Impact of systems biology on metabolic engineering

Published on November 4, 2014   43 min
0:00
So my name is Jens Nielsen, and I'm going to talk about impact of systems biology on metabolic engineering. OK, so I have a background as a chemical engineer and have always been interested in biological systems, so I started to quite early to look into modeling of microorganisms. And that has then led to further also using these models for engineering these microorganisms for different kind of applications. So currently, my group is working very much on engineering yeast for production of different chemicals and biofuels. But in this context, we're using a lot of systems biology tools for analysis of yeast metabolism.
0:37
So we go to the first slide, which shows basically the so-called biorefinery concept. According to this concept, the objective is to take biomass, pre-treat that, degrade it to get sugars that can then be used by microbial fermentation to produce fuels and chemicals, as illustrated to the right. And one typically applied cell factory is yeast, and I'll come back and talk a little bit more about that. But in connection with that, the enabling technology is what we call metabolic engineering. That means that we are engineering these factories such that their metabolism can efficiently convert these sugars to these different fuels and chemicals. There are already quite a lot of processes running like this, of course, the most famous being production of bioethanol. But in the future, we are likely to see that some of these process facilities can be upgraded to produce new biofuels and biochemicals that have higher value and may be better use.
1:29
Next slide shows the typical process of metabolic engineering. So it typically starts with modeling and design. Typically, one goes in looks into metabolism and finds how are we going to change the metabolism in order to produce this new chemical compound. Then we move on to string construction. The resulting strains are characterized by fermentation. Phenotypic characterization, where we kind of narrow in and see what was the impact of these genetic modifications we introduced. Often, we combine that with a very detailed phenotypic characterization using omics analysis. This can be, for example, looking into transcription profiling, proton profiling, or metabolomics, and so on. Often when we have these data here, we need to integrate them in the context of models, and this can lead to refinement of the model and then the model can be used to further the identification of new design strategies. So this is what we normally refer to as the metabolic engineering cell factory. And what is very clear is that we have the things marked to the left here, is very much the core of what we normally call systems biology, namely, high throughput analysis and also modeling and integrated analysis of these kind of data. So traditionally, modeling has played an active role in metabolic engineering, but there are still relatively few examples where modeling has really led to the design of this. And one of the reasons of this as we go to the next slide
2:57
is, of course, that compared with other engineering design processes where we really have detailed knowledge, for example, in the design of cars and so on, in a biological system, we still have a lot of unknown processes. And this is, we could say, the challenge that we are facing ahead, and we need to get more insight into cellular processes in order to build this into models and then use them for design. But I predict that in the future, we will see an increasing application of system biology in this field here to make better models for better design of cell factories in the future. So we will see an analogy of design of these kind of processes with other engineering disciplines in the future.
3:39
When we talk about systems biology, people normally consider two approaches to that. One is the top-down approach, which is coming out from the genomics revolution where we basically have a high throughput analysis or omics data analysis. And we then analyze all these data in the context and basically, as a top-down approach, try to get the simplification or mathematical model of the system. The other approach is a hypothesis-based, bottom-up approach where we basically take knowledge about biological information, we stitch this together in the form of mathematical models that then can describe the system here. And traditionally, it's been difficult to kind of merge these two because the cellular models that have been built so far have typically been relatively limited in scope, so typically only describing a subset of the biological system and not the comprehensive system. And therefore, it has been difficult to integrate with the high throughput data, which is basically genome-wide and much wider coverage.
4:40
But there is one type of approach that actually allows us to link this top-down and bottom-up approach, and that's what we call genome-scale metabolic models, because they are basically bottom-up built models where we take information about what reactions are there, which enzymes catalyze these reactions, which protein behind these enzymes, and which genes are encoding these different proteins. So hereby we are putting together a comprehensive metabolic network where we have a link between all the reactions and genes. And of course, each reaction, we also have a link with the specific metabolites. So hereby, these models become this comprehensive description of metabolism that also encompass genetic information. Let's illustrate a little bit here of how we can use these models. They can be used for model simulations. I'll talk a little bit about that. They can also be used for network-dependent analysis. And I'll cover both these aspects in my talk. I often use what is shown to the bottom to say the power of using this kind of approach, this network-dependent analysis, compared with traditionally just statistical evaluation of different data sets is that you can find co-regulated sub-networks in metabolism. And this is typically very useful for finding new metabolic engineering targets.
Hide

Impact of systems biology on metabolic engineering

Embed in course/own notes