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Designing biological systems: Computational modelling and systems biology

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Industrial Biotechnology

Universidade de Manchester

4.7 (267 classificações) | 12K alunos inscritos

Fossil fuels have been the primary energy source for society since the Industrial Revolution. They provide the raw material for the manufacture of many everyday products that we take for granted, including pharmaceuticals, food and drink, materials, plastics and personal care. As the 21st century progresses we need solutions for the manufacture of chemicals that are smarter, more predictable and more sustainable. Industrial biotechnology is changing how we manufacture chemicals and materials, as well as providing us with a source of renewable energy. It is at the core of sustainable manufacturing processes and an attractive alternative to traditional manufacturing technologies to commercially advance and transform priority industrial sectors yielding more and more viable solutions for our environment in the form of new chemicals, new materials and bioenergy. This course will cover the key enabling technologies that underpin biotechnology research including enzyme discovery and engineering, systems and synthetic biology and biochemical and process engineering. Much of this material will be delivered through lectures to ensure that you have a solid foundation in these key areas. We will also consider the wider issues involved in sustainable manufacturing including responsible research innovation and bioethics. In the second part of the course we will look at how these technologies translate into real world applications which benefit society and impact our everyday lives. This will include input from our industry stakeholders and collaborators working in the pharmaceutical, chemicals and biofuels industries. By the end of this course you will be able to: 1. Understand enzymatic function and catalysis. 2. Explain the technologies and methodologies underpinning systems and synthetic biology. 3. Explain the diversity of synthetic biology application and discuss the different ethical and regulatory/governance challenges involved in this research. 4. Understand the principles and role of bioprocessing and biochemical engineering in industrial biotechnology. 5. Have an informed discussion of the key enabling technologies underpinning research in industrial biotechnology 6. Give examples of industrial biotechnology products and processes and their application in healthcare, agriculture, fine chemicals, energy and the environment.

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Avaliações

4.7 (267 classificações)

5 stars
190 ratings
4 stars
64 ratings
3 stars
12 ratings
2 stars
1 ratings

JN

May 21, 2018

The lectures were divided into modules that were appropriate in length/tie. Speakers were very easy to comprehend and the outline of the topics were well presented.

KM

Nov 16, 2017

Hi, i am taking this online course via coursera i would like to know if their is a certificate of recognition for the course?

Na lição

Methods in Systems and Synthetic Biology

Recent advances in our ability to read and write genome sequences on a large scale have led to an ambitious vision for a new generation of biotechnology, often referred to as Synthetic Biology. Synthetic Biology aims at turning biology into an engineering discipline, in which organism engineers use computational tools to design biological systems with novel valuable functionalities, which are then built using advanced high-throughput genetic engineering, and tested by rapid screening technologies that collect diagnostic molecular profiles to drive improved designs in an iterative design-build-test cycle. This module will introduce the engineering concepts that inform Synthetic Biology and the cutting-edge technologies that underlie our dramatically increasing ability to construct living systems with custom-made functionalities. All stages of the design-build-test cycle for novel biosystems will be discussed, with a special focus on their integration in a unified bioengineering platform. Examples will focus on the application of Synthetic Biology as an enabling technology for the bioindustry, especially for the improved microbial production of high-value chemicals and drugs. A section on responsible research and innovation will explore the transformative potential of this innovative technology within a broader socio-economic context, creating awareness of the ethical and political implications of research in this field.

Introduction to Synthetic Biology: Visions for Biotechnology 2.06:27

Designing biological systems: Computational modelling and systems biology9:10

Building biological systems I: Genome synthesis and genome editing9:25

Controlling and engineering pathways in Synthetic Biology9:00

Testing Biological Systems Biological debugging using metabolomics8:12

Deploying biological systems: Perspectives on Responsible Research & Innovation and bioethics I5:24

Conclusions and Outlook Systems and Synthetic Biology6:40

Ministrado por

Prof. Nicholas Turner
Deputy Director
Prof. Nigel Scrutton
Director

Transcrição

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.

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