Scientists Reveal Rules that Govern Formation of Ribs


Recently, researchers from the USC Stem Cell lab of Francesca Mariani shared a recipe for ribs. In a new publication, the scientists examine ribcage development, which protects the internal organs, supports the body and enables life on land.
In the study, the researchers describe a computational tool that simulates the choices that cells make when the ribcage develops in a mouse embryo. Several cells to form the bony part of each rib while some cells to make the cartilage part of each rib. Understanding this process requires the scientists to integrate the effects of cell communication, cell growth, and cell death in order to know how the skeleton is formed.
Using the model, the researchers suggest that the different levels of secreted protein (Hedgehog) are important for cells to determine whether cartilage or bone. High levels of Hedgehog facilitate the formation of bone sections. As the Hedgehog travels away from its source, its concentrations drop. Lower concentrations enable the cell to make cartilage component.
The decision of each cell to contribute to the cartilage of the bone section is made when the embryo is small and maintained as the embryo develops.


A New Computational Model that Estimates the Cause of Sudden Cardiac Death

A new model of heart tissues allows scientist to estimate the possibility of rare irregularities of heartbeat that trigger sudden cardiac death. Developed by researchers from Johns Hopkins University and IBM Research, the model was presented in PLOS Computational Biology.
An increased risk of cardiac death is mainly linked to some heart diseases. Sudden cardiac death occurs when arrhythmia (irregular heartbeat) interfere with normal electrical signaling in the heart, causing cardiac arrest. In the past, research has shown that spontaneous and simultaneous release of calcium by clusters surrounding heart cells can trigger premature heartbeats that cause these fatal arrhythmias.
Arrhythmias that can trigger sudden cardiac death, even though their importance is very rare. Using a “brute force” approach, for example, over one billion simulations would be needed to estimate the probability of an occurrence over 1 million chance of occurring.
In the new research, the scientist developed a technique that needs just hundreds of simulations to approximate the probability of arrhythmia. Powered by a computational model, these simulations incorporate molecular processes details that occur in heart cells.
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Looking At Disease as a Problem of the Whole Body Using a Mathematical Lens

Heart disease and hypertension arise from many interacting and contributing factors: food, exercise, and genetics, as well as problems with kidney, liver, or heart problem. Although people with such problems can find specialists in each distinct area, medicine and science often struggle to understand the role of each organ in the initiation and progression of the disease and the roles change over time, particularly in multifaceted diseases such as hypertension.
Recently, a multi-disciplinary team of scientists at Jefferson has developed a new computational tool that showed the process of the disease that causes hypertension start in the brain. The research was published in PLOS Computational Biology journal.
The tool integrates data from various organs at different time points, allowing the scientists to understand where complex diseases such as hypertension originate. The tool also helps researchers to understand how various organs contribute to the disease progression and development. The tool could be used to study other diseases process to know the best time to intervene.
Dr. Rajanikanth Vadigepalli, Professor at the Daniel Baugh Institute for Functional Genomics, and colleagues took an animal model of heart disease and hypertension– a particular rat strain whose hypertension and heart disease involves several organs– and studied samples from five main organs. By applying the mathematical tool they had developed, the scientists were able to identify the origin of the disease and identify the organ that drove the other organs in the body towards dysregulating blood pressure and finally causing heart disease.

New Model Suggests That Protein was Life’s First Molecule

Proteins have taken a back seat to Ribonucleic acid (RNA) molecules in speculations of researchers about the origin of life. However, a new computational model that explains how early biopolymers may have developed to fond into useful forms may change that. The model which is currently guiding experiments in the laboratory for confirmation could demonstrate that protein is the original self-replicating biomolecule.
For researchers studying how life began, one of the most important questions is: what molecule came first— proteins or nucleic acids? About 4 billion years ago, basic chemical building blocks grew to become polymers that could self-replicate and carry out functions vital to life: namely catalyzing chemical reactions and storing information. For many years, protein has handled the later job and the nucleic acid the latter one. RNA and DNA carry the instructions for the creation of proteins.
Elizaveta Guseva and Ken Dill of Stony Brook University in New York, and Ronald Zuckermann of the Lawrence Berkeley National Laboratory in California have developed a model that suggests protein could be the first life molecule. Although the RNA as the first molecule hypothesis reigns supreme, Zuckermann and Dill remain optimistic about what further study will yield. The researchers also plan to use the model to study other questions about how life started, including why and how the genetic code arose.


A New Machine Learning System That Automatically Classifies the Shapes of Red Blood Cells

Using a computational approach referred to as deep learning, researchers have developed a new system that identifies the shapes of red blood cells. Published in PLOS Computational Biology, these findings could potentially assist doctors to monitor individuals with sickle cell disease.
People with sickle cell disease produce red blood cells that are abnormally shaped. These cells can build up and blood vessels in the body, causing pain and at times death. Also referred by some people as sickle-cell anemia, sickle cell disease is named after crescent-like (sickle-shaped) red blood cell. However, it results in several other shapes such as elongated or oval red blood cells. Although the shape of red blood cells in a particular patient can determine the severity of their disease, it is difficult to manually identify these shapes.
Mengjia Xu of Northeastern University, China, and his colleagues have developed a computational framework that uses a machine-learning tool called a deep convolutional neural network (CNN). The framework automates the process of classifying the shapes of the red blood cells.
The new framework employs three steps to identify the red blood cells shapes in microscopic images of blood. It distinguishes the cells from each other and the background of each image. Then for each cell identified, it zooms in or out until the cells images are uniform in size. Lastly, it uses a deep convolutional neural network to classify the cells by shape.