Selvarajoo K
Machine learning alternative to systems biology should not solely depend on data
Keywords : mechanistic modeling, machine learning, AI, systems biology, synthetic biology, metabolic engineering
ReadThe transformation of our food system using cellular agriculture: What lies ahead and who will lead it?
Phthalate esters (PAE) are compounds derived by double esterification of phthalic acid (1,2-benzenedicarboxylic acid). Since the Industrial Revolution, low molecular weight phthalates such as dimethyl phthalate (DMP) and diethyl phthalate (DEP) have been used in pharmaceutical and manufacturing industries to confer flexibility to products used in personal care, infant care and medical devices1.
ReadGeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis
Multi-dimensional biological data is rapidly accumulating, and it is expected that the size of the data will exceed astronomical levels by 2025 (Stephens et al., 2015). This resulted in the development of computational tools that became vital in driving scientific discovery in recent times (Markowetz, 2017). A parallel increase in the development of online servers and databases has also been witnessed (Helmy et al., 2016), raising a new set of challenges related to the usability and maintenance of all these tools (Mangul et al., 2019). About half of the computational biology tools were found to be difficult to install, 28% of them are unavailable online in the provided URLs, and many others are missing adequate documentation and manuals (Mangul et al., 2019). The problem gets more complex with the limited computational and coding skills of two-thirds of the biologists who use these tools (Schultheiss, 2011). On the other hand, it was also noted that bioinformatics tools that are easy to install and use are highly cited, indicating wider usability by the community and a larger contribution to scientific discovery (Mangul et al., 2019). Thus, more web-based tools that avoid installation difficulties and operating system compatibility issues, simple point-and-click tools are required to tackle multi-dimensional omics datasets.
ReadSystems Biology to Understand and Regulate Human Retroviral Proinflammatory Response
After the human genome sequencing project, it is now well-known that only a very minor component of the whole genome, that is, only about 1-2% constitute of protein-coding genes (1). The remaining sequences make up the numerous transcriptional and translational regulatory components, such as ribosomal DNA genes, transfer RNA genes, and non-coding DNA sequences. Notably, in mammalian cells, about half of the non-coding DNA sequences are transposable elements (TEs) (2).
ReadThe need for integrated systems biology approaches for biotechnological applications
Biotechnology applications have contributed significantly to “factory in a lab” research. Although the largely adopted Design–Build–Test–Learn cycle has considerably improved synthetic biology and metabolic engineering capabilities, we are still far from achieving industrial efficiency.
ReadThe need for integrated systems biology approaches for biotechnological applications
Biotechnology applications have contributed significantly to “factory in a lab” research. Although the largely adopted Design–Build–Test–Learn cycle has considerably improved synthetic biology and metabolic engineering capabilities, we are still far from achieving industrial efficiency.
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