Programming Cells to Fight Diseases


New research carried out by Professor Alfonso Jaramillo and colleagues in the School of Life Sciences has discovered that ribonucleic acid (RNA) is produced in large quantities by animals and plants can be genetically engineered to allow researchers to program the activities of a cell.
Ribonucleic acid carries information about DNA and protein in cells, and the researchers have shown that these molecules can be made and organized into sequences of commands-similar to computer software codes-which give specific instructions, want.
It can be used in a wide variety of applications, from mobile phones to mobile phones, to mobile phones, and to mobile phones. .
The scientist made their discovery by first modeling all possible interactions of RNA sequence on a computer. They have constructed the deoxyribonucleic acid (DNA) encoding the RNA designs, validated on cells of a bacteria in the laboratory.
After making the bacterial cells to produce RNA sequences, the scientist observed that they had changed the gene expression according to the RNA program. This demonstrates that it is possible to program cells with pre-defined RNA commands.


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.

Applications of Computational Biology

Computational biology involves the application of mathematical modeling, computational simulation techniques, data analytics and theoretical methods to the study of behavioral, biological and social systems. When broadly defined, the field includes foundations in applied mathematics, computer science, chemistry, biochemistry, molecular biology, biophysics, genomics, genetics, evolution, ecology, neuroscience, anatomy, and visualization.
Initially, the study of computational biology focussed on the structure and sequence of biological molecules, mainly in an evolutionary context. However, beginning in the 1990s, it extended gradually to the analysis of function. Functional prediction focusses on the structural and sequence similarity between known and known proteins and analyses the interactions of proteins with other molecules. These analyses may be extensive, allowing computational biology to become aligned with systems biology.
Regulatory, biochemical, and genetic pathways are interleaved and branched, as well as dynamic. This calls for sophisticated computational methods for their modeling and analysis. Also, modern technology platforms for rapid generation of biological data have extended the traditional hypothesis-driven testing to data-driven analysis, allowing computational research to be performed on genome-wide databases of unparalleled scale. Many aspects of the study of biology, as a result, have become unthinkable without the power of computers and computer science methodologies.

Production of Life-Long Blood Depends on Many Cells that Form Before Birth

St. Jude Children’s Research Hospital researchers have discovered that production of life-long blood depends on more “ancestor” cells than earlier reported. Published in the journal Nature Cell Biology, the study focused on the origins of blood-forming cells before birth.
Hematopoietic or blood-forming stem cells are responsible for the production of life-long blood. The cells can make any blood cells. Therapeutically, hematopoietic stem cells are used to restore immunity and blood production in patients undergoing bone marrow transplantation for cancer treatment. Understanding how the blood system develops throughout prenatal growth provides insight into the roots of blood diseases that occur early in life.
In the study, St. Jude scientists used a mathematical modeling and color-coded cell labeling system to show blood-forming stem cells in mice arise from roughly 500 precursor cells rather than a few cells. While developments of blood system are the same in humans and mice, the precursor cells in mice are likely at least ten times fewer.
All previous studies had reported that only a few precursor cells take part in determining the blood system. However, the current study has shown that many cells are involved. The findings are expected to help the researchers unravel the origins of disease and find cells that are susceptible to disease-causing mutations.

A Computer Program that Distinguishes Human Cells

Although each of the cells in human body carries the same DNA sequence, there are many varieties of cell types and functions. The differences stem from how the sequence of the DNA is interpreted.
Recent developments in single-cell sequencing are allowing researchers to measure which of 20,000 genes in human is active in each cell. With more than 30 trillion cells in our body, the methods offer an unparalleled level of detail that is transforming research in medicine and biology. But when this technique is applied to numerous cells from various tissues, it turns out to be increasingly problematic to process the huge amounts of data and perceive meaningful patterns.
Stein Aerts, a computational biologist and Professor at the University of Leuven, and his team joined forces with bioengineers, IT specialists, and mathematicians to rise to the challenge. They developed a computer program that detects different types of cells based on their patterns of gene expression. Referred to as SCENIC, the program identifies different cell types quickly and accurately.
The researchers’ technique could help develop a cell “atlas” in the human body. The result is expected to become an invaluable source of information for biology and medicine.

Some 2 Percent of Human DNA is Neanderthal

The genetic legacy of Neanderthal could influence many things from sunburns to cholesterol and bad habits. There was a time when Neanderthals were considered as mindless brutes. However, that idea has long been proven wrong. Neanderthals, in many ways, were just like humans. They were also superior in some ways. Today, anthropologist know that humans and Neanderthals interbred, leaving humans with a percentage of their DNA.
In the new study, computational biologists Janet Kelso and Michael Dannemann looked at the link between DNA of Neanderthal and human behavior and appearance. Their analysis was broad because it included more than 100,000 individuals. However, it was also limited because all the data came from UK Biobank.
The researchers found the Neanderthal genes determine eye and hair color, sleep time preference, and even how badly you sunburn. It may be genes of Neanderthal that control whether you are a night owl or a morning person. However, according to Kay Prüfer, a researcher at the Max Planck Institute for Evolutionary Anthropology in Leipzig, people should not go accusing Neanderthals of all their woes. She co-authored a different study, in which people living in Western Eurasia were found to carry less Neanderthal gene than people in East Asians.
Prüfer and colleagues conducted a broad, high-quality sequencing of a Neanderthal genome. They studied the bones of a 52,000-year-old Neanderthal woman. They found that genes Neanderthal contribute 1.8 to 2.6% of the total genetic makeup of Eurasian people.