Hello, I'm Dr. Neil Swainston, I work here at the Synthetic Biology Center, SYNBIOCHEM, at the University of Manchester. The work that I do is entirely computational, and it involves the development of software and computational models that can be used to design genetic parts, systems, and devices that I can pass onto my experimental colleagues to build and test. Now during the course of this module, I'll be discussing some of these software approaches that I've developed. Computational modelling and systems biology approaches can be used to run numerous computational experiments that would be too expensive or time consuming to perform using traditional wet lab approaches. Modeling approaches are typically integrated into an iterative cycle with experimental work. Models are built from experimental data, and this is typically from omics data such as transcriptomics, proteomics, or metabolomics data, which are going to be described more fully in a subsequent module. Computational simulations can then be run to generate hypotheses, which are then tested with the subsequent round of lab experiments. The additional data generated from these experiments can also be used to further refine the model. And the cycle continue until the model is sufficiently predictive to be used for a given purpose. Now in the context of industrial biotechnology, computational modeling is typically applied at the design phase, allowing modules to be designed to develop cell factories that produce a given target compound. Now such designs can be applied at different levels of biological complexity. In the simplest case, we can design individual enzyme parts, a process that typically takes a naturally occurring enzyme and modifies it to generate varian libraries of the enzyme. These libraries can then be screened for those that show a desired property, such as increased speed or amended specificity towards a certain chemical substrate. Next up is the design of devices, in this case, synthetic pathways containing a number of enzymes that can be introduced into a given host microorganism to convert a native metabolite to a desired target compound. And the final case is the design of systems, which involves whole cell genome scale modeling to investigate potential beneficial modifications that could be made to the host microorganisms in order to provide improved yields of our target molecule. So as mentioned, the goal of industrial biotechnology is in the development of microbial cell factories to produce target compounds of interest. Now these target compounds might take the form of biofuels, pharmaceuticals, or other chemicals of interest such as fragrances or food flavorings. A microbial cell factory consists of a number of components, and it's the interplay of these components that must be considered when designing such systems. The first consideration is the natural metabolism of the host organism. Despite many functions of metabolism being conserved across species, metabolism can differ dramatically from organism to organism. Therefore, understanding the metabolic capabilities of a given organism or even a strain of an organism is an important consideration when selecting and modifying a host. A second issue is the design of the engineered synthetic pathways themselves. In most cases, a given host organism will be incapable of producing the desired target compound naturally. And therefore, a synthetic pathway consisting of a number of enzymes from other species needs to be designed and introduced to the host. The host organism's natural metabolic pathways, then work in tandem with the host's biosynthetic pathways to produce the target compound. A final consideration is the feedstock upon which the cell factory will grow. A key goal of industrial biotechnology approaches is the ability to produce target chemicals sustainably. Therefore, the feedstock may take the form of a resource such as waste biomass, or even naturally abundant gases such as carbon dioxide or methane. Understanding the interplay between feedstock, the host's natural metabolism, and an engineered synthetic pathway, is crucial in designing cell factories. And it is this interplay that can be modeled computationally. When modeling whole cell or genome scale metabolism of a particular organism, we need a definition of the metabolic network in the form of a map or reconstruction that catalogs the metabolic processes that can occur in the organism. Many such maps exist for a number of organisms and strains that are used in industrial biotechnology. Now the image we have here provides a cartoon representation of the complexity of cellular metabolism, showing the thousands of metabolites that occur naturally in the cell, along with the thousands of metabolic reactions that can interconvert them. Now metabolic network maps provide a computational and mathematical representation of this complexity, and contain the following components. We have definitions of metabolic reactions in terms of which metabolites can be converted to which others. Where known, we always specify the enzymes that catalyze each of these reactions. Transport reactions are also specified. Network maps contain definitions of both the intracellular and extracellular environments. And not all metabolites can pass through the cellular membrane. So those that can be taken up and excreted by the cell are specified, along with the transport proteins that mediate these transport reactions if they are known. Finally, in the case of eukaryotic organisms, intracellular compartments such as the cytosol or mitochondria can be specified, along with the intracellular transport reactions and proteins that allow metabolites to move between them. These metabolic network reconstructions act as a map, and they show potential paths through the network. But they do not provide the specific path through the map that is being taken at any given time. Now modeling allows us to simulate the traffic through this map, predicting the metabolic flux through the network. It's essentially a prediction of how active each metabolic reaction is under a given condition. So if we specify the feedstock, that is the growth medium upon which the whole cells are grown, some experimental data, which can perhaps be measured expression levels of metabolic enzymes, and an assumed objective that the cell is thought to fulfill. And often we use the assumption that the organism has evolved to maximize growth, we can predict the metabolic flux pattern that the host cell is exhibiting. Armed with this knowledge, computational simulations can be run to determine potential gene knockouts that could be performed on the host to optimize production of the target compound. For example, the removal of two enzymatic genes may redirect flux to increase the yield of the given target. In addition to considering host metabolism, the synthetic pathways that are to be expressed in the host must also be carefully designed. To aid this process, databases of metabolic reactions and the enzymes that catalyze them have been collated to provide what effects to a potential parts list of the enzymes and therefore, biochemical transformations that are available to us. These databases contain enzymes from thousands of organisms. The goal is then to select the best collection of enzymes, that, when we introduce them to the whole metabolic network will maximize the production of our target compound. This collection of enzymes forms our synthetic pathway. In designing a synthetic pathway, there are a number of factors to consider. The first is simplicity. There are practical considerations regarding the number of enzymes that we can include in a synthetic pathway. And ideally, these pathways should be limited to a small number of enzymes and therefore, biochemical steps. Another factor is toxicity. It's important to design synthetic pathways in such a way the toxic intermediate compounds do not accumulate, as these can impede the growth of the host cell. And another issue is maximizing the production of the target compound itself, which involves carefully selecting enzymes with specific activities and specificities for given metabolites. Now, this is an example of the multi-objective optimization problem, and specialized software has been developed to aid the design of these synthetic pathways.
