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Printable Handouts
Navigable Slide Index
- Introduction
- Outline
- Biomanufacturing
- Traditional ways for strain development
- Metabolic engineering
- Find engineering targets in a network
- Synthetic biology
- Learn from genome: metabolic network reconstruction
- Tools for GEM reconstruction
- ModelSeed/Kbase
- Tools for GEM reconstruction: RAVEN
- Problems of the draft model
- MEMOTE: tool for metabolic model test
- High quality network reconstruction
- Biomass production equation (1)
- Biomass production equation (2)
- Proteinic AA composition
- Synthesis of biomass components
- Gap filling
- Tools for gap filling
- GEM model database
- BiGG models database
- Tool for network visualization: Escher
- Methods for GEM analysis: FBA (Flux Balance Analysis)
- S matrix of GEM (1)
- S matrix of GEM (2)
- Tools for GEM analysis
- GEM analysis using Cobrapy
- Cobrapy demo
- GEM analysis using CAVE
- CAVE demo: calculation
- CAVE demo: results
- CAVE demo: visualization
- From GEM analysis to ME targets
- Take home message
- Thank You
- Financial disclosures
Topics Covered
- Metabolic engineering
- Methods/ tools for genome scale metabolic network (GEM) reconstruction
- MEMOTE: tool for metabolic model test
- BiGG model database
- Flux balance analysis
- COBRApy and CAVE demos
Links
Series:
Categories:
External Links
- Slide 10: ModelSeed for reconstruction of metabolic network
- Slide 10: Kbase for reconstruction of metabolic network
- Slide 10: E.coli genome download
- Slide 21: BiGG - metabolic model databases
- Slide 21: M model - metabolic model databases
- Slide 21: BioModels - metabolic model databases
- Slide 22: BiGG models
- Slide 27: COBRA/COBRApy for GEM analysis
- Slide 27: CellNetAnalyzer for Gem analysis
- Slide 27: OptFlux for GEM analysis
- Slide 27: FASIMU for GEM analysis
- Slide 28: Try Jupyter
- Slide 30: CAVE
Talk Citation
Ma, H. (2026, July 30). Synthetic biology tools for metabolic engineering [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved August 5, 2026, from https://doi.org/10.69645/ELYV7843.Export Citation (RIS)
Publication History
- Published on July 30, 2026
Financial Disclosures
- There are no commercial/financial matters to disclose.
Other Talks in the Series: Introduction to Computational Biology
Transcript
Please wait while the transcript is being prepared...
0:00
Hello everyone. This is
an AI-generated voice
speaking on behalf
of Prof. Hongwu Ma
from the Tianjin Institute
of Industrial Biotechnology,
Chinese Academy of Sciences.
Today, I will be
presenting his lecture on
synthetic biology tools
for metabolic engineering.
0:23
Let's begin with the outline.
First, I will give you
a brief introduction
to the background
of the lecture,
and then I will show some
methods and tools for
genome scale metabolic network
reconstruction and
quality control.
Genome scale metabolic
network is a central topic of
this lecture, so I will use
the abbreviation GEM for it.
The last one is methods
and tools for GEM analysis
and how we use GEM
for metabolic engineering
target design.
0:55
To start, many of
you may already be
familiar with the term
biomanufacturing.
It is about the manufacture
of valuable products,
maybe biomaterials,
chemicals or drugs,
and they are produced from
renewable bio-resources,
such as starch
and cellulose or
even carbon dioxide.
The central part of
this biomanufacturing
is the microorganisms.
The microorganisms
do the conversion
from bio-resources to
products, and we often call it
microbial cell factory
or industry strains.
1:31
Traditionally, strain
development relied
on mutation and screening.
When I study microbiology
at university,
I always screen microbials
from soil and water.
By using some mutant
technologies,
we can accelerate
the mutation process
and create a more diverse
population of organisms,
and then have a
good chance to get
the strains with
good properties.
Also, we have this direct
evolution technology.
It will greatly accelerate
the evolution process
and create strains
which can adapt to
a new environment
or a new property.
But with both technologies,
the strains are treated
as black boxes.
We do not know what pathways,
what enzymes are used for
the metabolic conversion.