Almost a century ago, researchers uncovered that reducing intake of calorie could extend lifespan in some animal species. However, despite many studies since, scientists have not been able to explain why. Today, researchers at the Lewis Katz School of Medicine at Temple University have gone past that barrier. In an article that was published in Nature Communications on 14th September 2017, these researchers are the first to demonstrate that the speed at which the epigenome varies with age is linked to lifespan in certain species and that restriction of calorie shows this change process, potentially explaining its effects on prolonged existence.
According to Jean-Pierre Issa, MD, Director of the Fels Institute for Cancer Research at LKSOM, the research shows that epigenetic change, which is characterized by losses and gains in DNA methylation in the genome after some time, happens faster in mice than in chimps and faster in chimps than in people. The study helps to explain the reasons why humans live about 70 or 80 years on average, rhesus monkeys about 25 years, and mice 3 to 4 years.
Dr.Issa and his team made their findings after examining patterns in methylation on DNA in blood gathered from individuals of diverse ages of each of three species- human, monkey, and mouse. Mice age ranged from a few months to about three years, chimps from at most three decades, and human from 0 to 86 years. Age-related differences in DNA methylation were examined by deep sequencing technology that shows distinct patterns. Gains in methylation in mature occurred at genomic areas that were not methylated in young individuals.
Scientists at the Helmholtz Zentrum München have created a new method that reconstructs continuous biological processes. The study was published in ‘Nature Communications.’
Today, life science is generating massive data in very short cycles. Making that data suitable and controllable for evaluation is the goal of the researchers at the Helmholtz Zentrum München’s Institute of Computational Biology. With that in mind, Dr. Alexander Wolf and his colleague are attempting to create software that handles this evaluation.
According to Wolf, the study dealt with the issues that software is unable to assign image data to continuous processes. For instance, it is possible to categorize information according to defined classifications, but in developmental biology and disease progression, the limits are rapidly reached since the processes are continuous. To take that into account, the researchers employed methods from machine learning processes. They combined pictures into processes and displayed them in a way that people understand.
To understand the capability of the method, the researchers selected two approaches. In the first experiment, the software was used to reconstruct the continuous cell cycle of leukocytes using pictures from an imaging flow cytometer. In the second example, the scientists reconstructed diabetic retinopathy progress. The software was feed with 30,000 retinas images as sparring partners. Since the software automatically compiles the data into a continuous process, it allows the researchers to predict the progression of disease on a continuous scale.
Computational systems biology is the use of mathematical and systems biology. It aims to develop efficient algorithms, visualization, and communication tools and data structures with the goal of modeling biological systems. Generally, computational systems biology uses simulations of biological systems.
Today, technological advances have a major impact on molecular biology. Developments in experimental methods mean a large volume of sequence, localization and expression data are now gathered by individual investigators. Additionally, large amounts of these data are kept in many private and public databases. At the same time, access to large-scale computing resources is becoming more common in laboratories of molecular biology. Computational systems biology helps us to learn how to leverage these advances in both computational and experimental resources.
To comprehensively understand biological systems, the integration of computational and experimental research is required. Through theoretical exploration and pragmatic modeling, computational biology provides a foundation from which to address scientific questions head-on. A study in computational systems biology allows individuals to solve biological challenges by combining computing, math and a strong base of biological concepts and knowledge. Individuals learn to approach challenges and formulate questions that span biological systems, from cells to genes to cells to ecology to medicine to evolution.