Biology subjects

Koeppl, H.

Publications and source records attributed to Koeppl, H..

18 recordsLinked to original sources

bioRxiv2026

Releasing Protein Cargo from Synthetic Cells by Coupling Cell-Free Expression to Osmotic Actuation

Macromolecular release from synthetic cells often relies on membrane pores or channels. We explored whether internally generated osmotic stress could increase membrane permeability and promote release. Using double-emulsion microfluidics, we encapsulated an Escherichia coli cell-free expression system in Pluronic L121 polymersomes to co-express the fluorescent protein mScarlet3 and the dextranase PsDex1711 in the presence of dextran. PsDex1711-mediated dextran hydrolysis produced a greater increase in bulk osmolality than reactions without PsDex1711 DNA. Tracking hundreds of individual polymersomes showed that PsDex1711-containing populations expanded, whereas control populations contracted. The distribution of normalized fluorescence revealed a subset with lower residual fluorescence than the main population. In selected expanded polymersomes, integrated internal mScarlet3 fluorescence declined to 18-22% of peak levels, without visible membrane disruption, consistent with protein release. These findings support a proof of concept for using internal gene expression to promote macromolecular release through osmotic actuation without introducing dedicated membrane pores.

synthetic biology↗

bioRxiv2026

A Yeast Logic Toolkit for Fast and Efficient Assembly of Genetic Circuits

Synthetic biologists aim for standardization and simplicity in order to ensure reproducibility and reliability. Toolkits offer both, making it easier to share DNA sequences between labs while minimizing context dependent effects from sequences surrounding the construct of interest. The Yeast Toolkit (YTK) for the model organism Saccharomyces cerevisiae has found wide adoption within the scientific community. While the Toolkit and its existing extensions offer a large variety of different parts, there is a lack of characterized logic gates and insulator containing backbones for the assembly of genetic circuits with the yeast toolkit syntax. In this work, we present the Yeast Logic Toolkit (YLT). YLT contains 40 pre-assembled backbones with dropouts, 109 parts for NOT/NOR gate assembly, and a subdivided promoter Type 2 part that allows more flexible 5'UTR design. For insulation, the backbones contain spacer sequences and self-cleaving ribozymes which buffer against transcriptional read-through from upstream sequences. Due to our subdivision of Type 2 parts, we are able to manipulate the 5' untranslated region without a cloning scar upstream of the start codon. We also characterized the NOT and NOR gates from the Cello library for yeast in the sequence context of the yeast toolkit cloning scheme. This toolkit extension will make it easier and faster for scientists to assemble genetic circuits in yeast while providing a minimum of context dependent effects such as genetic read-trough due to the standardization of up- and downstream sequences surrounding the transcriptional units.

synthetic biology↗

bioRxiv2025

iFLinkC-EZ: A scalable and automatable method for the assembly of complex fusion proteins and multi-gene expression constructs based on the iFLinkC framework

Standardized methods for the assembly of DNA constructs have become indispensable in synthetic biology, biotechnology and basic research. The majority of frameworks - most prominently based on Golden Gate - have been developed and optimized for the assembly of transcriptional units (TUs) and thereof composed multi-gene expression constructs. This is significantly enabled by the modular organization of functional elements underlying any given TU. In contrast, the assembly of protein coding sequences typically necessitates tailored approaches. Addressing this technological gap, iterative functional linker cloning (iFLinkC) was recently developed to provide a standardized framework for assembling protein coding sequences of arbitrary size and complexity from the ground up based on pairwise ligations of any two DNA fragments via a two-base overlap. Yet, a need for physically purifying DNA fragments imposes significant procedural complexity and operator skill. Overcoming these limitations, a new assembly algorithm, termed iFLinkC-EZ, is presented. Crucially, the DNA assembly products are now purified by genetic means which greatly simplifies the underlying assembly process and also renders it compatible with robotic automation. Further, the potency of iFLinkC-EZ is demonstrated in the assembly of mono- and poly-chromatic poly-fluorescent fusion proteins as well as poly-cistronic expression constructs while examining the effect of poly-PT and poly-GGS linkers as well as ribosome binding sites on the functional expression of poly-fluorescent fusion protein constructs. Further, the utility of poly-fluorescent proteins is demonstrated for the enhanced labeling of surface displayed recombinant binders in yeast display. Given its ease and efficiency, iFLinkC-EZ is anticipated to be applicable to the assembly of many different types of fusion proteins and thereof based multi-gene expression constructs including but not limited to synthetic protein switches, sensors and multi-enzyme complexes as well as thereof composed genetic circuits and metabolic pathways.

synthetic biology↗

bioRxiv2025

The Coli Toolkit (CTK): An extension of the modular Yeast Toolkit for use in E. coli

Genetic circuits are a cornerstone of synthetic biology, enabling programmable control of cellular behavior for applications in health, sustainability, and biotechnology. While Genetic Design Automation (GDA) tools have optimized and streamlined the design of such circuits, rapid and efficient assembly of DNA remains a bottleneck in the DBTL cycle. Here we present the Coli Toolkit (CTK), a modular Golden Gate-based cloning system, adapted from the Yeast Toolkit (YTK) for use in Escherichia coli. The CTK expands on the original YTK architecture by introducing a more flexible control of transcription and translation through subdividing the former promoter part into subparts; promoter, insulating ribozyme, and ribosome binding site (RBS). We provide a range of basic parts that enable the assembly of a wide range of constructs, as well as characterization data for all constitutive and inducible promoters provided. Additionally, we provide characterization data, as well as calibrated models, for all 20 NOT gates from the Cello library, and we provide the NOT gates as preassembled basic parts, which enables rapid cloning of larger genetic circuits. With this toolkit, we leverage the strengths of the YTK architecture to enable rapid and high-efficiency assembly of genetic circuits in E. coli, filling a key gap in the infrastructure of bacterial synthetic biology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/663901v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@189a5c2org.highwire.dtl.DTLVardef@1e18f6dorg.highwire.dtl.DTLVardef@18f5ddeorg.highwire.dtl.DTLVardef@15bd410_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Decoding stimulus-specific regulation of promoter activity of p53 target genes

The tumor suppressor p53 plays a crucial role in maintaining genome integrity in response to exogenous or endogenous stresses. The dynamics of p53 activation are stimulus- and cell type- dependent and regulate cell fate. Acting as a transcription factor, p53 induces the expression of target genes involved in apoptosis, cell cycle arrest and DNA repair. However, transcription is not a deterministic process, but rather occurs in bursts of activity and promoters switch stochastically between ON and OFF states, resulting in substantial cell-to-cell variability. Here, we characterized how stimulus-dependent p53 dynamics are converted into specific gene regulation patterns by inducing diverse forms of DNA damage ranging from ionizing and UV radiation to clinically relevant chemotherapeutics. We employed single molecule fluorescence in-situ hybridization (smFISH) to quantify the activity of target gene promoters at the single-cell and single-molecule level. To analyse this comprehensive data set, we developed a new framework for determining parameters of stochastic gene expression by Bayesian inference. Using this combined theoretical and experimental approach, we revealed that features of promoter activity are differentially regulated depending on the target gene and the nature and extent of the DNA damage induced. Indeed, stimulus-specific stochastic gene expression is predominantly regulated by promoter activation and deactivation rates. Interestingly, we found that in many situations, transcriptional activity was uncoupled from the total amount of p53 and the fraction bound to DNA, highlighting that transcriptional regulation by p53 is a multi- dimensional process. Taken together, our study provides insights into p53-mediated transcriptional regulation as an example of a dynamic transcription factor that shapes the cellular response to DNA damage.

systems biology↗

bioRxiv2025

NAND Hybrid Riboswitch Design by Deep Batch Bayesian Optimization

The design of large genetic circuits requires genetic regulatory devices capable of performing complex logic operations. Hybrid riboswitches, synthetically enhanced compact RNA elements (<100 nucleotides) that form a tertiary structure with the ability to specifically bind two different target molecules, can be used to design genetic regulators that emulate Boolean logic. When inserted into the 5 UTR of an mRNA, these devices can regulate translation initiation upon specific binding of one or both ligands. The goal of this study is to design hybrid riboswitches that emulate Boolean NAND logic in yeast. We propose a novel machine learning-based design framework combining high-throughput in vivo screening and deep Bayesian optimization. Through an initial screening, we discovered a hybrid riboswitch with NAND behavior. Using batch Bayesian optimization with an ensemble neural network as surrogate, we further improve the NAND functionality of our hybrid riboswitch with respect to a performance score, thereby achieving near digital NAND behavior. With its focus on model-based and score-driven design, our proposed method can complement experiment driven approaches by allowing fine grained adaptation of functionality, including constructs sensitive to single nucleotide changes.

synthetic biology↗

bioRxiv2024

Entropy production constrains information throughput in gene regulation

Biochemical systems process signals through stochastic reaction dynamics that are inherently continuous in time and often exhibit memory, feedback, and nonequilibrium driving. At the same time, they are frequently modeled by effective reactions, e.g., multi-step processes such as transcription are treated as single events, while energetic bookkeeping is commonly omitted. Moreover, mesoscopic dissipation estimates are highly sensitive to whether coarse-graining and reservoir coupling are performed in a thermodynamically consistent way. Together, these features complicate the direct application of classical Shannon information theory and stochastic thermodynamics "as is" to biochemical reaction networks. This paper provides a self-contained route from first principles to a practically usable framework for studying information transmission through chemical reaction networks (CRNs) under energetic constraints. In particular, we discuss and extend the notions of classical information theory, methodically progressing to a level of generality that is necessary for the theme of causal communication through general CRNs. We then derive expressions for mutual information and directed information between bipartite CRN trajectories of disjoint sets of molecular species and show that the MI diverges without bipartiteness. These expressions account for cases in which different reactions are indistinguishable after projection to the respective subnetworks or where multiple driving mechanisms produce the same observable effect. We finally introduce a rigorous, operational Shannon-style continuous-time chemical communication model: messages are encoded by time-dependent chemostat protocols for a set of signaling molecules, the causal channel law is an immutable property of the reaction dynamics, and channel capacity is posed as an optimization over causal chemical encoders subject to thermodynamic costs of encoding and transmission. Trajectory information measures and the operational channel capacity are related by a Fano-type converse theorem. Complementary, we formulate the dual perspective of minimum-energy-per-bit necessary for reliable communication. A tractable promoter-switching example illustrates the practical application. Our work provides a formal and general framework to obtain universal energetic bounds for reliable communication in biochemical systems.

systems biology↗

bioRxiv2024

Deep Learning Reaction Network: a machine learning framework for modeling time resolved data

Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. Such analysis combines experimental hypotheses of the process with mathematical models related to the systems physical mechanisms. This combination can provide a concise description of complex system dynamics and extrapolate kinetic model parameters, such as kinetic pathways, time constants, and species amplitudes. However, the process leading to the final kinetic model requires several intermediate steps in which different assumptions and models are tested, even using different experimental data sets. This approach requires considerable experience in modeling and data comprehension, as poor decisions at any stage of time-resolved data analysis (such as time-resolved spectra and agarose gel electrophoresis) can lead to an incorrect or incomplete kinetic model, resulting in inaccurate model parameters and amplitudes. The Deep Learning Reaction Network (DLRN) can rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. Additionally, DLRN works in scenarios in which the initial state is a non-emitting dark state and for multiple timescales. The utility of DLRN is also shown for more than one 2D system, as it performed well for both spectral and time-resolved agarose gel electrophoresis data.

biophysics↗

bioRxiv2024

Tuning Ultrasensitivity in Genetic Logic Gates using Antisense RNA Feedback

This work provides a study of a possible improvement of existing inverting genetic logic gates by introduction of a common sequestration reaction between their input and output chemical species. As a mechanism of study, we use antisense RNAs (asRNAs). The asRNAs are expressed with the existing messenger RNA (mRNA) of a logic gate in a single transcript and target mRNAs of adjacent gates, creating a feedback of the protein-mediated repression that implements the core function of the logic gates. The extended transcripts then share a common sequestration reaction mediated by the cellular hosts RNA metabolism. This sequestration consists of double-stranded RNA (dsRNA) formation by asRNA and adjacent mRNA and subsequent degradation by the host. Numerical and stochastic analysis suggests that the feedback increases the steepness of the gates transition region, reduces the leakage, and can potentially be used to adjust the transition location. To leverage these effects, we demonstrate how design parameters can be tuned to obtain desired dose-response curves and how arbitrary circuits can be assembled using the improved gates.

synthetic biology↗

bioRxiv2024

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits-shifts a cells priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuits functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/601038v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@77317borg.highwire.dtl.DTLVardef@151a140org.highwire.dtl.DTLVardef@318c19org.highwire.dtl.DTLVardef@e4be6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Hawkes process modelling for chemical reaction networks in a random environment

Cellular processes are open systems, situated in a heterogeneous context, rather than operating in isolation. Chemical reaction networks (CRNs) whose reaction rates are modelled as external stochastic processes account for the heterogeneous environment when describing the embedded process. A marginal description of the embedded process is of interest for (i) fast simulations that bypass the co-simulation of the environment, (ii) obtaining new process equations from which moment equations can be derived, (iii) the computation of information-theoretic quantities, and (iv) state estimation. It is known since Snyders and related works that marginalization over a stochastic intensity turns point processes into self-exciting ones. While the Snyder filter specifies the exact history-dependent propensities in the framework of CRNs in Markov environment, it was recently suggested to use approximate filters for the marginal description. By regarding the chemical reactions as events, we establish a link between CRNs in a linear random environment and Hawkes processes, a class of self-exciting counting processes widely used in event analysis. The Hawkes approximation can be obtained via moment closure scheme or as the optimal linear approximation under the quadratic criterion. We show the equivalence of both approaches. Furthermore, we use martingale techniques to provide results on the agreement of the Hawkes process and the exact marginal process in their second order statistics, i.e., covariance, auto/cross-correlation. We introduce an approximate marginal simulation algorithm and illustrate it in case studies. AMS subject classifications37M05, 60G35, 60G55, 60J28, 60K37, 62M15

systems biology↗

bioRxiv2023

Bayesian analysis dissects kinetic modulation during non-stationary gene expression

Labelling of nascent stem loops with fluorescent proteins has fostered the visualization of transcription in living cells. Quantitative analysis of recorded fluorescence traces can shed light on kinetic transcription parameters and regulatory mechanisms. However, existing methods typically focus on steady state dynamics. Here, we combine a stochastic process transcription model with a hierarchical Bayesian method to infer global as well locally shared parameters for groups of cells and recover unobserved quantities such as initiation times and polymerase loading of the gene. We apply our approach to the cyclic response of the yeast CUP1 locus to heavy metal stress. Within the previously described slow cycle of transcriptional activity on the scale of minutes, we discover fast time-modulated bursting on the scale of seconds. Model comparison suggests that slow oscillations of transcriptional output are regulated by the amplitude of the bursts. Several polymerases may initiate during a burst.

systems biology↗

bioRxiv2022

Beyond linearity: Quantification of the mean for linear CRNs in a random environment

Molecular reactions within a cell are inherently stochastic, and cells often differ in morphological properties or interact with a heterogeneous environment. Consequently, cell populations exhibit heterogeneity both due to these intrinsic and extrinsic causes. Although state-of-the-art studies that focus on dissecting this heterogeneity use single-cell measurements, the bulk data that shows only the mean expression levels is still in routine use. The fingerprint of the heterogeneity is present also in bulk data, despite being hidden from direct measurement. In particular, this heterogeneity can affect the mean expression levels via bimolecular interactions with low-abundant environment species. We make this statement rigorous for the class of linear reaction systems that are embedded in a discrete state Markov environment. The analytic expression that we provide for the stationary mean depends on the reaction rate constants of the linear subsystem, as well as the generator and stationary distribution of the Markov environment. We demonstrate the effect of the environment on the stationary mean. Namely, we show how the heterogeneous case deviates from the quasi-steady state (Q.SS) case when the embedded system is fast compared to the environment.

systems biology↗

bioRxiv2022

Context-Aware Technology Mapping in Genetic Design Automation

Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their wide-spread adoption is hampered by their limited predictive power, resulting in frequent deviations between in-silico and in-vivo performance of a genetic circuit. Context-effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power, and hence, their deployment. Using fine-grained thermodynamic models of promoter activity we show in this work, how to account for two major components of cellular context effects: (i) Crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show, how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cellos device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.

synthetic biology↗

bioRxiv2021

Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (1). We show that an average 1.3-fold and a peak 6.5-fold performance increase can be achieved by simply considering structural variants and that an average 1.8-fold and a peak 30-fold gain in the novel robustness score can be obtained when selecting circuits according to it. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/456094v1_ufig1.gif" ALT="Figure 2"> View larger version (20K): org.highwire.dtl.DTLVardef@3ef8aorg.highwire.dtl.DTLVardef@635957org.highwire.dtl.DTLVardef@7e747eorg.highwire.dtl.DTLVardef@122db0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

The Functional Nanopore (FuN) Screen: A Versatile Genetic Assay to Study and Engineer Protein Nanopores in Escherichia coli

Nanopores comprise a versatile class of membrane proteins that carry out a range of key physiological functions and are increasingly developed for different biotechnological applications. Yet, a capacity to study and engineer protein nanopores by combinatorial means has so far been hampered by a lack of suitable assays that combine sufficient experimental resolution with throughput. Addressing this technological gap, the Functional Nanopore (FuN) screen now provides a quantitative and dynamic read-out of nanopore assembly and function in E. coli. The assay is based on genetically-encoded fluorescent protein sensors that resolve the nanopore-dependent influx of Ca2+ across the inner membrane of E. coli. Illustrating its versatile capacity, the FuN screen is first applied to dissect the molecular features that underlie the assembly and stability of nanopores formed by the S2168 holin. In a subsequent step, nanopores are engineered by recombining the transmembrane module of S2168 with different ring-shaped oligomeric protein structures that feature defined hexa-, hepta- and octameric geometries. Library screening highlights substantial plasticity in the ability of the S2168 transmembrane module to oligomerize in alternative geometries while the functional properties of the resultant nanopores can be fine-tuned through the identity of the connecting linkers. Overall, the FuN screen is anticipated to facilitate both fundamental studies and complex nanopore engineering endeavors with many potential applications in biomedicine, biotechnology and synthetic biology.

synthetic biology↗

bioRxiv2021

Functionalizing cell-free systems with CRISPR-associated proteins: Application to RNA-based circuit engineering

Cell-free systems have become a compelling choice for the prototyping of synthetic circuits. Many robust protocols for preparing cell-free systems are now available along with toolboxes designed for a variety of applications. Thus far the production of cell-free extracts has often been decoupled from the production of functionalized proteins. Here, we leveraged the most recently published protocol for E. coli-based cell extracts with the endogenous production of two CRISPR-associated proteins, Csy4 and dCas9. We found pre-expression did not affect the resulting extract performance, and the final concentrations of the endonucleases matched the level required for synthetic circuit prototyping. We demonstrated the benefits and versatility of dCas9 and Csy4 through the use of RNA circuitry based on a combination of single guide RNAs, small transcriptional activator RNAs and toehold switches. For instance, we show that Csy4 processing increased fourfold the dynamic range of a previously published AND-logic gate. Additionally, blending the CRISPR-enhanced extracts enabled us to reduce leakage in a multiple inputs gate, and to extend the type of Boolean functions available for RNA-based circuits, such as NAND-logic. Finally, the use of dual transcriptional and translational reporters for the engineering of RNA-based circuits, allowed us to gain better insight into their underlying mechanisms. We hope this work will facilitate the adoption of advanced processing tools for RNA-based circuit prototyping in a cell-free environment.

synthetic biology↗

bioRxiv2019

Cell-free prototyping of AND-logic gates based on heterogeneous RNA activators

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages like short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multi-level regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterise the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analysed a prototype gate in vitro as well as in silico, employing parameterised ordinary differential equations (ODEs), where parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. Based on this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.\n\nAbbreviationsTX-TL (transcription-translation), ODEs (ordinary differential equations), STARs (small transcriptional activator RNAs), MCMC (Markov chain Monte Carlo).

synthetic biology↗