Metabolism is a major source of cell information that combines information from the outside world with information from inside the cell to help cells make decisions about how to use nutrients, communicate, or change into new cells. This dual role has brought back a focus on metabolism in fields ranging from microbiology to cancer research. Metabolite concentrations are important for both mass and information transfer because they show many things about metabolic activity, such as metabolic fluxes, enzyme activity, and cell regulation.
Biotechnology:
In biotechnology, the focus is often on engineering and understanding specific metabolic pathways, so targeted metabolomics is still the best way to do this. Targeted methods were used to look for bottlenecks in biosynthetic pathways, improve fermentation media compositions, detect toxic intra- or extracellular metabolites, or look for redox, energy, and cofactor imbalances in strains that had been engineered to make certain products.
Microbial interactions:
In both industry and nature, microbes have to deal with unsuitable and constantly changing environments. Because cell adaptation must happen quickly, many stress mechanisms are based on metabolism. Because of this, metabolomics has been used a lot to study how bacteria respond to osmotic, temperature, oxidative, or acid stress. Also, there are bioprocess-specific responses to the toxic chemicals furfural and phenol and the biofuels ethanol and ethylbenzene, which are both bad for you.
Functional genomics and mGWAS:
The growing availability of fully sequenced genomes is contrasted with the large number of genes that still don’t know what they do. Metabolomics has been used to figure out how orphan genes do their jobs, label orphan metabolic functions, and find new metabolic pathways. Targeted methods were used to test specific computer ideas about how enzymes might work or to describe specific reactions in a pathway.
Health and Disease:
Metabolomics is becoming more common in biomedical research because many diseases and health problems are caused by changes in metabolism. For example, it is used to find biomarkers. The wide range of nontargeted metabolomics methods makes it possible to find biomarker signatures made up of a lot of different metabolites, which could lead to more sensitive and specific detection of a lot of diseases, like cancer.
Biological Insights knowledge Graph:
A knowledge graph is a way to get data for machine learning methods that can help people solve complicated problems in the life sciences. This has become a lot more common in recent years. Biological Insights Knowledge Graph (BIKG) is a tool that brings together important data for drug development from both public and internal sources. It can help with everything from finding new targets to repurposing existing drugs. Organizational knowledge graphs need to be able to accurately describe the domain and let users search and query the data, but they also need to be able to handle multiple use cases and be able to support use case-specific machine learning models. The data models must also be simplified for later tasks, and the graph content must be easy to change for different use cases. Different projections of the graph content are needed to support a wider range of consumption modes. In this paper, we talk about how we built the BIKG graph and how we used it. We also talk about how the graph went from being made to being used.
Use of Knowledge graph:
1.Recently, there has been a lot of growth in the use of knowledge graphs in the field of living things.
2.Knowledge graphs are often used as the backbone for data integration in businesses.
3.They provide a common way to show and query data from different sources. With the recent advances in machine learning (ML), knowledge graphs have become even more important.
4.They can be used to train ML models, and graph machine learning models in particular.
5.They can now be used as training data, which has to be taken into account when making knowledge graphs.
6.This changes how the design of the graphs is made. It can be hard for machine learning to work with data that has a very expressive schema because it can be hard to scale and spread signals too far away to be used.
7.Similarly, using the data as ML training data requires support for a wide range of ways to use the data, not just structured queries.
8.AstraZeneca is working on a project to build a knowledge graph that includes both public and internal data so that machine learning can help people find new things by looking at them.
9.There are public databases like ChEMBL and Ensemble, as well as full-text publications that have been processed using Natural Language Processing (NLP) techniques.
Projections of graphs:
The graph is made up of several columnar data tables (node, mapping, and edge table) that make it easy to process quickly and reduce the size of the total data set. In the end, you get all the information you need, including mappings and other labels for nodes, as well as a lot of other features (contextual attributes for nodes, and underpinning evidence for edges). You can’t use this kind of graph because it has billions of rows and a hundred or even a thousand columns. If people don’t have the tools to process big data, even simple tasks like querying the graph or making edge sets can be difficult. In order to help users and make the graph more accessible to a wide range of people in the company, the pipeline makes a number of different formats, called graph projections.
Conclusion:
It’s been hard to do nontargeted metabolomics because of three problems so far. First, the coverage of the metabolome and the speed of analysis used to limit what could be seen. But now, new methods deal well with both of these issues. Second, a systematic way to identify metabolites based on mass spectra is important, but some ambiguity is OK in exchange for a wide range of coverage and short measurement times.