Heterogeneity in pure microbial systems: experimental measurements and modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale

Biosensor-based engineering of biosynthetic pathways

Biosynthetic pathways provide an enzymatic route from inexpensive renewable resources to valuable metabolic products such as pharmaceuticals and plastics. Designing these pathways is challenging due to the complexities of biology. Advances in the design and construction of genetic variants has enabled billions of cells, each possessing a slightly different metabolic design, to be rapidly generated. However, our ability to measure the quality of these designs lags by several orders of magnitude. Recent research has enabled cells to report their own success in chemical production through the use of genetically encoded biosensors. A new engineering discipline is emerging around the creation and application of biosensors. Biosensors, implemented in selections and screens to identify productive cells, are paving the way for a new era of biotechnological progress

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tool

Developments in the tools and methodologies of synthetic biology.

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a body of knowledge from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community

Programming Synthetic Microbial Communities for Coexistence, Coordination, and Information Processing

Synthetic microbial communities offer a variety of potential advantages over single species approaches for many medical, industrial, and environmental applications. At the cellular level, metabolic pathways can be distributed amongst several community residents to lower the metabolic burden on individual cells and to enable optimization of reaction conditions for different parts of metabolic pathways. At the population level, diverse microbial communities in different natural contexts have been shown to be more productive, efficient, stable, and resistant to invasion by foreign agents. Along with these potential advantages, however, come a variety of new challenges as well. First, different species or cell types of interest must be able to coexist. Additionally, in many scenarios the relative abundance of each resident can impact the overall property of the community. Beyond coexistence and community composition, information processing and sharing is often essential to the types of complex, coordinated behavior that is required for many desired medical, industrial, and environmental applications. My dissertation has centered around the design and implementation of two novel systems which address some of the challenges discussed above that must be overcome to realize the potential of synthetic microbial communities for use in technological applications. In the first system our goal was to develop a tool that can be used to enable coexistence and program community composition within a synthetic microbial community. We use xvi temperature as a modality to enable coexistence of two microorganisms, Escherichia coli and Pseudomonas putida, with different thermal niches and to further program the composition of this model synthetic bi-culture. Specifically, I developed two different approaches, referred to as a constant temperature regime and a cycling temperature regime. Employing a combination of wet-lab experiments and mathematical modeling, I showed that a variety of parameters such as temperature, cycle duration, etc. can be manipulated to achieve desired community compositions. Building on this work, I then used a mathematical framework developed by ecologists to explore design principles and specific mechanisms underlying the observed relationship between culture temperature and coexistence. In the second system, I designed a novel synthetic microbial community with a distributed sensing and centralized reporting architecture that is enabled by what we have termed bacteriophage-mediated information transfer. Our goal is to explore a novel distributed sensing with centralized memory system architecture that is capable of addressing limitations of previously developed systems. A modular genetic circuit was developed that connects the input of an environmental signal of interest to activation of a lysogenic lambda bacteriophage which is used to transfer information about the sensing event from the sensor cell population to a reporter cell population. A variety of different ways to encode and store information were explored. While seemingly different, the lines of work described above are connected by a common thread of developing generalizable and modular approaches for engineering synthetic microbial communities to deliver the potential advantages they offer in a variety of medical, industrial, and environmental applications. Synthetic microbial communities are capable of xvii performing complex and varied functions within these contexts and this dissertation is contributing to the rapidly growing body of research work for addressing the challenges that must be overcome to realize that potential.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163080/1/agkrieg_1.pd

Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology

Living organisms are differentiated by their genetic material-millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature. In doing so, they test the limits of both natural and engineered organisms

Combining Metabolic Engineering and Synthetic Biology Approaches for the Production of Abscisic Acid in Yeast

Nature presents us with a myriad of complex and diverse molecules. Many of these molecules prove to be useful to humans and find applications as pharmaceuticals, biofuels, agrochemicals, cosmetic ingredients or food additives. One highly promising natural product with a broad range of potential applications is the terpenoid abscisic acid (ABA). ABA fulfils a pivotal role in higher plants by regulating various developmental processes as well as abiotic stress responses. However, ABA is also produced in many other organisms, including humans. It appears to be a ubiquitous and evolutionary conserved signalling molecule throughout nature. Genetically engineered microorganisms, referred to as microbial cell factories, can be a sustainable source of natural products. In this thesis, a cell factory for the heterologous production of ABA was established and optimized employing the yeast Saccharomyces cerevisiae. Cell factory development is an inherently time-consuming process. As an enabling technology for subsequent work on the ABA cell factory, we expanded the modular cloning toolkit for yeast and made it more applicable for common genetic engineering tasks (Paper I). The ABA biosynthetic pathway of Botrytis cinerea was used to construct an ABA-producing S. cerevisiae strain (Paper II). The activity of two B. cinerea proteins, BcABA1 and BcABA2, was found to limit ABA titers. Two optimization approaches were devised for the following studies. Firstly, various rational engineering targets were explored, of which the native yeast gene PAH1 was identified as the most promising candidate (Paper III). Knockdown of PAH1 benefited ABA production without affecting growth. Secondly, platform strains for screening BcABA1 and BcABA2 enzyme libraries were developed, which utilize an ABA biosensor and enable a high throughput screening approach (Paper IV). In this work, we combined metabolic engineering and synthetic biology approaches for the heterologous production of ABA, and furthermore provided tools and insights that will be useful beyond the scope of this project