Computational optimization of cell culture media for cultured meat production
Alternative Proteins (AP), particularly Cultivated Meat (CM), offer a promising avenue towards sustainability by providing animal protein alternatives that require fewer resources and produce fewer negative externalities. This project presents a novel approach to identifying plant and fungal pept...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176184 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-176184 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1761842024-05-20T15:33:11Z Computational optimization of cell culture media for cultured meat production Chia, Shawn - School of Biological Sciences Bioinformatics Institute (BII), A*STAR Chiam Keng Hwee chiamkh@bii.a-star.edu.sg Other Cultivated meat Computational optimization Cultured meat Quasi-sequence order QSO Principal component analysis PCA Centroid clustering Euclidean distances Cell culture media Growth media Growth factor Alternative Proteins (AP), particularly Cultivated Meat (CM), offer a promising avenue towards sustainability by providing animal protein alternatives that require fewer resources and produce fewer negative externalities. This project presents a novel approach to identifying plant and fungal peptides as potential replacements for animal-derived growth factors in CM production. Through a computational screening prediction algorithm where peptides are encoded using the Quasi Sequence Order (QSO) effect to preserve the directionality of protein sequence descriptors, peptides from commonly cultivated plants and fungi were compared to growth factors from three targeted animal species: chicken, cow, and pig. The project identified four potential candidates: ITPK4_SOYBN (Inositol-tetrakisphosphate 1-kinase 4) from soybean for chicken growth factor replacement, A0A3B5Z4L3_WHEAT (Acireductone dioxygenase) from wheat for cow growth factor replacement, A0A3Q7F8S5_SOLLC (Peroxidase) from tomato for pig growth factor replacement, and GHD7_ORYSJ (Transcription factor GHD7) from rice as a replacement across targeted animal species. These peptides possess high sequence similarity to animal growth factors, suggesting their potential as non-animal alternatives for CM growth media. While further validation and refinement are required, this research lays the groundwork for leveraging plant and fungal resources to drive the development of sustainable food systems and to accelerate the commercialization of CM. Bachelor's degree 2024-05-14T05:20:52Z 2024-05-14T05:20:52Z 2024 Final Year Project (FYP) Chia, S. (2024). Computational optimization of cell culture media for cultured meat production. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176184 https://hdl.handle.net/10356/176184 en application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Other Cultivated meat Computational optimization Cultured meat Quasi-sequence order QSO Principal component analysis PCA Centroid clustering Euclidean distances Cell culture media Growth media Growth factor |
spellingShingle |
Other Cultivated meat Computational optimization Cultured meat Quasi-sequence order QSO Principal component analysis PCA Centroid clustering Euclidean distances Cell culture media Growth media Growth factor Chia, Shawn Computational optimization of cell culture media for cultured meat production |
description |
Alternative Proteins (AP), particularly Cultivated Meat (CM), offer a promising
avenue towards sustainability by providing animal protein alternatives that require
fewer resources and produce fewer negative externalities. This project presents a
novel approach to identifying plant and fungal peptides as potential replacements for
animal-derived growth factors in CM production. Through a computational screening
prediction algorithm where peptides are encoded using the Quasi Sequence Order
(QSO) effect to preserve the directionality of protein sequence descriptors, peptides
from commonly cultivated plants and fungi were compared to growth factors from
three targeted animal species: chicken, cow, and pig. The project identified four
potential candidates: ITPK4_SOYBN (Inositol-tetrakisphosphate 1-kinase 4) from
soybean for chicken growth factor replacement, A0A3B5Z4L3_WHEAT
(Acireductone dioxygenase) from wheat for cow growth factor replacement,
A0A3Q7F8S5_SOLLC (Peroxidase) from tomato for pig growth factor replacement,
and GHD7_ORYSJ (Transcription factor GHD7) from rice as a replacement across
targeted animal species. These peptides possess high sequence similarity to animal
growth factors, suggesting their potential as non-animal alternatives for CM growth
media. While further validation and refinement are required, this research lays the
groundwork for leveraging plant and fungal resources to drive the development of
sustainable food systems and to accelerate the commercialization of CM. |
author2 |
- |
author_facet |
- Chia, Shawn |
format |
Final Year Project |
author |
Chia, Shawn |
author_sort |
Chia, Shawn |
title |
Computational optimization of cell culture media for cultured meat production |
title_short |
Computational optimization of cell culture media for cultured meat production |
title_full |
Computational optimization of cell culture media for cultured meat production |
title_fullStr |
Computational optimization of cell culture media for cultured meat production |
title_full_unstemmed |
Computational optimization of cell culture media for cultured meat production |
title_sort |
computational optimization of cell culture media for cultured meat production |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176184 |
_version_ |
1814047435682480128 |