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As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering. Science , this issue p. 10.1126/science.aac7341 A programming language is devised for biological regulatory circuits. Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits for Escherichia coli (880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.

The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.Vaidyanathan et al. present a heuristic algorithm for the selection of fluorescent reporters in the context of single-cell analysis. They present a tool to enable biologists to design multi-colour fluorophore panels based on specific equipment’s configurations. The authors demonstrate the efficacy of their algorithm by comparing computational predictions with experimental observations.

Synthetic genetic regulatory networks (or genetic circuits) can operate in complex biochemical environments to process and manipulate biological information to produce a desired behavior. The ability to engineer such genetic circuits has wide-ranging applications in various fields such as therapeutics, energy, agriculture, and environmental remediation. However, engineering multilevel genetic circuits quickly and reliably is a big challenge in the field of synthetic biology. This difficulty can partly be attributed to the growing complexity of biology. But some of the predominant challenges include the absence of formal specifications—that describe precise desired behavior of these biological systems, as well as a lack of computational and mathematical frameworks—that enable rapid in-silico design and synthesis of genetic circuits. This thesis introduces two major frameworks to reliably design genetic circuits. The first implementation focuses on a framework that enables synthetic biologists to encode Boolean logic functions into living cells. Using high-level hardware description language to specify the desired behavior of a genetic logic circuit, this framework describes how, given a library of genetic gates, logic synthesis can be applied to synthesize a multilevel genetic circuit, while accounting for biological constraints such as 'signal matching', 'crosstalk', and 'genetic context effects'. This framework has been implemented in a tool called Cello, which was applied to design 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. Across all these circuits, 92% of the output states functioned as predicted. The second implementation focuses on a framework to design complex genetic systems where the focus is on how the system behaves over time instead of its behavior at steady-state. Using Signal Temporal Logic (STL)—a formalism used to specify properties of dense-time real-valued signals, biologists can specify very precise temporal behaviors of a genetic system. The framework describes how genetic circuits that are built from a well characterized library of DNA parts, can be scored by quantifying the 'degree of robustness' of in-silico simulations against an STL formula. Using formal verification, experimental data can be used to validate these in-silico designs. In this framework, the design space is also explored to predict external controls (such as approximate small molecule concentrations) that might be required to achieve a desired temporal behavior. This framework has been implemented in a tool called Phoenix.

Standards play a crucial role in ensuring consistency, interoperability, and efficiency of communication across various disciplines. In the field of synthetic biology, the Synthetic Biology Open Language (SBOL) Visual standard was introduced in 2013 to establish a structured framework for visually representing genetic designs. Over the past decade, SBOL Visual has evolved from a simple set of 21 glyphs into a comprehensive diagrammatic language for biological designs. This perspective reflects on the first ten years of SBOL Visual, tracing its evolution from inception to version 3.0. We examine the standard's adoption over time, highlighting its growing use in scientific publications, the development of supporting visualization tools, and ongoing efforts to enhance clarity and accessibility in communicating genetic design information. While trends in adoption show steady increases, achieving full compliance and use of best practices will require additional efforts. Looking ahead, the continued refinement of SBOL Visual and broader community engagement will be essential to ensuring its long-term value as the field of synthetic biology develops.

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as emission and excitation spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through. Competing Interest Statement The authors have declared no competing interest. Footnotes * http://fpselection.org/

The dynamic behavior of synthetic gene circuits plays a key role in ensuring their correct function. Although there has been substantial work on modeling dynamic behavior after circuit construction, the forward engineering of dynamic behavior remains a major challenge. Previous engineering methods have focused on quantifying average behaviors of circuits over an extended time window, however this provides a static characterization of behavior that is a poor predictor of dynamics. Here we present a method for characterizing the dynamic behavior of synthetic gene circuits, using parameter inference of dynamical system models applied to time-series measurements of cell cultures growing in microtiter plates. We demonstrate that the behaviors of simple devices can be characterized dynamically and used to predict the behaviors of more complex circuits. Specifically, we compose 23 biological parts into 9 devices and use them to design 9 synthetic gene circuits in E. coli that provide core functionality for engineering cell behavior at the population level, including relays, receivers and a degrader. We embody our method in a software package and corresponding programming language. Our method supports the notion of an inference graph for iterative inference of models as new circuits are constructed, without the need to infer all models from scratch, and lays the foundation for characterizing large libraries of synthetic gene circuits in a scalable manner. Footnotes * - Streamlined main text slightly. - Moved one main text figure to the supplement. - Improved description of software associated with the manuscript. - Updated author ordering

Signal Temporal Logic (STL) is a formal language for describing a broad range of real-valued, temporal properties in cyber-physical systems. While there has been extensive research on verification and control synthesis from STL requirements, there is no formal framework for comparing two STL formulae. In this paper, we show that under mild assumptions, STL formulae admit a metric space. We propose two metrics over this space based on i) the Pompeiu-Hausdorff distance and ii) the symmetric difference measure, and present algorithms to compute them. Alongside illustrative examples, we present applications of these metrics for two fundamental problems: a) design quality measures: to compare all the temporal behaviors of a designed system, such as a synthetic genetic circuit, with the \"desired\" specification, and b) loss functions: to quantify errors in Temporal Logic Inference (TLI) as a first step to establish formal performance guarantees of TLI algorithms.

A two-zone bioreactor was developed for simultaneous saccharification and extractive fermentation of lignocellulosic materials into lactic acid. The system is composed of an immobilized cell reactor, a separate column reactor containing the lignocellulosic substrate and a hollow-fiber membrane. It is operated by recirculating the cell free enzyme (cellulase) solution from the immobilized cell reactor to the column reactor through the membrane. The enzyme and microbial reactions thus occur at separate locations, yet simultaneously. This design provides flexibility in reactor operation. It simplifies the separation of the solid substrate from the microorganism. It makes a fed-batch or continuous operation feasible. Most importantly, the simultaneous product removal of inhibitory lactic acid can be easily achieved in this system because the product stream is free of microorganism and solid substrate. This reactor system was tested using pretreated switchgrass as the substrate. It was operated under a fed-batch mode with continuous removal of lactic acid by solvent extraction. The overall lactic acid yield obtainable from this bioreactor system is 77% of the theoretical. Recent advancement in acid hydrolysis reactor technology has progressed to the point where it can compete with the enzymatic process. The focus of the new development is the emergence of a counter-current shrinking-bed reactor. A theoretical model was developed for a counter-current shrinking-bed reactor. The results of the model strongly support the earlier findings of the NREL's experimental work based on bench scale simulated counter-current reactor system. The simulation results of bed-shrinking reactor predict that glucose yield above 90% and near quantitative pentose yield (97–99%) with sugar concentrations between 2.0–3.0 g/100 mL for both, are attainable under optimum set of operating conditions in acid hydrolysis of cellulose and hemicellulose respectively. A novel aspect of a well-known Lactobacillus strain was uncovered that it can ferment xylose as efficiently as glucose. This strain is a registered organism, extremely stable upon long term operation. A pH level about 6.0 was found to be the optimum. The yield (g/g) of lactic acid obtained from xylose is in excess of 80% with initial volumetric productivity of 0.38 g/L hr. Very low amounts of side-products, acetate and ethanol are detected. It shows high tolerance for lactic acid as well as extraneous toxins. In addition to xylose, it can ferment all other minor sugars in hemicellulose except arabinose.

This study evaluated initial antihypertensive drug prescription patterns in Indian healthcare settings. An observational, cross‐sectional, prospective prescription registry analyzed prescriptions for 4723 newly diagnosed hypertension patients. Additionally, it investigated the extent to which physicians adhered to either European or Indian hypertension guidelines. Angiotensin receptor blockers (ARBs) were the most commonly prescribed drugs, given to 79% of patients, followed by calcium channel blockers (CCBs) at 55%. Diuretics and beta‐blockers (BBs) were prescribed to 27% and 17% of patients, respectively. Monotherapy was administered to 35% of patients, while combination therapies were more prevalent, with dual therapy at 51% and regimens involving three or more drugs prescribed to 14%. Among multi‐drug treatments (n = 3082, 65%), 98% received fixed‐dose combination tablets. The most common combinations were ARB + CCB (26%), ARB + diuretic (12%), and ARB + CCB + diuretic (8%). Key predictors for an increasing number of prescribed drugs included statin use/dyslipidemia, age, blood pressure level, and diabetes. Non‐adherence to hypertension guidelines was evident as 1364 patients classified from moderate to very high risk received monotherapy. Of these, 496 patients had grade 2 or 3 hypertension. Additionally, 88 patients received the undesirable combination of ACEi + ARB, and 267 (15.9%) type 2 diabetes mellitus (T2DM) patients did not receive RAS‐blockers (146 on monotherapy). The findings reveal a trend toward utilizing ARBs, CCBs, and combination tablets, indicating improved adherence to guidelines. However, a significant number of patients did not receive appropriate treatment, highlighting areas for improvement in prescription practices.