (Hsa21) is the smallest human autosome, and it represents 1–1.5% of the human genome . Jérôme locus that is needed to manifest DS  of only 34 kb located on distal 21q22.13 from 37,929,229 to 37,963,130 shared by all subjects with PT21 and with diagnosis of DS  is a key model for linking genotype and phenotype in subjects with DS . PT21 isifest DS . The limifest DS  that allifest DS . Systemaifest DS . A systeifest DS  rather tis of DS .Hominidae family, suggesting a role in the development of higher brain function  which extends the KCNJ6locus through the addition of two exons upstream of HR-DSCR and includes this region within a long intron (see KCNJ6-201 (NM_002240.5) and KCNJ6-202 are both transcripted from the reverse DNA strand and they share the complete coding sequence (CCDS) (CCDS42927)  neurons and generates slow inhibitory post-synaptic potentials playing an important role in the control of resting membrane potential and regulation of cellular excitability (KCNJ6-201 mutation is associated with Keppen-Lubinsky Syndrome (OMIM #614098), a rare condition characterized by severe developmental delay, facial dysmorphism, and ID.The HR-DSCR has not yet been characterized, but recently, two transcripts partially crossing this region were identified by massive RNA sequencing (RNA-Seq) and automatically annotated in Ensembl Genome Browser. The first transcript is tron see . KCNJ6-2tability . KCNJ6-2KCNJ6-202 partially shares its two additional exons with DSCR4 isoforms , DSCR4-202 (ENST00000398948.5), and DSCR4-203 (ENST00000482032.1) are annotated in the genome databases and they share the first two exons and the promoter. The first exon is separated by 92-bp sequence from DSCR8 gene; this separator sequence is a bidirectional promoter that starts the transcription of both genes. DSCR4-202 is the only transcript that overlaps HR-DSCR with two exons  software we performed an in silico analysis to report KCNJ6 and DSCR4 expression values in several tissues. In the same panel of tissues, KCNJ6 and DSCR4 expression values were detected by Reverse Transcription-Polymerase Chain Reaction (RT-PCR).The aim of this work is to characterize HR-DSCR https://www.ensembl.org/index.html) was used to annotate the exon limits of the investigated isoforms and to observe their location in comparison to the HR-DSCR limits. BLASTN software was used to align exon junction sequences (used as query sequence) with RNA-Seq experiments selected as explained below, in order to evaluate tissue isoform expression. For reads obtained by the bio-project “Illumina HiSeq 2500 of 4 human trisomy 21 and 4 human normal control blood cells” (PRJNA635385), recently annotated on Gene, we used local SRA-BLAST (https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software) to perform the alignments. We considered only alignments with at least 95% of query cover and 97% of identity as significative.Ensembl Genome Browser (KCNJ6 (https://www.ncbi.nlm.nih.gov/gene/3763) and DSCR4 (https://www.ncbi.nlm.nih.gov/gene/10281) NCBI Gene schedule under the “Expression” heading. The three bio-projects selected were: “HPA RNA-seq normal tissues (PRJEB4337)”; “RNA sequencing of total RNA from 20 human tissues (PRJNA280600)”; “Illumina bodyMap2 transcriptome (PRJEB2445)”; we excluded “Tissue-specific circular RNA induction during human foetal development (PRJNA270632)” because it included experiments performed on circular RNA during the fetal phase. We included the bio-project “Illumina HiSeq 2500 of 4 human trisomy 21 and 4 human normal control blood cells” (PRJNA635385), in which RNA-Seq was performed on RNA samples extracted following the same procedures and conditions of RNA used in this work for in vitro analyses  involved in the DS phenotype and which were available in our laboratory to confirm computational data with molecular biology experiments.The euploid tissues selected from the four bio-projects were: adrenal gland, brain, cerebellum, cerebral cortex, heart, liver, placenta, skeletal muscle, skin, testis, thymus, thyroid, white blood cells and blood cells. The T21 tissue selected was T21 blood cells see .in vitro analyses were compared with those reported by the RNA-Seq experiments carried out on the same tissues and listed above.For the molecular biology experiments, we used commercially available RNA samples , derived from the following human euploid tissues: adrenal gland, brain, cerebellum, cerebral cortex, heart, liver, placenta, skeletal muscle, testis, thymus, thyroid. The main characteristics of RNA donors are listed in KCNJ6 and DSCR4 both in euploid and trisomic tissues only for RNA derived from fibroblast and blood cells obtained as follows.It was possible to detect the expression of in vitro analyses were compared with those reported by the RNA-Seq experiments (PRJNA635385) performed on normal and T21 blood samples.Normal and T21 blood samples were collected at the Neonatology Unit of S. Orsola-Malpighi Hospital of Bologna, in the context of the “Genotype-phenotype correlation in trisomy 21 (Down syndrome)” project . Blood samples were collected in ethylenediaminetetraacetic acid (EDTA)-coated blood collection tubes, and plasma fraction was isolated within 2 h from blood collection according to our previously published work . Five mLmycoplasma to exclude possible contamination. Furthermore, a karyotype analysis was carried out on T21 cell lines by GGB to confirm the cytogenetic diagnosis. Five mL of denaturing solution was added to the cell flasks and they were stored at −20°C until RNA extraction. The main features of donors are listed in in vitro analyses performed on euploid fibrobast cell lines were compared with those reported by the RNA-Seq experiments (PRJEB4337) performed on normal skin tissues.Normal and T21 primary fibroblast cell lines were provided by Galliera Genetic Bank (GGB), member of the “Network Telethon of Genetic Biobanks.” All the cell lines were tested for RNA extraction from blood samples and fibroblast cell lines was performed with the method of Chomczynski and Sacchi , and RNAa) between 55°C and 65°C and the difference between the two temperatures should be less than 2°C. Guanines (Gs) and Cytosines (Cs) content should be around 40–60% and it is recommended to avoid secondary structures such as hairpins, self-dimers and cross-dimers which could be caused by self-complementarity of forward and reverse primers. The primers must have biological specificity to avoid non-specific amplifications within the genome (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) was used to evaluate the biological specificity by comparing the primer pair sequences versus the entire collection of nucleotides (nr/nt). The primer sequences and characteristics were reported in Primer pairs were designed with “Amplify 3” program  followine genome . Primer Pools of RNA samples were created from RNA derived from T21 and normal control blood cells: one with three T21 blood cell RNA samples (330 ng of each) and the second with three normal control blood cell RNA samples (330 ng of each) see . Pools oKCNJ6 and DSCR4 expression. Each 25 µL PCR reaction contained 2.5 µL of cDNA, 1 unit (U) of Platinum Taq DNA Polymerase , 2.5 µL of PCR buffer (10x), 25 mM of MgCl2 , 5 mM dNTPs mix , 0.2 µM of each primer. The mixture was denatured at 94°C for 5 min and the PCR was performed for 45 cycles under the following conditions: denaturation at 94°C for 30 seconds (s), annealing at 58–62°C (depending on primer features) for 30 s or 1 min , and extension at 72°C for 1 min. The final extension cycle of 72°C was for 10 min. RT reactions were performed using MJ Research PTC-200 Thermal Cycler and PCR reactions were performed using GenePro TC-E-48D Thermal Cycler.Reverse transcription (RT) was conducted on 1 µg of RNA . Complementary DNA (cDNA) was obtained using “SuperScript III First-strand Synthesis Supermix” kit  according to manufacturer instructions: 10 min (minutes) at 25°C, 30 min at 50°C, 5 min at 85°C. cDNA obtained from RT was amplify through PCR to detect Amplification products were analyzed by electrophoresis on agarose gel and by Sanger sequencing. The RT-PCR products were purified with GenElute PCR Clean-up Kit  and prepared for Sanger sequencing. The mixture was prepared following BigDye Kit protocol : 10 ng of amplicon, 0.32 µM of forward or reverse primer, 2 µL of PCR buffer, 0.5–1 µL of BigDye 1b and E1bis  Gene database, was discovered in 1997 via Expressed Sequence Tag (EST) (DSCR4-202 transcript is described as long non-coding-RNA (lncRNA), and it has 4 exons of which the third and the fourth are included in the HR-DSCR   and it hag (EST) . DSCR4-2DSCR see . The lonxons see  and it iKCNJ6-201 and KCNJ6-202 shared 2 exon junctions, one generated by the last 25 nt of the E2 and the first 25 nt of the E3, and the second generated by the last 25 nt of the E3 and the first 25 nt of the E4 (see KCNJ6-202 and the three isoforms of DSCR4 shared 1 exon junction generated by the last 25 nt of the E1 (or E1b) and the first 25 nt of the E2 (or E1bis)  with RNA-Seq experiments. The exon junction sequence of 50 bp was used as query sequence, and it was generated by the last 25 nucleotides (nt) of the previous exon  and the first 25 nt of the next exon . e E4 see . KCNJ6-2bis) see .KCNJ6-201  confirming the data reported in literature , detected in placenta and testis, is the same of DSCR4-201, DSCR4-202, and DSCR4-203 and was indicated with an asterisk (*) in . E1bis-E2, which is exclusive of this transcript, did not generate alignments with RNA-Seq experiments. The last part of the transcript (E2-E4), being in common with KCNJ6-201 isoform, generated the same alignments reported in The reads generated by RNA-Seq experiments were aligned with exon junctions of DSCR4-201 transcript. The alignments predict the expression of the entire transcript in placenta and testis, confirming the previous literature data  was used to perform sues see .KCNJ6-201 transcript, amplified by primer pairs on E1-E4 .The entire b-E4 see , was detb-E4 see . We obtab-E4 see . Sanger DSCR4-201 transcript, amplified by primer pairs on E1-E3 .The entire sues see , but wassues see . The seqDSCR4-203 transcript was not detected in any tissues (see The entire or partial sues see .loci. In this work, starting from HR-DSCR limits designed by Pelleri and coll. (HR-DSCR is the minimal region of Hsa21 shared by all subjects with PT21 and a confirmed diagnosis of DS. It is described as an intergenic region, but its link with the typical DS symptoms suggests that it could contain active nd coll. , EnsemblKCNJ6-201 and DSCR4-201 are validated transcripts and are deposited on the NCBI Gene database, even if the role of DSCR4-201 is not yet well characterized. In this work, the computational and molecular analyses confirmed the gene expression profile reported in NCBI Gene database and through an enlarged panel of tissues we enriched the knowledge on their tissue specificity (see city see , 4.in vitro characterization of KCNJ6-202, DSCR4-202, and DSCR4-203 transcripts, automatically annotated in Ensembl Genome Browser from massive RNA-Seq data.For the first time we performed the KCNJ6-202 transcript was detected in brain, cerebellum, and placenta, while the entire DSCR4-202 and DSCR4-203 transcripts were not detected in any tissues. In particular, a 177 bp amplicon was detected using a primer pair based on exon 3 and exon 4 of DSCR4-202 that was not connected with its previous exons. DSCR4-203 was not detected by either computational and molecular analyses.The entire All the transcripts were visible on agarose gel after 45 cycles of PCR and only in specific tissues. These characteristics are typical of lncRNA, which follow a specific timing and tissue expression pattern, and have a key role in cell differentiation, organogenesis, and tissue homeostasis .DSCR4 isoforms are annotated as lncRNA in Ensembl database and HR-DSCR could contain regulatory RNA elements in its genomic locus. Instead, KCNJ6-202 function is associated to KCNJ6-201 because it is thought that they code for the same protein.LncRNAs can interact with DNA and RNA molecules, transcription factors, and they participate in various biological processes such as DNA methylation, histone modification, chromatin remodeling, up-regulation or down-regulation of target genes . All DSCDSCR4-202 has the third and fourth exons in the HR-DSCR (see KCNJ6-202 includes HR-DSCR in a long intron between its second and third exon (see locus where splicing occurs.Interestingly, DSCR see , while Kexon see . 9ecent exon see . Thus, tHominidae family, highlighting a role in the development of higher brain function (DSCR4 was overexpressed in human non-cancerous cells and differential gene expression analysis was performed to measure the consequences on the cell transcriptome. The results suggested that DSCR4 could have a role in the regulation of interconnected biological pathways related to cell migration, coagulation and immune system consistent with well-known pathways affected in subjects with DS (The organization of HR-DSCR is extremely complex and was conserved during the evolution of the function . Recentl with DS  and conflocus and of the KCNJ6-202 and DSCR4-202 transcripts that intersect it and to clarify their properties and function in the tissues analyzed.In order to understand the function of this region it is necessary to define the function of the transcripts included in it. Further studies are necessary to characterize the organization of the HR-DSCR Hominidae family, we suggest avoiding the use of mouse as a model organism to test the effects of overexpression of the transcripts crossing the HR-DSCR. Actually, it may be difficult to make assumptions about complex loci across different species. This topic is exemplified by another gene located on Hsa21 named DSCAM which can produce only a few splicing isoforms in humans, while it has the potential to produce 38,016 isoforms in Drosophila melanogaster, contributing to the formation of complex patterns of neuronal connections (loci across different species, other limitations for the use of mouse as a model organism for DS should be considered (Due to the particular structure of the HR-DSCR, which is unique to the nections . Aside fnsidered , such asnsidered  and moreIn conclusion, this study confirms the importance of a systematic and thorough analysis of apparently silent regions of the human genome. In particular, the strict association between HR-DSCR and diagnosis of DS deriving from clinical data suggests the need for an intensive study of this region to understand the genotype-phenotype relationship in DS and to identify targets for a rational treatment of the ID associated to DS.


E. coli. Our findings demonstrate that while RNA-based regulators excel in speed as compared to protein-based regulators, the fast reaction kinetics of RNA-based regulators could also undermine the functionality of a circuit . The agreement between experiments and simulations suggests that the mechanistic modeling can help debug issues and validate the hypothesis in designing a new circuit. Moreover, the applicability of the kinetic parameters extracted from the RNA-only circuit to the RNA–protein hybrid circuit also indicates the modularity of RNA-based regulators when used in a different context. We anticipate the findings of this work to guide the future design of gene circuits that rely heavily on the dynamics of RNA-based regulators, in terms of both modeling and experimental realization.RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators, such as the small transcriptional activating RNA (STAR), toehold switch (THS), and three-way junction (3WJ) repressor, have been utilized to construct RNA-based synthetic gene circuits in living cells. In this work, we utilized these regulators to construct type 1 incoherent feed-forward loop (IFFL) circuits in vivo and explored their dynamic behaviors. A combination of a STAR and 3WJ repressor was used to construct an RNA-only IFFL circuit. However, due to the fast kinetics of RNA–RNA interactions, there was no significant timescale difference between the direct activation and the indirect inhibition, that no pulse was observed in the experiments. These findings were confirmed with mechanistic modeling and simulation results for a wider range of conditions. To increase delay in the inhibition pathway, we introduced a protein synthesis process to the circuit and designed an RNA–protein hybrid IFFL circuit using THS and TetR protein. Simulation results indicated that pulse generation could be achieved with this RNA–protein hybrid model, and this was further verified with experimental realization in  Synthetic biology is a nascent discipline that integrates various research fields, including biotechnology, molecular biology, and biophysics, which aims to build artificial decision-making circuits in living organisms in a predictable and programmable manner ,2. Over Nucleic acid-based regulators are promising tools for building synthetic biological systems due to their excellent programmability and predictable thermodynamic properties . In partThe type 1 incoherent feed-forward loop (IFFL) network motif is commonly found in natural bacterial networks and has received interest due to its wide applications such as band-pass filters, fold-change detection ,21, biosHere, we employ STARs to realize the direct activation of gene expression at the transcription level, and 3WJ repressors to achieve repression at the translation level for an RNA-only IFFL circuit. In a similar vein, we also explore an RNA–protein hybrid IFFL circuit with THS and TetR protein . Gene exMathematical modeling validated with experimental measurements can provide a rapid characterization of parts and a reliable prediction of circuit performance under a broad range of conditions that are challenging and costly to achieve by experiments. The success of using mathematical modeling to quantify and guide the design of synthetic gene circuits has been reported in various systems from bacteria to mammalian cells ,31,32,33E. coli. Then, we constructed mathematical models to perform a more comprehensive investigation of the dynamics of the RNA-only IFFL circuit, under different conditions. The experiment and simulation indicate that the RNA-only IFFL circuit may not have enough delays between the activation and the inhibition pathways for a pronounced pulse generation and confirm the importance of a timescale difference in IFFL, as previously reported [E. coli also showed pulse generation for a wide range of inducer concentrations, demonstrating a good agreement with the mathematical model. Our results demonstrate that the combination of modeling and experiments is an effective approach to prototyping synthetic RNA circuits for the predictable and precise control of gene expression. The findings in our study also provide evidence that RNA-based circuits can serve as modular and composable parts for synthetic biological circuits.Here, we started by constructing an RNA-only IFFL circuit and analyzing its dynamics in reported ,37. BaseE. coli BL21 AI strain for RNA-only IFFL circuit and E. coli BL21 DE3 strain for RNA–protein hybrid IFFL circuit were used for in vivo tests.Backbones for the plasmids were taken from the commercial vectors pET15b, pCDFDuet, and pCOLADuet . Node X was constructed in pET15b. Node Y and node Z were constructed in pCDFDuet and pCOLADuet. All constructs were cloned via Gibson assembly and round-the-horn site-directed mutagenesis, and detailed protocol can be found in d-1-thiogalactopyranoside (IPTG) and Arabinose were treated immediately. IPTG was treated at four different concentrations: 500 µM, 125 µM, 31.2 µM, 7.81 µM. Arabinose was treated at four different concentrations: 6600 μM, 1650 μM, 412.5 μM, 103.1 μM. For the RNA–protein hybrid IFFL circuit, overnight cultures were diluted 1/50-fold, and then IPTG and anhydrotetracycline (aTc) were treated immediately. IPTG was treated at four different concentrations: 1 mM, 0.2 mM, 0.1 mM, 0.02 mM. aTc was treated at six different concentrations: 400 nM, 200 nM, 40 nM, 4 nM, 2 nM . An aliquot of 200 µL of inducer-treated cells was added per well on a 96-well Black Plate . Plates were incubated at 37 °C with double-orbital shaking in a Synergy H1 microplate reader  running Gen5 3.08 software. GFP Fluorescence  and OD600 were measured at 10-min intervals during incubation. The RNA-only IFFL circuit was incubated for 5 h (31 cycles), and the RNA–protein hybrid-based IFFL circuit was incubated for 8 h (49 cycles). GFP fluorescence levels were normalized as follows: GFP fluorescence for LB blank was subtracted and the resulting value was divided by OD600. The number of replicates was three for RNA-only and six for RNA–protein hybrid IFFL circuits.Transformed cells were cultured on Luria-Bertani (LB) agar plates (1.5% agar) and then single colonies were inoculated into 500 µL LB liquid medium supplemented with appropriate antibiotics: pCOLADuet (50 μg/mL Kanamycin), pCDFDuet (50 μg/mL Spectinomycin), pET15b (100 μg/mL Ampicillin). These cells were grown overnight (~16 h) in 96-well plates with shaking at 800 r.p.m. and 37 °C. For the RNA-only IFFL circuit, overnight cultures were diluted 1/100-fold, and then Isopropyl β-odes23s solver for the simulated GFP concentration, in both the model parameterization with experimental data, and the sensitivity analysis for the RNA–protein hybrid model. Parameterization of the RNA-only and RNA–protein hybrid models were performed by fitting to the experimental measurements with MATLAB functions fminsearch and fmincon, respectively. The sensitivity analysis was completed using MATLAB random number generation function rand to randomly select kinetic parameters within the perturbed range, as described in detail in the modeling section below. All the related MATLAB scripts for this work are available at https://github.com/mathiasfoo/ifflbiomolecule (accessed on 22 July 2021).The mathematical ODE models were solved with MATLAB E. coli. To this end, we designed a three-component circuit where the top regulator, X, generated a trans-acting RNA that controlled STAR elements in the two downstream nodes, Y and Z. The intermediate node Y, in turn, expresses an inhibitor for Z, which contains a 3WJ repressor switch module that can be inhibited by a 3WJ trigger RNA. The 3WJ trigger from Y binds to the bottom of the weak stem domain in the 3WJ repressor switch in Z to terminate the translation of GFP reporter protein. Thus, in the system we designed, X activated Z directly and inhibited Z indirectly through an intermediate node Y to complete the IFFL circuit.We first sought to construct the RNA-only IFFL circuit in E. coli. X contained a trans-acting RNA under the control of the T7 promoter (pT7), Y contained a STAR connected to a 3WJ trigger under the control of the Lac promoter (pLlacO), and Z contained a STAR connected to a 3WJ switch and a GFP reporter under the control of pLlacO , insulated Y  and Arabinose (A) concentrations . In the α is the production rate, γ and ω are the binding rates, and δ is the degradation rate. araI and IPTGI are the Arabinose and IPTG inducers, respectively, araK and IPTGK are the Hill constants, m and n are the Hill coefficients, We observe in all the experiments performed so far that no pulse in the GFP concentration was detected, and this contradicts the expectation for a typical IFFL circuit. To understand if this phenomenon was due to the limited range of inducer concentrations tested or it was because of the RNA-only IFFL circuit design itself, we constructed a mechanistic mathematical model to represent the key reactions in the design given in fminsearch, using the Nelder–Mead simplex algorithm [Model parameterization was performed by fitting to the experimental measurements with MATLAB function lgorithm . The GFPWith the fitted model, we first validated the prediction capability of the model with additional experiments with varying Arabinose and IPTG concentrations. The middle row of Next, we used the model to predict the system dynamics under a wider range of IPTG and Arabinose concentrations. The results in the bottom row of Following the analysis of the RNA-only IFFL circuit, we then propose an RNA–protein hybrid IFFL circuit, which featured a THS for RNA-based translational activation and a transcription factor for the protein-based transcriptional repression. Such an RNA–protein hybrid design could be realized by introducing TetR as an intermediate molecule that accomplished the repression of the target gene.Our hypothesis was that the inclusion of the protein (TetR) formation would contribute to a sufficient amount of time delay in the inhibition pathway and would lead to a pulse generation in the output GFP concentration a. To furα is the production rate, δ is the degradation rate, γ and ω are the binding rates, n is the Hill coefficient. Z and Y are the transcribed THS RNAs that were activated by X to form the complexes X:Y and X:Z at the binding rate γ. α, respectively. ω.We developed a mathematical model for the RNA–protein hybrid IFFL circuit, following the same argument as for the RNA-only IFFL circuit model development to validate our hypothesis b. X was Given the similarities between the RNA-only and the RNA–protein hybrid model, we estimated the RNA-related kinetic parameters in the hybrid model from the fitted values in the RNA-only model and inferred the protein-related kinetic parameters from literature to examine the dynamics of the proposed RNA–protein hybrid IFFL circuit. The detailed information regarding the parameter values are given in Considering the possible deviations in the kinetic parameters due to changes in the design of the circuit and in the experimental conditions, we also allowed the kinetic parameters to vary within ±50% of the values identified in Encouraged by the modeling results, we then constructed and tested the RNA–protein hybrid circuit in vivo. The proposed RNA–protein hybrid circuit used a protein regulator, TetR, in the intermediate node Y to increase the time delay between the direct activation of Z from X and the indirect inhibition of Z from X via Y. The top regulatory node X contained a trigger RNA under the control of pT7. The intermediate node Y contained a THS followed by TetR under a constitutive promoter. The output node Z contained a THS followed by a GFP reporter under the control of pT7 that also contained a Tet operator site (TetO). These three nodes were encoded in separate plasmids such that different combinations of nodes could be easily explored a.E. coli BL21 DE3 which harbors a genomic T7 RNAP under the control of Lac promoter. Thus, the expression levels of X and Z were controlled by IPTG, while the expression level of Y was constitutive. Further, the regulatory strength of node Y could be tuned as follows: changing the protein degradation tag on TetR, adjusting the promoter strength of Y, and the addition of a small molecule inducer aTc which titrates TetR. Since TetR can tightly bind to TetO within the promoter sequence, typically a very low concentration of TetR is sufficient to reduce the target promoter activity [Experimentally, these plasmids were transformed in activity . Thus, wactivity . As the To test the performance of THS and trigger RNA pairs, we first designed an activation experiment with input node X and output node Z. Upon the production of trigger RNA from X, the translation of GFP reporter from the transcript of Z would be initiated b. Since E. coli and tested the behavior of the circuit with different inducer concentrations. The trigger RNA from X activated the translation of THS in Y and Z to translate TetR and GFP, respectively. The regulator protein, TetR, in turn, could inhibit the transcription of node Z by binding to the TetO in pT7  can be flexibly combined to achieve desired dynamics. Previous works have shown that STAR and THS can be combined to form AND-gate circuits ,44, and In summary, we demonstrated the RNA-only circuit and the RNA–protein hybrid circuit for IFFL and explored their dynamics with both experiments and modeling. We demonstrated how mathematical modeling can be used to assess and guide the experimental design of synthetic biological circuits. We also found that the parameters obtained from the RNA-only IFFL circuit could also be applicable to the RNA–protein hybrid IFFL circuit, and this transferability indicates that the RNA regulator can be flexibly combined with other regulatory components without experiencing dramatic changes in the kinetics. Together, we demonstrated the utility of de novo-designed RNA regulators for synthetic circuits, and the combined use of mechanistic mathematical modeling in guiding and optimizing the design of synthetic circuits with diverse parts.