Synthetic biology, or biological engineering, deals with the rational, forward engineering of biological systems for useful purposes. In particular the subfield of biocomputing, i.e. the engineering of cell behaviour, has seen tremendous interest in applications ranging from medicine (e.g. living anti-cancer therapeutics) to the transportation industry (e.g. antifouling agent-producing bacteria living on submerged hulls of ships). In these complex environments, engineered cells' success or failure is defined by their individual as well as collective behaviours over long periods of time. Therefore, for successful biocomputing of living cells it is crucial to design, build and test engineered cells at single-cell resolution, with time-dependent behaviour in mind. This is possible through microfluidics-enhanced time-lapse imaging, specifically a microfluidics geometry called "Mother Machine". A Mother Machine microfluidic device (MFD) consists of a simple feeding lane through which cell medium flows, with perpendicularly connected cell trapping chambers, called trenches. This has previously been used to study wild-type or minimally-modified bacteria for mostly fundamental microbiology studies. In this thesis, I present my work on the adaptation of Mother Machine MFDs for biological engineering, and in particular biocomputing. For this purpose I have been using synthetic/ molecular biology, microfabrication, microfluidics, laboratory automation and programming approaches. Digital records, including programmes, design files and sequence maps, can be found on my PhD thesis GitHub repository (github.com/BioCam/PhD_thesis_cm967). Chapter 1 introduces the field of synthetic biology to a broad audience, presents my work on facilitating the biological engineering design process, and gives a background on biocomputing studies that leveraged microfluidics. Next, I introduce the detailed process for a Mother Machine-based synthetic biology experiment in Chapter 2. This acts as a case study of the motivations and limitations that existed at the beginning of my PhD. By testing three simple genetic circuits, which exert varying levels of energy demands on their host, I showcase the unique insights into engineered cells that microfluidics-enhanced, single-cell, time-lapse imaging offers. This work concluded that there are at least three precedence constraints for Mother Machine-enhanced synthetic biology that required overcoming for the field to advance. These are (1) problems with Mother Machine geometry-based limitations of retaining and efficiently supplying nutrients to cells with burdened, morphologically-changed phenotypes; (2) a sample/genotype throughput problem of only being able to test one sample in the same Mother Machine; and (3) the difficulty of preparing large numbers of samples at both the build and test stage in an automated, dynamic, low-cost manner. The aim of this thesis is to develop engineering solutions to these three precedence constraints. In Chapter 3, I address constraint #1. I discuss microfabrication techniques for the construction of Mother Machines with geometries that facilitate studying of burdened, morphologically-changed cells. I demonstrate important limitations with traditional UV photolithography, and present a novel hybrid 2-photon polymerisation (2PP)/UV photolithography microfabrication technology. In the following two chapters I examine two separate approaches addressing constraint #2. Though a Mother Machine can host up to millions of trenches which host individual cells, the sample number defined by unique genotypes is restricted to one due to limitations of how cells have to be loaded into trenches. This means that Mother Machine microfabrication defines trench throughput but sample throughput has to be increased in alternative ways. In Chapter 4, I explore the idea of rapidly increasing sample throughput through poolsynthesised libraries, screening them inside the MFD, and selecting cells with desired phenotypes through light-induced/optogenetic resistance to antibiotics. I present the work I have done towards this goal, and discuss serious limitations with existing optogenetic systems. To overcome these restrictions I designed a novel system with increased predictability In Chapter 5, in situ genotyping and holistic phenotype-genotype mapping of defined genetic libraries is discussed. I have investigated 5 different genotyping techniques before identifying fluorescent in situ hybridisation (FISH) as the most promising method. However, traditional FISH is severely limited in its maximal throughput due to cost, time and experimental complexity. I invented a novel, quencher-enhanced FISH technology that resolves these issues and present the proof-of-concept of this approach for Mother Machine-based, sample multiplexed experiments. In Chapter 6, I address constraint #3 by developing a low-cost, high-throughput lab automation pipeline, called "iBioFoundry" which facilitates the generation and experiment preparation of genetic circuits, with an emphasis on FISH-barcode integration. I established a series of programming algorithms that focus on dynamic automation and adaptability - two often overlooked paradigms in the field. Finally, I automated a collaborator's complex, 384-well experiment to highlight the advanced factorial design capabilities of the iBioFoundry.