Design principles of neuromorphic computing using genetic circuits

Genetic circuits and reaction cascades are of great importance for synthetic biology, biochemistry, and bioengineering. An open question is how to maximize the modularity of their design to enable the integration of different reaction networks and to optimize their scalability and flexibility. One option is encapsulation within liposomes which enables chemical reactions to proceed in well-isolated environments. Here we adapt liposome encapsulation to enable the modular, controlled compartmentalization of genetic circuits and cascades. We demonstrate that it is possible to engineer genetic circuit-containing synthetic minimal cells (synells) to contain multiple-part genetic cascades, and that these cascades can be controlled by external signals as well as inter-liposomal communication without cross-talk. We also show that liposomes containing different cascades can be fused in a controlled way so that the products of incompatible reactions can be brought together. Synells thus enable more modular creation of synthetic biology cascades, an essential step towards their ultimate programmability.

Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology, synthetic biology, and molecular programming is chemical reaction networks (CRNs) which formalizes coupled chemical reactions in a well-mixed solution. CRNs are pivotal for our understanding of biological regulatory and metabolic networks, as well as for programming engineered molecular behavior. Although it is clear that small CRNs are capable of complex dynamics and computational behavior, it remains difficult to explore the space of CRNs in search for desired functionality. We use Alloy, a tool for expressing structural constraints and behavior in software systems, to enumerate CRNs with declaratively specified properties. We show how this framework can enumerate CRNs with a variety of structural constraints including biologically motivated catalytic networks and metabolic networks, and seesaw networks motivated by DNA nanotechnology. We also use the framework to explore analog function computation in rate-independent CRNs. By computing the desired output value with stoichiometry rather than with reaction rates (in the sense that X → Y+Y computes multiplication by 2), such CRNs are completely robust to the choice of reaction rates or rate law. We find the smallest CRNs computing the max, minmax, abs and ReLU (rectified linear unit) functions in a natural subclass of rate-independent CRNs where rate-independence follows from structural network properties.

Synthetic Biology is an interdisciplinary field that utilizes well-established engineering principles, ranging from electrical, control and computer systems, for analyzing the biological systems, such as biological circuits, enzymes, pathways and controllers. Traditionally, these biological systems, i.e., the genetic circuits are analyzed using paper-and-pencil proofs and computer-based simulations techniques. However, these methods cannot provide accurate results due to their inherent limitations such as human error-proneness, round-off errors and the unverified algorithms present in the core of the tools, providing such analyses. In this paper, we propose to use higher-order-logic theorem proving as a complementary technique for analyzing these systems and thus overcome the above-mentioned issues. In particular, we propose a higher-order-logic theorem proving based framework to formally reason about the genetic circuits used in synthetic biology. The main idea is to, first, model the continuous dynamics of the genetic circuits using differential equations. The next step is to obtain the systems' transfer function from their corresponding block diagram representations. Finally, the transfer function based analysis of these differential equation based models is performed using the Laplace transform. To illustrate the practical utilization of our proposed framework, we formally analyze the genetic circuits of activated and repressed expressions of protein.

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications.

In recent years, synthetic biology develops rapidly, the purpose of which is to create beneficial products or organisms with special functions. Genetic circuits are dynamic regulating systems of organisms controlling their own life processes. According to specific rules and grammar, synthetic biologists try to design artificial genetic circuits to achieve their goals. In our research, we designed grammar rules on basis of the context-free grammar, imported a part library which we acquired from IGEM database and then we processed, and constructed a genetic circuit for expressing TetR in GenoCAD, which is a free web-based application for synthetic biology. Finally, we generated a plasmid map on the PlasMapper, and we can see the concrete information of the plasmid. We creatively designed the genetic circuit to express the TetR. The work provides a new dynamic regulating system for synthetic biology. We think the genetic circuit will help us predict the biological regulator process.

Recent developments demonstrate that the combination of microbiology with micro- and nanoelectronics is a successful approach to develop new miniaturized sensing devices and other technologies. In the last decade, there has been a shift from the optimization of the abiotic components, for example, the chip, to the improvement of the processing capabilities of cells through genetic engineering. The synthetic biology approach will not only give rise to systems with new functionalities, but will also improve the robustness and speed of their response towards applied signals. To this end, the development of new genetic circuits has to be guided by computational design methods that enable to tune and optimize the circuit response. As the successful design of genetic circuits is highly dependent on the quality and reliability of its composing elements, intense characterization of standard biological parts will be crucial for an efficient rational design process in the development of new genetic circuits. Microengineered devices can thereby offer a new analytical approach for the study of complex biological parts and systems. By summarizing the recent techniques in creating new synthetic circuits and in integrating biology with microdevices, this review aims at emphasizing the power of combining synthetic biology with microfluidics and microelectronics.

Optimization-Based Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) Tools: Computational Approach to Synthetic Biology

BackgroundThe use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors.ResultsSynchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned.ConclusionsThe genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.

Synthetic biology uses genetically encoded devices and circuits to implement novel complex functions in living cells and organisms. A hallmark of these genetic circuits is the interaction among their individual parts, according to predefined rules, to process cellular information and produce a circuit output or response. As the number of individual components in a genetic circuit increases, so does the number of interactions needed to achieve the correct behavior, and hence, a greater need to fine-tune the levels of expression of each component. Transcriptional promoters play a key regulatory role in genetic circuits, as they influence the levels of RNA and proteins produced. In multicellular organisms, such as plants, they can also determine developmental, spatial, and tissue-specific patterns of gene expression. The 35S promoter from the Cauliflower Mosaic Virus (CaMV 35S) is widely used in plant biotechnology to direct high levels of gene expression in a variety of plant species. We produced a library of 21 variants of the CaMV 35S promoter by introducing all single nucleotide substitutions to the promoter's TATA box sequence. We then characterized the activity of all variants in homozygous transgenic plants and showed that some of these variants have lower activity than the wild type in plants. These promoter variants could be used to fine-tune the behavior of synthetic genetic circuits in plants.

Biological systems are inherently stochastic, a fact which is often ignored when simulating genetic circuits. Synthetic biology aims to design genetic circuits de novo, and cannot therefore afford to ignore the effects of stochastic behavior. Since computational design tools will be essential for large-scale synthetic biology, it is important to develop an understanding of the role of stochasticity in molecular biology, and incorporate this understanding into computational tools for genetic circuit design. We report upon an investigation into the combination of evolutionary algorithms and stochastic simulation for genetic circuit design, to design regulatory systems based on the Bacillus subtilis sin operon.

The construction of stable and functional synthetic circuits in bacteria is necessary in the areas of systems and synthetic biology. The common approach using plasmids to carry foreign genetic circuits offers convenience in genetic construction but is poor in genetic stability (e.g., variation in copy number). Genome recombination provides the stable genetic maintenance of synthetic circuits, but is often labor intensive and time consuming when the genetic circuits are complex and the DNA fragments become larger. The method introduced here is modified from that reported by Wanner's group and is available for integration of complex genetic circuits into the Escherichia coli chromosome.

Rational engineering of biological systems is often complicated by the complex but unwanted interactions between cellular components at multiple levels. Here we address this issue at the level of prokaryotic transcription by insulating minimal promoters and operators to prevent their interaction and enable the biophysical modeling of synthetic transcription without free parameters. This approach allows genetic circuit design with extraordinary precision and diversity, and consequently simplifies the design-build-test-learn cycle of circuit engineering to a mix-and-match workflow. As a demonstration, combinatorial promoters encoding NOT-gate functions were designed from scratch with mean errors of <1.5-fold and a success rate of >96% using our insulated transcription elements. Furthermore, four-node transcriptional networks with incoherent feed-forward loops that execute stripe-forming functions were obtained without any trial-and-error work. This insulation-based engineering strategy improves the resolution of genetic circuit technology and provides a simple approach for designing genetic circuits for systems and synthetic biology.

Compartmentalization is a hallmark of cellular systems and an ingredient actively exploited in evolution. It is also being engineered and exploited in synthetic biology, in multiple ways. While these have demonstrated important experimental capabilities, understanding design principles underpinning compartmentalization of genetic circuits has been elusive. We develop a systems framework to elucidate the interplay between the nature of the genetic circuit, the spatial organization of compartments, and their operational state (well-mixed or otherwise). In so doing, we reveal a number of unexpected features associated with compartmentalizing synthetic and template-based circuits. These include (i) the consequences of distributing circuits including trade-offs and how they may be circumvented, (ii) hidden constraints in realizing a distributed circuit, and (iii) appealing new features of compartmentalized circuits. We build on this to examine exemplar applications, which consolidate and extend the design principles we have obtained. Our insights, which emerge from the most basic and general considerations of compartmentalizing genetic circuits, are relevant in a broad range of settings.

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

The “-omics” era has brought a new set of tools and methods that have created a significant impact on the development of Metabolic Engineering and Synthetic Biology. These fields, rather than working separately, depend on each other to prosper and achieve their individual goals. Synthetic Biology aims to design libraries of genetic components (promoters, coding sequences, terminators, transcriptional factors and their binding sequences, and more), the assembly of devices, genetic circuits and even organism; in addition to obtaining quantitative information for the creation of models that can predict the behavior of biological systems (Cameron et al., 2014). Metabolic engineering seeks for the optimization of cellular processes, endemic to a specific organism, to produce a compound of interest from a substrate, preferably cheap and simple. It uses different databases, libraries of components and conditions to generate the maximum production rate of a desired chemical compound and avoiding inhibitors and conditions that affect the growth rate and other vital functions in the specific organism to achieve these goals; metabolic fluxes manipulation represents an important alternative (Stephanopoulos, 2012).