Difference between revisions of "BEECH(F23):Build Knowledge"
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==Videos== | ==Videos== | ||
+ | ===Fundamental concepts=== | ||
+ | [[File:MicheleGabriele_DNA_Screenshot_231116.png| border|200px | https://www.dropbox.com/scl/fi/j8x7hhun6rd50jnbgsi52/MicheleGabriele_231107_DNA_StructureFunction.mp4?rlkey=jwg2hve4lku0hh279dqvi8jpn&dl=0]] | ||
+ | [https://www.dropbox.com/scl/fi/j8x7hhun6rd50jnbgsi52/MicheleGabriele_231107_DNA_StructureFunction.mp4?rlkey=jwg2hve4lku0hh279dqvi8jpn&dl=0 DNA structure, by Dr. Michele Gabriele] | ||
+ | <br> | ||
+ | [[File:MicheleGabriele_RNA_Screenshot_231116.png| border|200px | https://www.dropbox.com/scl/fi/hkhqx8bfsp5jdul6d736s/MicheleGabriele_231107_RNA_Transcription.mp4?rlkey=ra2im7sq06znfma1vmu7x579a&dl=0]] | ||
+ | [https://www.dropbox.com/scl/fi/hkhqx8bfsp5jdul6d736s/MicheleGabriele_231107_RNA_Transcription.mp4?rlkey=ra2im7sq06znfma1vmu7x579a&dl=0 RNA and transcription, by Dr. Michele Gabriele] | ||
+ | <br> | ||
+ | [[File:MicheleGabriele_Protein_Screenshot_231116.png| border|200px | https://www.dropbox.com/scl/fi/e9dyn1cpc63hw35xik1sb/MicheleGabriele_231113_Protein_Translation.mp4?rlkey=sailsbv5hio4zuhrqrsk7mzck&dl=0]] | ||
+ | [https://www.dropbox.com/scl/fi/e9dyn1cpc63hw35xik1sb/MicheleGabriele_231113_Protein_Translation.mp4?rlkey=sailsbv5hio4zuhrqrsk7mzck&dl=0 Protein translation and function, by Dr. Michele Gabriele] | ||
+ | <br> | ||
+ | |||
===Techniques=== | ===Techniques=== | ||
− | |||
+ | ===Research projects=== | ||
+ | [[File:JoeKreitz_Screenshot_231116.png| border| 200px | https://www.dropbox.com/scl/fi/hf7404b7c60krhaptugkc/JoeKreitz_231105_GeneTherapy_BacterialNanoSyringes.mov?rlkey=wbf3i3rg4gh69zw1ilwwbra4o&dl=0]] | ||
+ | [https://www.dropbox.com/scl/fi/hf7404b7c60krhaptugkc/JoeKreitz_231105_GeneTherapy_BacterialNanoSyringes.mov?rlkey=wbf3i3rg4gh69zw1ilwwbra4o&dl=0 Gene therapy: bacterial nano-syringes, by Joe Kreitz ] | ||
<br> | <br> | ||
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[https://news.mit.edu/2021/robust-artificial-intelligence-tools-predict-future-cancer-0128 Using AI to predict cancer development] <br> | [https://news.mit.edu/2021/robust-artificial-intelligence-tools-predict-future-cancer-0128 Using AI to predict cancer development] <br> | ||
[https://news.mit.edu/2020/gaussian-machine-learning-tb-drug-1015 Machine learning to identify new tuberculosis drugs] <br> | [https://news.mit.edu/2020/gaussian-machine-learning-tb-drug-1015 Machine learning to identify new tuberculosis drugs] <br> | ||
+ | [https://news.mit.edu/2022/alphafold-potential-protein-drug-0906 Computational tools to understand antibiotic resistance]<br> | ||
+ | [https://news.mit.edu/2022/neurodegenerative-disease-can-progress-newly-identified-patterns-0927 Machine learning to identify neurodegeneration patterns]<br> | ||
===Disease biology=== | ===Disease biology=== | ||
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[https://news.mit.edu/2021/gut-brain-chip-0129 Organs on a chip to study disease] <br> | [https://news.mit.edu/2021/gut-brain-chip-0129 Organs on a chip to study disease] <br> | ||
[https://news.mit.edu/2022/probing-how-proteins-pair-inside-cells-0203 Mapping how proteins interact with each other to predict disease]<br> | [https://news.mit.edu/2022/probing-how-proteins-pair-inside-cells-0203 Mapping how proteins interact with each other to predict disease]<br> | ||
+ | [https://news.mit.edu/2023/self-assembling-proteins-can-store-cellular-memories-0102 Following cellular events encoded in proteins]<br> | ||
+ | [https://news.mit.edu/2022/expansion-revealing-microscopy-cells-0829 Using expansion microscopy to better understand amyloid plaques in Alzheimer's disease]<br> | ||
===Development of diagnostic and laboratory tools=== | ===Development of diagnostic and laboratory tools=== | ||
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[https://news.mit.edu/2021/new-programmable-gene-editing-proteins-found-outside-crispr-systems-0915 Programmable enzymes for gene editing] <br> | [https://news.mit.edu/2021/new-programmable-gene-editing-proteins-found-outside-crispr-systems-0915 Programmable enzymes for gene editing] <br> | ||
[https://news.mit.edu/2022/chromatin-loop-structures-gene-expression-0414 Understanding how temporary DNA structure affects gene expression]<br> | [https://news.mit.edu/2022/chromatin-loop-structures-gene-expression-0414 Understanding how temporary DNA structure affects gene expression]<br> | ||
+ | [https://news.mit.edu/2022/synthetic-gene-expression-control-1101 Using CRISPR to control protein production]<br> | ||
+ | [https://news.mit.edu/2022/scientists-unveil-functional-landscape-essential-genes-1121 CRISPR knockdown identifies gene function]<br> | ||
===Microbes=== | ===Microbes=== | ||
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[https://news.mit.edu/2021/metabolic-bacteria-antibiotic-0218 Mutations that create antibiotic resistance] <br> | [https://news.mit.edu/2021/metabolic-bacteria-antibiotic-0218 Mutations that create antibiotic resistance] <br> | ||
[https://news.mit.edu/2022/mucus-molecules-fungal-infection-0606 Modifying mucus to prevent fungal infection]<br> | [https://news.mit.edu/2022/mucus-molecules-fungal-infection-0606 Modifying mucus to prevent fungal infection]<br> | ||
− | + | [https://news.mit.edu/2022/bacteria-good-gut-microbes-antibiotics-0411 Engineering bacteria to maintain the gut microbiome]<br> | |
</div> | </div> |
Latest revision as of 01:04, 17 November 2023
Overview
Notes about how to use this page
Videos
Fundamental concepts
DNA structure, by Dr. Michele Gabriele
RNA and transcription, by Dr. Michele Gabriele
Protein translation and function, by Dr. Michele Gabriele
Techniques
Research projects
Gene therapy: bacterial nano-syringes, by Joe Kreitz
Press releases about current MIT biotechnology research
Agriculture and Climate science
Sustainable palm oil alternative
Using microbes as environmental sensors
Producing lab-grown timber
Creating a portable desalination device for drinking water
Using plant biology to counteract climate change
Biomaterials
DNA-scaffold quantum rods
Light-responsive muscle grafts
Growing pancreatic organoids
Creating synthetic mucus
Enhancing vaccine efficacy with nanoparticles
Computational biology
Computational screening for drug discovery
AI model for biology research
Computational model for effective DNA editing
Using AI to predict cancer development
Machine learning to identify new tuberculosis drugs
Computational tools to understand antibiotic resistance
Machine learning to identify neurodegeneration patterns
Disease biology
Influence of cell fate on cancer progression
Previously unknown immune response regulator
Modulating immune cells for cancer therapy
Using cellular information to find new malaria drugs
Organs on a chip to study disease
Mapping how proteins interact with each other to predict disease
Following cellular events encoded in proteins
Using expansion microscopy to better understand amyloid plaques in Alzheimer's disease
Development of diagnostic and laboratory tools
Mapping a 3D genome
Using cell mass as a new cancer diagnostic
Counting circulating tumor cells as a marker of cancer progression
Microscopy for deep tissue imaging
Artificial intelligence for cancer detection
Using expansion microscopy to view small things in high resolution
Imaging complex cell communication
Creating genetic tools to allow fMRI to map brain connections
Drug delivery
Bacteriophage delivery system
Nanoparticle drug delivery
Vaccine boost for CAR-T cell cancer therapy
Delivering RNA for gene editing
Identifying new cancer drugs with technology
Manufacturing red blood cells for blood transfusions
Developing antibody treatments for infectious disease
Drug delivery to boost immune system in cancer
Engineering nanoparticles to cross the blood brain barrier
Gene editing
Targeting RNA therapies
Programmable RNA to edit genome
Programmable enzymes for gene editing
Understanding how temporary DNA structure affects gene expression
Using CRISPR to control protein production
CRISPR knockdown identifies gene function
Microbes
Bacteria communication and antibiotic resistance
Mutations that create antibiotic resistance
Modifying mucus to prevent fungal infection
Engineering bacteria to maintain the gut microbiome