scholarly journals Generating effective models and parameters for RNA genetic circuits

2015 ◽  
Author(s):  
Chelsea Y Hu ◽  
Jeffrey D Varner ◽  
Julius B Lucks
Keyword(s):  
Gene Expression ◽  
Circuit Design ◽  
Core Gene ◽  
Equation Model ◽  
Genetic Circuit ◽  
Test Case ◽  
Genetic Circuits ◽  
Modeling Framework ◽  
Broad Array ◽  
Effective Models

RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the thirteen parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parameterization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems.

scholarly journals Phase space characterization for gene circuit design

2019 ◽  
Author(s):  
Macarena A. Muñoz Silva ◽  
Tamara Matute ◽  
Isaac Nuñez ◽  
Ambrosio Valdes ◽  
Carlos A. Ruiz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phase Space ◽  
Circuit Design ◽  
Piecewise Linear ◽  
Genetic Circuit ◽  
Cell Physiology ◽  
Gene Circuits ◽  
Linear Dynamics ◽  
Relative Expression ◽  
Cellular Context

ABSTRACTGenetic circuit design requires characterization of the dynamics of synthetic gene expression. This is a difficult problem since gene expression varies in complex ways over time and across different contexts. Here we present a novel method for characterizing the dynamics of gene expression with a few parameters that account for changes in cellular context (host cell physiology) and compositional context (adjacent genes). The dynamics of gene circuits were characterized by a trajectory through a multi-dimensional phase space parameterized by the expression levels of each of their constituent transcriptional units (TU). These trajectories followed piecewise linear dynamics, with each dynamical regime corresponding to different growth regimes, or cellular contexts. Thus relative expression rates were changed by transitions between growth regimes, but were constant in each regime. We present a plausible two-factor mathematical model for this behavior based on resource consumption. By analyzing different combinations of TUs, we then showed that relative expression rates were significantly affected by the neighboring TU (compositional context), but maintained piecewise linear dynamics across cellular and compositional contexts. Taken together these results show that TU expression dynamics could be predicted by a reference TU up to a context dependent scaling factor. This model provides a framework for design of genetic circuits composed of TUs. A common sharable reference TU may be chosen and measured in the cellular contexts of interest. The output of each TU in the circuit may then be predicted from a simple function of the output of the reference TU in the given cellular context. This will aid in genetic circuit design by providing simple models for the dynamics of gene circuits and their constituent TUs.

scholarly journals Next-generation biocomputing: mimicking artificial neural network with genetic circuits

2021 ◽  
Author(s):  
Leo Chi U Seak ◽  
Owen Lok In Lo ◽  
Wade Chun-Wai Suen ◽  
Ming-Tsung Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Circuit Design ◽  
De Novo ◽  
Design Method ◽  
Logic Gate ◽  
Mathematical Expression ◽  
Genetic Circuit ◽  
Genetic Circuits ◽  
Artificial Neural

AbstractArtificial neural network (ANN) is nowadays one of the most used and researched computational methods. In the field of biocomputing, however, synthetic biologists are still using logic gate technologies to design genetic circuits. We here propose and computationally validate a novel method to mimic ANN with genetic circuits. We first describe the flow and the mathematical expression of this genetic circuit design and then we provide in silico proof to support the functionality of our method in regression and classification analyses. We believe that this de novo genetic circuit design method would have wide applications in biotechnology.

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

2013 ◽  
Vol 17 (6) ◽  
pp. 878-892 ◽  
Author(s):  
Alec AK Nielsen ◽  
Thomas H Segall-Shapiro ◽  
Christopher A Voigt
Keyword(s):  
Gene Expression ◽  
Circuit Design ◽  
Genetic Circuit ◽  
Deep Part

scholarly journals Distributed implementation of Boolean functions by transcriptional synthetic circuits

2020 ◽  
Author(s):  
M. Ali Al-Radhawi ◽  
Anh Phong Tran ◽  
Elizabeth A. Ernst ◽  
Tianchi Chen ◽  
Christopher A. Voigt ◽  
...  
Keyword(s):  
Circuit Design ◽  
Boolean Functions ◽  
Cell Types ◽  
Genetic Circuit ◽  
Synthesis Methods ◽  
Genetic Circuits ◽  
Chemical Inducers ◽  
Distributed Approach ◽  
Multiple Cell ◽  
Rational Construction

AbstractStarting in the early 2000s, a sophisticated technology has been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually-orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, which communicate among themselves using diffusible small molecules (DSMs) and each of which implements a small sub-circuit. Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions.The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs.For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM’s, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software.

scholarly journals A Synthetic Riboswitch to Regulate Haloarchaeal Gene Expression

2021 ◽  
Vol 12 ◽  
Author(s):  
Johannes Born ◽  
Kerstin Weitzel ◽  
Beatrix Suess ◽  
Felicitas Pfeifer
Keyword(s):  
Gene Expression ◽  
Reporter Gene ◽  
Culture Media ◽  
Fold Increase ◽  
Start Codon ◽  
Genetic Circuit ◽  
Genetic Circuits ◽  
Domains Of Life ◽  
Dose Dependent ◽  
Ribosomal Binding Site

In recent years, synthetic riboswitches have become increasingly important to construct genetic circuits in all three domains of life. In bacteria, synthetic translational riboswitches are often employed that modulate gene expression by masking the Shine-Dalgarno (SD) sequence in the absence or presence of a cognate ligand. For (halo-)archaeal translation, a SD sequence is not strictly required. The application of synthetic riboswitches in haloarchaea is therefore limited so far, also because of the molar intracellular salt concentrations found in these microbes. In this study, we applied synthetic theophylline-dependent translational riboswitches in the archaeon Haloferax volcanii. The riboswitch variants A through E and E∗ were chosen since they not only mask the SD sequence but also the AUG start codon by forming a secondary structure in the absence of the ligand theophylline. Upon addition of the ligand, the ribosomal binding site and start codon become accessible for translation initiation. Riboswitch E mediated a dose-dependent, up to threefold activation of the bgaH reporter gene expression. Raising the salt concentration of the culture media from 3 to 4 M NaCl resulted in a 12-fold increase in the switching capacity of riboswitch E, and switching activity increased up to 26-fold when the cultivating temperature was reduced from 45 to 30°C. To construct a genetic circuit, riboswitch E was applied to regulate the synthesis of the transcriptional activator GvpE allowing a dose-dependent activation of the mgfp6 reporter gene under PpA promoter control.

scholarly journals High-performance chemical- and light-inducible recombinases in mammalian cells and mice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin H. Weinberg ◽  
Jang Hwan Cho ◽  
Yash Agarwal ◽  
N. T. Hang Pham ◽  
Leidy D. Caraballo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
High Performance ◽  
Genome Engineering ◽  
Genetic Circuits ◽  
Control Of Gene Expression ◽  
Expression Control ◽  
Gene Expression Control ◽  
High Performance Systems ◽  
Spatiotemporal Control

Abstract Site-specific DNA recombinases are important genome engineering tools. Chemical- and light-inducible recombinases, in particular, enable spatiotemporal control of gene expression. However, inducible recombinases are scarce due to the challenge of engineering high performance systems, thus constraining the sophistication of genetic circuits and animal models that can be created. Here we present a library of >20 orthogonal inducible split recombinases that can be activated by small molecules, light and temperature in mammalian cells and mice. Furthermore, we engineer inducible split Cre systems with better performance than existing systems. Using our orthogonal inducible recombinases, we create a genetic switchboard that can independently regulate the expression of 3 different cytokines in the same cell, a tripartite inducible Flp, and a 4-input AND gate. We quantitatively characterize the inducible recombinases for benchmarking their performances, including computation of distinguishability of outputs. This library expands capabilities for multiplexed mammalian gene expression control.

scholarly journals CRIMoClo plasmids for modular assembly and orthogonal chromosomal integration of synthetic circuits in Escherichia coli

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Stefano Vecchione ◽  
Georg Fritz
Keyword(s):  
Escherichia Coli ◽  
Synthetic Biology ◽  
Integrated Circuits ◽  
High Efficiency ◽  
Genetic Circuit ◽  
Dna Assembly ◽  
Chromosomal Integration ◽  
Golden Gate ◽  
Genetic Circuits ◽  
E Coli

Abstract Background Synthetic biology heavily depends on rapid and simple techniques for DNA engineering, such as Ligase Cycling Reaction (LCR), Gibson assembly and Golden Gate assembly, all of which allow for fast, multi-fragment DNA assembly. A major enhancement of Golden Gate assembly is represented by the Modular Cloning (MoClo) system that allows for simple library propagation and combinatorial construction of genetic circuits from reusable parts. Yet, one limitation of the MoClo system is that all circuits are assembled in low- and medium copy plasmids, while a rapid route to chromosomal integration is lacking. To overcome this bottleneck, here we took advantage of the conditional-replication, integration, and modular (CRIM) plasmids, which can be integrated in single copies into the chromosome of Escherichia coli and related bacteria by site-specific recombination at different phage attachment (att) sites. Results By combining the modularity of the MoClo system with the CRIM plasmids features we created a set of 32 novel CRIMoClo plasmids and benchmarked their suitability for synthetic biology applications. Using CRIMoClo plasmids we assembled and integrated a given genetic circuit into four selected phage attachment sites. Analyzing the behavior of these circuits we found essentially identical expression levels, indicating orthogonality of the loci. Using CRIMoClo plasmids and four different reporter systems, we illustrated a framework that allows for a fast and reliable sequential integration at the four selected att sites. Taking advantage of four resistance cassettes the procedure did not require recombination events between each round of integration. Finally, we assembled and genomically integrated synthetic ECF σ factor/anti-σ switches with high efficiency, showing that the growth defects observed for circuits encoded on medium-copy plasmids were alleviated. Conclusions The CRIMoClo system enables the generation of genetic circuits from reusable, MoClo-compatible parts and their integration into 4 orthogonal att sites into the genome of E. coli. Utilizing four different resistance modules the CRIMoClo system allows for easy, fast, and reliable multiple integrations. Moreover, utilizing CRIMoClo plasmids and MoClo reusable parts, we efficiently integrated and alleviated the toxicity of plasmid-borne circuits. Finally, since CRIMoClo framework allows for high flexibility, it is possible to utilize plasmid-borne and chromosomally integrated circuits simultaneously. This increases our ability to permute multiple genetic modules and allows for an easier design of complex synthetic metabolic pathways in E. coli.

scholarly journals Characterization, modelling and mitigation of gene expression burden in mammalian cells

2019 ◽  
Author(s):  
T Frei ◽  
F Cella ◽  
F Tedeschi ◽  
J Gutierrez ◽  
GB Stan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Genome Engineering ◽  
Host Cells ◽  
Genetic Circuits ◽  
Circuit Performance ◽  
Resource Requirements ◽  
Synthetic Circuit ◽  
Resource Aware

AbstractDespite recent advances in genome engineering, the design of genetic circuits in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here we demonstrate that competition for limited transcriptional and translational resources dynamically couples otherwise independent co-expressed exogenous genes, leading to diminished performance and contributing to the divergence between intended and actual function. We also show that the expression of endogenous genes is likewise impacted when genetic payloads are expressed in the host cells. Guided by a resource-aware mathematical model and our experimental finding that post-transcriptional regulators have a large capacity for resource redistribution, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the novel use of endogenous miRNAs as integral components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.

scholarly journals Generative modeling of single-cell population time series for inferring cell differentiation landscapes

2020 ◽  
Author(s):  
Grace H.T. Yeo ◽  
Sachit D. Saksena ◽  
David K. Gifford
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Differentiation ◽  
Single Cell ◽  
Cell Fate ◽  
Lineage Tracing ◽  
Model Framework ◽  
Modeling Framework ◽  
Generative Modeling ◽  
Model Complex

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.

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