Continuous-Flow Microfluidic Devices (CFMDs), also known as biochips, provide automated and cost-effective solutions for biomolecular analysis in life science applications. Multi-operation biochemical reactions require incorporating microfluidic components into CFMDs, which may raise their design complexity and lead to Design-Objective-Constraint Compatibility (DOCC) conflicts. Existing validation methods excel in optimizing Control-Layer Pressurization Protocol (CLPP) for small networks. However, they face challenges in describing operation constraints for microfluidic large-scale integration (mLSI) experiments and addressing the undesired flow risk. To overcome these challenges, we develop Vespa, an open-source validation framework that uses logic expressions to describe operation constraints with lower human labor costs. For each operation, taking an objective list, a constraint list, a design netlist, and a super-parameter as inputs, Vespa validates the DOCC by constructing a target fluid transportation path. In addition, it introduces an undesired flow risk mitigation mechanism, issuing warnings for incorrect constraints and potential fluid undesired flow risks. The work includes over 900 real-world and synthetic benchmarks in three complexity ranges to support researchers in the field. As a result, more than 85% of benchmark experiments yield correct results within 0.3 seconds, enabling Vespa for real-time validation and integration into interactive CFMD design tools. Finally, to show Vespa’s practical efficacy and real-world impact, we create case studies using a real-world CFMD. We demonstrate that Vespa eliminates over 90% of DOCC-related wet lab tests by detecting issues and making updates before fabrication.

Synthetic consortia represent an emerging research area in synthetic biology, promising to solve various industrial challenges through the metabolic diversity, division of labor, and spatial organization inherent in microbial consortia. As synthetic biology advances into multi-cellular systems, new design strategies are essential for engineering distributed functions across networks of cells. However, existing strategies either lack scalability or require extensive reformulation, limiting their usage to various applications. In this work, we propose an application-agnostic approach to partitioning networks of interacting biological components using graph-based algorithms. We develop a three-stage algorithm, named "Oriole", that verifies and optimizes the subgroup distribution of all entities within a network, considering biological constraints and objectives for engineering these systems. We validate our algorithm on three types of networks, including 30 small-graph benchmarks, 537 regular electronic circuit designs, and 56 large circuit benchmarks. One large circuit benchmark was recently implemented experimentally. Compared with the other sub-graph partitioning solutions, the results generated by our new algorithm reduced design time from days to hours and decreased the total number of cells required for the multi-cellular system by 3%. This case study demonstrates that our algorithm provides a more efficient approach to designing multi-cellular systems.

BACKGROUND: In protein sequences-as there are 61 sense codons but only 20 standard amino acids-most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. RESULTS: We demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR's performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. CONCLUSIONS: The results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

No abstract available.

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

The lack of standardization in the specification and representation of microfluidic designs and their corresponding architectures is one of the largest hurdles faced by the developers of Microfluidic Design Automation (MDA) tools. In this paper, we introduce MINT, a Microfluidic Hardware Description Language (MHDL) for defining components and devices in a human readable manner, and ParchMint, an MDA interchange format and associated benchmark suite that can be used to compare the performance of different physical design algorithms. We further demonstrate how the introduction of MINT and ParchMint into the engineering workflow can bridge the gaps from the specification to the fabrication of microfluidic devices. While recent efforts to democratize microfluidics have been recognized by the community, there is an unfortunate lack of open source tools, design languages, and standards. Consequently, microfluidic designs shared on open platforms such as Metafluidics[15] leave conceptual gaps in terms of missing design information that are necessary to realize the “creative process flows” (Reproduce, Remix, and Test multiple systems). MINT and ParchMint are open source projects, which allows the community to contribute and extend their functionality to enable advanced algorithmic methodologies and new commercialization possibilities that differ from the vertically integrated industries we see today.

Most digital electronic circuits utilize a timing reference to synchronize the progression of signals and enable sequential memory elements. These designs may not be realizable in biological substrates due to the lack of a reliable high-frequency clock signal. Asynchronous designs eliminate the need for a clock with data encodings and request/acknowledge handshake protocols. This paper proposes a workflow to automate the design of asynchronous genetic circuits. This workflow extends genetic design tools by leveraging asynchronous logic design methods customized for this technology. This workflow is demonstrated on a genetic sensor that uses filtering and cellular communication to improve its reliability.

Digital electronic circuits have inspired synthetic biologists to program living cells with synthetic decision-making circuits by creating multilevel genetic logic gates. In both genetic and electronic circuits, logic synthesis translates an abstract functional description into a representation that can be physically implemented. However, inherent differences in genetic logic devices present new synthesis challenges. Here we provide a starting point for a growing set of biodesign automation tools to tackle this challenge in a unified way. This framework will enable direct comparison of new approaches, results that are transferable and standardized, and research into independent areas of the problem formulation with a clear path toward future integration.

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications. Our incomplete knowledge of the effects of metabolic load and biological “crosstalk” on the host cell make it difficult to construct multilevel genetic logic circuits in a single cell, limiting the scalability of engineered biological systems. Microfluidic technologies provide reliable and scalable construction of synthetic biological systems by allowing compartmentalization of cells encoding simple genetic circuits and the spatiotemporal control of communication among these cells. This control is achieved via valves on the microfluidics chip which restrict fluid flow when activated. We describe a Computer Aided Design (CAD) framework called “Fluigi” for optimizing the layout of genetic circuits on a microfluidic chip, generating the control sequence of the associated signaling fluid valves, and simulating the behavior of the configured biological circuits. We demonstrate the capabilities of Fluigi on a set of Boolean algebraic benchmark circuits found in both synthetic biology and electrical engineering and a set of assay-based benchmark circuits. The integration of microfluidics and synthetic biology has the capability to increase the scale of engineered biological systems for applications in DNA assembly, biosensors, and screening assays for novel orthogonal genetic parts.

Synthetic Biology is an engineering discipline where parts of DNA sequences are composed into novel, complex systems that execute a desired biological function. Functioning and well-behaving biological systems adhere to a certain set of biological “rules”. Data exchange standards and Bio-Design Automation (BDA) tools support the organization of part libraries and the exploration of rule-compliant compositions. In this work, we formally define a design specification language, enabling the integration of biological rules into the Synthetic Biology engineering process. The supported rules are divided into five categories: Counting , Pairing , Positioning , Orientation , and Interactions . We formally define the semantics of each rule, characterize the language's expressive power, and perform a case study in that we iteratively design a genetic Priority Encoder circuit following two alternative paradigms—rule-based and template-driven. Ultimately, we touch a method to approximate the complexity and time to computationally enumerate all rule-compliant designs. Our specification language may or may not be expressive enough to capture all designs that a Synthetic Biologist might want to describe, or the complexity one might find through experiments. However, computational support for the acquisition, specification, management, and application of biological rules is inevitable to understand the functioning of biology.

Cyber-Physical Systems are integrations of computation and physical processes and as such, will be increasingly relevant to industry and people. The complexity of designing CPS resides in their heterogeneity. Heterogeneity manifest itself in modeling their functionality as well as in the implementation platforms that include a multiplicity of components such as microprocessors, signal processors, peripherals, memories, sensors and actuators often integrated on a single chip or on a small package such as a multi-chip module. We need a methodology, tools and environments where heterogeneity can be dealt with at all levels of abstraction and where different tools can be integrated. We present here Platform-Based Design as the CPS methodology of choice and metro II, a design environment that supports it. We present the metamodeling approach followed in metro II, how to couple the functionality and implementation platforms of CPS, and the simulation technology that supports the analysis of CPS and of their implementation. We also present examples of use and the integration of metro II with another popular design environment developed at Verimag, BIP.

We present a framework that allows us to construct and formally analyze the behavior of synthetic gene circuits from specifications in a high level language used in describing electronic circuits. Our back-end synthesis tool automatically generates genetic-regulatory network (GRN) topology realizing the specifications with assigned biological “parts” from a database. We describe experimental procedures to acquire characterization data for the assigned parts and construct mathematical models capturing all possible behaviors of the generated GRN. We delineate algorithms to create finite abstractions of these models, and novel analysis techniques inspired from model-checking to verify behavioral specifications using Linear Temporal Logic (LTL) formulae.

With the growing complexity of synthetic biological circuits, robust and systematic methods are needed for design and test. Leveraging lessons learned from the semiconductor and design automation industries, synthetic biologists are starting to adopt computer-aided design and verification software with some success. However, due to the great challenges associated with designing synthetic biological circuits, this nascent approach has to address many problems not present in electronic circuits. In this session, three leading synthetic biologists will share how they have developed software tools to help design and verify their synthetic circuits, the unique challenges they face, and their insights into the next generation of tools for synthetic biology.

The construction of composite genetic devices from primitive parts is a key activity in synthetic biology. Currently there does not exist a formal method to specify constraints on the construction of these devices. These constraints would help enable an automated design flow from device specification to physical assembly. This paper examines the laboratory creation of variations of a particular genetic device called a phagemid. We illustrate how lessons learned empirically from the non-functional designs can be captured formally as constraints in a newly created domain specific language called “Eugene”. These constraints will prevent many faulty constructions automatically in the future saving time and money while increasing design abstraction and productivity.

Rapid design space exploration with accurate models is necessary to improve designer productivity at the electronic system level. We describe how to use a new event-based design framework, Metro II, to carry out simulation and design space exploration of multi-core architectures. We illustrate the design methodology on a UMTS data link layer design case study with both a timed and untimed functional model as well as a complete set of MPSoC architectural services. We compare different architectures (including RTOSes) explored with Metro II and quantify the associated simulation overhead.

Combinational logic circuits are a fundamental building block in today's digital electronics. Combinational logic representations are highly amenable to various levels of abstraction, and manipulation is naturally performed via Boolean algebraic expressions. These properties have allowed automatic VLSI synthesis and analysis capabilities to reach a high level of maturity. In the growing field of Synthetic Biology, devices are emerging which are being classified as ldquocombinational logicrdquo. These devices can be constructed using standardized design practices which pave the way to more advanced analysis and construction techniques. However there exist a number of barriers to the application of VLSI design techniques to biological systems. We describe two key barriers and begin the process of examining how difficult they will be to overcome.

We present a modular and scalable approach for automatically extracting actual performance information from a set of FPGA-based architecture topologies. This information is used dynamically during simulation to support performance analysis in a system level design environment. The topologies capture systems representing common designs using FPGA technologies of interest. Their characterization is done only once; the results are then used during simulation of actual systems being explored by the designer. Our approach allows a rich set of FPGA architectures to be explored accurately at various abstraction levels to seek optimized solutions with minimal effort by the designer. To offer an industrial example of our results, we describe the characterization process for Xilinx Core Connect-based platforms and the integration of this data into the METROPOLIS modeling environment

Productivity data for IC designs indicates an exponential increase in design time and cost with the number of elements that are to be included in a device. Present applications require the development of complex systems to support novel functionality. To cope with these difficulties, we need to change radically the present design methodology to allow for extensive re-use, early verification in the design cycle, pervasive use of software, and architecture-level optimization. Platform-based design as defined in A. Sangiovanni-Vincentelli (2002), has these characteristics. We present the application of this methodology to a complex industrial application provided by Cypress Semiconductor. In this case study, we focus on a particular aspect of this methodology that eases considerably the verification process: successive refinement. We compare this approach versus a parallel team of designers who developed the IC using standard design approaches.