Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.Synthetic biology has so far made limited use of mathematical models, mostly because their inference has been traditionally perceived as expensive and/or difficult. We have recently demonstrated how in silico simulations and in vitro/vivo experiments can be integrated to develop a cyber-physical platform that automates model calibration and leads to saving 60-80% of the effort. In this book chapter, we illustrate the protocol used to attain such results. By providing a comprehensive list of steps and pointing the reader to the code we use to operate our platform, we aim at providing synthetic biologists with an additional tool to accelerate the pace at which the field progresses toward applications.Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.Applications of control engineering to mammalian cell biology have been recently implemented for precise regulation of gene expression. In this chapter, we report the main experimental and computational methodologies to implement automatic feedback control of gene expression in mammalian cells using a microfluidics/microscopy platform.Cell-free synthetic biology offers an approach to building and testing gene circuits in a simplified environment free from the complexity of a living cell. Recent advances in microfluidic devices allowed cell-free reactions to run under nonequilibrium, steady-state conditions enabling the implementation of dynamic gene regulatory circuits in vitro. In this chapter, we present a detailed protocol to fabricate a microfluidic chemostat device which enables such an operation, detailing essential steps in photolithography, soft lithography, and hardware setup.Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. IDEC-C2B8 A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.Restriction digest analysis and Sanger sequencing are among the most commonly used techniques to check the sequence of synthetic DNA constructs. However, both require careful preparation to select restriction enzymes or DNA primers adapted to the expected constructs sequences. In projects involving manufacturing of large batches of synthetic constructs, the task can be tedious and error-prone. This chapter demonstrates the use of two free and open-source web applications providing fast and automated selection of enzymes and sequencing primers for DNA construct verification.Type-2S restriction enzymes allow the routine assembly of large batches of synthetic constructs from individual genetic parts. However, design flaws in the part sequence can cause assembly failures, incurring troubleshooting costs and project delays. As a result, the careful design and checking of the assembly plan is often a bottleneck of large assembly projects, and may require computational support. This chapter demonstrates the use of two free and open-source web applications accelerating this task by automating genetic part design and simulating type-2S cloning to detect potential assembly issues.Laboratory automation is a key enabling technology for genetic engineering that can lead to higher throughput, more efficient and accurate experiments, better data management and analysis, decrease in the DBT (Design, Build, and Test) cycle turnaround, increase of reproducibility, and savings in lab resources. Choosing the correct framework among so many options available in terms of software, hardware, and skills needed to operate them is crucial for the success of any automation project. This chapter explores the multiple aspects to be considered for the solid development of a biofoundry project including available software and hardware tools, resources, strategies, partnerships, and collaborations in the field needed to speed up the translation of research results to solve important society problems.