Researchers have identified two molecules that could treat inflammatory disease. Referred to as T23 and T8, these molecules inhibit the function of the protein known as tumour necrosis factor, which is involved in inflammatory diseases such as multiple sclerosis, psoriasis, Crohn’s disease, rheumatoid arthritis, and more. According to a paper that was published in PLOS Computational Biology, the scientists identified the molecules using a drug screening method they developed.
Led by Georgia Melagraki, the researchers from Greece and Cyprus came up with a new computer-based drug screening platform that aided them to discover better tumour necrosis factor inhibitor drugs. The platform integrates proprietary molecular characteristics shared between tumour necrosis factor and another protein known as RANK, which also plays a role in chronic inflammatory diseases.
The scientists developed the platform based on several advanced computational tools. Then, the platform was used to screen almost 15, 0000 molecules with unidentified activity. After that, they predicted the interactions of the molecules with RANKL proteins and tumour necrosis factor; particularly, how the molecules might interrupt the protein-protein interactions leading to activation of these vital proteins. Out of thousands of candidates, the experiment identified nine potential molecules.
To further evaluate the potential of the molecules, the researchers studied how the nine molecules interacted with RANKL and tumour necrosis factor in real-world laboratory experiments. T23 and T8 were identified as strong tumour necrosis factor inhibitor.
It is not easy to find a bioinformatics book that provides all the information in computing, biology, and mathematics fields. The few books available are very expensive. Books on computational biology can be grouped into books of general interest, those for biologists interested in bioinformatics and those best suited to individuals from the mathematical or computational background.
If you are interested in learning human genome, look for Matt Ridley’s “Genome”. The book offers an interesting introduction to issues raised by the revolution of bioinformatics. If you’re non-scientists, go for James Watson’s “The Double Helix“. The book enables the reader to understand the structure of the DNA. A broad introduction to key computational ideas applied in bioinformatics is provided in “Bioinformatics Algorithms: An Active Learning Approach” by Pavel Pevzner and Phillip Compeau.
If you are interested in mathematical/ Computational aspects, Michael Waterman’s “Introduction to Computational Biology“ is the best book for you. The book describes the computational the structure of biological data, especially from chromosomes and sequences. The text exposes the reader to structure of biological data and describes how to treat associated combinatorial and statistical problems.
One outstanding general book for biologists is “Bioinformatics” by David W. Mount. Although the book is quite expensive, it is one of the best books if you want to study bioinformatics.
Autism spectrum disorder affects nearly 1.5% of children. Unfortunately, the diagnosis of the disorder is difficult and relies on multidisciplinary medics. Although past research has shown distinct metabolic processes in children who suffer from the disorder, they have not previously been looked at in diagnosis.
Scientists Daniel Howsmon, Juergen Hahn and colleagues have successfully developed an accurate diagnostic technique for children centred on blood sampling. The technique identifies constituents in the blood produced by metabolic processes: the transulfuration (TS) pathways and the folate-dependent one-carbon (FOCM) metabolism. Both processes are altered in children suffering from autism.
The researchers compared blood sample from neurotypical children and children with autism, all between three and ten years old. Advanced statistical analysis tools enabled the scientist to accurately classify 97.1% neurotypical children and 97.6% of the children with autism based exclusively on their blood biomarkers.
While further study is required to confirm the results and to look at any effect of medications on the biomarkers’ blood concentration, this research is an indication that in the future, there might be a simple and accurate method to detect autism in children
A computer-based simulation has shown that Tourette Tics may arise from interactions between many brain parts, rather than one malfunctioning part, according to a study that was published in PLOS Computational Biology.
The symptoms of Tourette syndrome include involuntary motor tics, such as sniffing, clapping, or eye blinking. Traditionally, those tics were related to dysfunction of a brain part referred to as the basal ganglia. However, recent studies of human, monkey and rat brains show that thalamus, cortex, and cerebellum may be involved, too.
Led by Daniele Caligiore of the National Research Council, Italy, researchers have developed a computer-based brain simulation that triggers motor tics in Tourette syndrome. The model imitates neutral activity that was related to tics in the monkey study, which suggested that tics also involve signalling between basal ganglia, cerebellum, and cortex.
The researchers tweaked the model to reproduce the results of the study of the monkey brain. Consequently, they were able to understand how the brain produces tics. The model shows that abnormal activity of dopamine in the basal ganglia and activity in the thalamo-cortical system work together to generate a tic. The model also suggests that the cerebellum- basal ganglia link discovered in the study of monkey may enable the cerebellum to influence the production of tic.