LogicTRN Model Illuminates Regulatory Gene Framework
A research team from the School of Chinese Medicine (SCM) of Hong Kong Baptist University (HKBU) has developed the world’s first model framework and “LogicTRN” algorithm to accurately establish a gene regulatory route to analyse the genetic function and understand the biological processes that are responsible for the development of organs, progression of diseases and other complex biological events such as aging. Such a new mechanism could help efficiently locate the key regulatory route for complicated diseases, thereby facilitating the research and development of targeted therapy drugs. The research team has successfully applied the new integrative approach to analyse breast cancer and characterise the logical relations among transcription factors (TFs) in regulating biological processes. The work was recently published in Nature Communications.
Professor Lyu Aiping, dean of SCM, said that scientists have generated vast amounts and various types of biological big data; therefore, effective computational approaches must be developed to integrate and analyse such data to generate useful information for understanding the core mechanism of biological processes. It was difficult in the past to develop a reliable relationship for gene regulation due to the lack of a theoretical model. “LogicTRN” is a model structure based on the biological processes with significant impact on biology, with conclusions that can be verified using experiments. Therefore, the application of LogicTRN has key implications for our knowledge of the gene regulatory mechanism during biological processes.
TFs are functional proteins that can bind to gene promoters to turn gene expression on or off. Cells regulate many important processes through the gene expression controlled by TFs. TFs themselves are regulated by other genes upstream, thus creating a very complicated regulatory route. This complicated and combinatorial nature of TF regulation is the core of various cellular processes; it also explains why 2,000 to 3,000 TFs are enough to control the complex spatio-temporal expression of over 30,000 genes.
Dr Zhu said that “LogicTRN” is an open model framework which can be potentially extended to integrate the influences of various processes such as gene mutation, TF-DNA binding, miRNA regulation, protein translation, and protein-protein-interaction to decode the underlying mechanisms of gene transcription. With the acquisition and accumulation of biological data of more and more cellular processes, data analysis based on “LogicTRN” can enhance and contribute to a comprehensive understanding of molecular interactions in cells. He added that “LogicTRN” was successfully applied to analyse datasets representing the estrogen-induced breast cancer and human-induced pluripotent stem cell (hiPSC)-derived cardiomyocyte (CM) development. The derived networks are consistent with existing knowledge and previous experiments.
Professor Zhang Ge explained that the major problem in the areas of disease research and drug discovery is how to cure targeted genes. Untangling the complex relationship between genes poses difficulties to the study of crucial genes. The biological informative method that is currently used can identify the correlation between genes, however, the accuracy is still unsatisfactory and yet to be verified. Professor Zhang added that “LogicTRN” provides researchers with a comparatively powerful analytic tool in unravelling the key pathways and new therapeutic targets of complicated diseases such as cancer.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
Mechanism Controlling Multiple Sclerosis Risk IdentifiedNews
Researchers at Karolinska Institutet have now discovered a new mechanism of a major risk gene for multiple sclerosis (MS) that triggers disease through so-called epigenetic regulation. They also found a protective genetic variant that reduces the risk for MS through the same mechanism.
Synthetic DNA Shuffling Enzyme Outpaces Natural CounterpartNews
A new synthetic enzyme, crafted from DNA rather than protein, flips lipid molecules within the cell membrane, triggering a signal pathway that could be harnessed to induce cell death in cancer cells. Researchers say their lipid-scrambling DNA enzyme is the first in its class to outperform naturally occurring enzymes – and does so by three orders of magnitudeREAD MORE