scholarly journals Accurate prediction of genetic circuit behavior requires multidimensional characterization of parts

Author(s):  
Galen Dods ◽  
Mariana Gómez-Schiavon ◽  
Hana El-Samad ◽  
Andrew H. Ng

AbstractMathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. With such multidimensional characterizations, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.

2020 ◽  
Author(s):  
Guillermo Yáñez Feliú ◽  
Benjamín Earle Gómez ◽  
Verner Codoceo Berrocal ◽  
Macarena Muñoz Silva ◽  
Isaac N. Nuñez ◽  
...  

AbstractCharacterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different labs and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parameterize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated to circuit part composition via SBOL. We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.


2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Stefano Vecchione ◽  
Georg Fritz

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.


2020 ◽  
pp. 147807712096337
Author(s):  
Gizem Gumuskaya

In this paper, we argue that synthetic biology can help us employ living systems’ unique capacity for self-construction and biomaterial production toward developing novel architectural fabrication paradigms, in which both the raw material production and its refinement into a target structure can be merged into a single computational process embedded in the living structure itself. To demonstrate, here we introduce bioPheme, a novel biofabrication method for engineering bacteria to build biomaterial(s) of designer’s choice into arbitrary 2D geometries specified via transient UV tracing. To this end, we present the design, construction, and testing of the enabling synthetic DNA circuit, which, once inserted into a bacterial colony, allows the bacteria to execute spatial computation by interacting with one another based on the if-then rules encoded in this circuit. At the heart of this genetic circuit is a pair of UV sensor – actuator, and a pair of cell-to-cell signal transmitter – receptor modules, created with genes extracted from the virus λ Phage and marine bacterium Vibrio fischeri, respectively. These modules are wired together to help designers engineer bacteria to build macro-scale structures with seamlessly integrated biomaterials, thereby bridge the molecular and architectural scales. In this way, a bacterial lawn can be programmed to produce different objects with complementary biomaterial compositions, such as a biomineralized superstructure and an elastic tissue filling in-between. In summary, this paper focuses on how scientists’ increasing ability to harness the innate computational capacity of living cells can help designers create self-constructing structures for architectural biofabrication. Through the discussions in this paper, we aim to initiate a shift in today’s biodesign practices toward a greater appreciation and adoption of bottom-up governance of living structures. We are confident that such a paradigm shift will allow for more efficient and sustainable biofabrication systems in the 4th industrial revolution and beyond.


2021 ◽  
Author(s):  
Gonzalo Vidal ◽  
Carlos Vidal-Céspedes ◽  
Timothy James Rudge

Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.


2020 ◽  
Author(s):  
Behide Saltepe ◽  
Eray Ulaş Bozkurt ◽  
Murat Alp Güngen ◽  
A. Ercüment Çiçek ◽  
Urartu Özgür Şafak Şeker

AbstractWhole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and easy-to-use assessment methods, a considerable amount of WCBs retains certain drawbacks such as long response time, low precision and accuracy. Furthermore, the output signal level does not correspond to a specific analyte concentration value but shows comparative quantification. Here, we utilized a neural network-based architecture to improve the aforementioned features of WCBs and engineered a gold sensing WCB which has a long response time (18 h). Two Long-Short Term-Memory (LSTM)-based networks were integrated to assess both ON/OFF and concentration dependent states of the sensor output, respectively. We demonstrated that binary (ON/OFF) network was able to distinguish between ON/OFF states as early as 30 min with 78% accuracy and over 98% in 3 h. Furthermore, when analyzed in analog manner, we demonstrated that network can classify the raw fluorescence data into pre-defined analyte concentration groups with high precision (82%) in 3 h. This approach can be applied to a wide range of WCBs and improve rapidness, simplicity and accuracy which are the main challenges in synthetic biology enabled biosensing.


2020 ◽  
Author(s):  
Cameron McBride ◽  
Domitilla Del Vecchio

AbstractSynthetic biology applications have the potential to have lasting impact; however, there is considerable difficulty in scaling up engineered genetic circuits. One of the current hurdles is resource sharing, where different circuit components become implicitly coupled through the host cell’s pool of resources, which may destroy circuit function. One potential solution around this problem is to distribute genetic circuit components across multiple cell strains and control the cell population size using a population controller. In these situations, perturbations in the availability of cellular resources, such as due to resource sharing, will affect the performance of the population controller. In this work, we model a genetic population controller implemented by a genetic circuit while considering perturbations in the availability of cellular resources. We analyze how these intracellular perturbations and extracellular disturbances to cell growth affect cell population size. We find that it is not possible to tune the population controller’s gain such that the population density is robust to both extracellular disturbances and perturbations to the pool of available resources.


2020 ◽  
Author(s):  
Samuel Fajemilua ◽  
Solomon Bada ◽  
M. Ahsanul Islam

AbstractContaminants of emerging concern (CEC) such as tetracycline, erythromycin, and salicylic acid in groundwater can seriously endanger the environment and human health due to their widespread and everlasting harmful effects. Thus, continuous monitoring of various CEC concentrations in groundwater is essential to ensure the safety, security, and biodiversity of natural habitats. CECs can be detected using whole-cell biosensors for environmental surveillance and monitoring purposes, as they provide a cheaper and more robust alternative to traditional and expensive analytical techniques. In this study, various genetic circuit designs are considered to model three biosensors using the genetic design automation (GDA) software, iBioSim. The genetic circuits were designed to detect multiple CECs, including atrazine, salicylic acid, and tetracycline simultaneously to produce quantitative fluorescent outputs. The biosensor responses and the viability of the genetic circuit designs were further analysed using ODE-based mathematical simulations in iBioSim. The designed circuits and subsequent biosensor modelling presented here, thus, not only show the usefulness and importance of GDA tools, but also highlight their limitations and shortcomings that need to overcome in the future; thereby, providing a practical guidance for further improvement of such tools, so that they can be more effectively and routinely used in synthetic biology research.


2019 ◽  
Vol 47 (19) ◽  
pp. 10464-10474 ◽  
Author(s):  
Natalia Barger ◽  
Phyana Litovco ◽  
Ximing Li ◽  
Mouna Habib ◽  
Ramez Daniel

Abstract Bioluminescence is visible light produced and emitted by living cells using various biological systems (e.g. luxCDABE cassette). Today, this phenomenon is widely exploited in biological research, biotechnology and medical applications as a quantitative technique for the detection of biological signals. However, this technique has mostly been used to detect a single input only. In this work, we re-engineered the complex genetic structure of luxCDABE cassette to build a biological unit that can detect multi-inputs, process the cellular information and report the computation results. We first split the luxCDABE operon into several parts to create a genetic circuit that can compute a soft minimum in living cells. Then, we used the new design to implement an AND logic function with better performance as compared to AND logic functions based on protein-protein interactions. Furthermore, by controlling the reverse reaction of the luxCDABE cassette independently from the forward reaction, we built a comparator with a programmable detection threshold. Finally, we applied the redesigned cassette to build an incoherent feedforward loop that reduced the unwanted crosstalk between stress-responsive promoters (recA, katG). This work demonstrates the construction of genetic circuits that combine regulations of gene expression with metabolic pathways, for sensing and computing in living cells.


Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4203 ◽  
Author(s):  
William Chen ◽  
Ka Wai Eric Cheng ◽  
Jianwei Shao

Light emitted diode (LED) is becoming more popular in the illumination field, and the design of LED lighting is generally made to provide illumination at lower power usage, helping save energy. A power electronic converter is needed to provide the power conversion for these LEDs to meet high efficiency, reduce components, and have low voltage ripple magnitude. The power supply for LED is revisited in this paper. The LEDs connected in series with diode, transistor, or inductor paths are examined. The formulation for each of the cases is described, including the classical converters of buck, boost, buck–boost, and Ćuk. The circuit reductions of the classic circuit, circuit without the capacitor, and without a freewheeling diode are studied. Using LED to replace freewheeling diodes is proposed for circuit component reduction. General equations for different connection paths have been developed. The efficiency and output ripple amplitude of the proposed power converters are investigated. Analytical study shows that the efficiency of proposed circuits can be high and voltage ripple magnitude of proposed circuits can be low. The results show that the proposed circuit topologies can be easily adapted to design LED lighting, which can meet the criteria of high efficiency, minimum components, and low-voltage ripple magnitude at the same time.


2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Michael Fitzgerald ◽  
Mark Livingston ◽  
Chelsea Gibbs ◽  
Tara L Deans

Abstract Approaches in mammalian synthetic biology have transformed how cells can be programmed to have reliable and predictable behavior, however, the majority of mammalian synthetic biology has been accomplished using immortalized cell lines that are easy to grow and easy to transfect. Genetic circuits that integrate into the genome of these immortalized cell lines remain functional for many generations, often for the lifetime of the cells, yet when genetic circuits are integrated into the genome of stem cells gene silencing is observed within a few generations. To investigate the reactivation of silenced genetic circuits in stem cells, the Rosa26 locus of mouse pluripotent stem cells was modified to contain docking sites for site-specific integration of genetic circuits. We show that the silencing of genetic circuits can be reversed with the addition of sodium butyrate, a histone deacetylase inhibitor. These findings demonstrate an approach to reactivate the function of genetic circuits in pluripotent stem cells to ensure robust function over many generations. Altogether, this work introduces an approach to overcome the silencing of genetic circuits in pluripotent stem cells that may enable the use of genetic circuits in pluripotent stem cells for long-term function.


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