Researchers have developed a computational model that correctly predicts the continuous and long-term metabolic syndrome progression in mice. Created by Yvonne Rozendaal of Eindhoven University of Technology and colleagues, the model was presented in PLOS Computational Biology.
Several factors are associated with metabolic factors: high lipid levels in the blood, high blood pressure, insulin resistance and obesity. A person suffering from metabolic syndrome faces higher risk of type 2 diabetes, non-alcoholic fatty liver disease and cardiovascular disease. Computational modeling of metabolic syndrome provides new insights into its growth. However, previous modeling efforts have failed to capture the complexity and the gradual progression of the disease.
Now, Rozendaal and colleagues have created a new computational model that describes lipid, cholesterol and glucose metabolism– key factors in metabolic syndrome. A simulation method that was previously was applied to the model, facilitating accurate calculation of gradual, long-term progression of the disease. The researchers then used data from real-world experiments to run the model in which mice were fed foods that resulted in metabolic syndrome development.
The scientists found that their modeling method accurately predicted f metabolic syndrome progression in the mice. It also accurately predicted the comorbidities, such as fatty liver disease.
Researchers have developed a new approach that can help us to understand animal preferences. The findings could lead to greater insight into human decision-making.
Pet owners are always faced with the challenges of knowing what their pets want. Using Pavlov’s work of training his dogs to associate the food with the ringing of the bell, scientists have tried to understand how human and animals react to rewards under various circumstances. In the real world, however, how a reward may motivate behavior and what constitutes a reward is rarely clear.
A team of researchers at Kyoto University’s Graduate School of Biostudies reported how worms asses a reward, examining the reaction of the worms by studying their movements. The study was published in PLOS Computational Biology.
The researchers observed worms as they looked for food in different temperature zones. Applying a machine learning method, the scientists were able to understand how food rewards guide the movements of the worms.
Initially, worms fed at particular temperatures moved toward that temperature area when moved to a plate with different surface temperatures. On the other hand, worms that were starved while in a certain temperatures moved away from that zone. The worms that were fed sensed both the environmental temperature and the change of temperature. These worms combined these sensations to form a behavioral strategy in order to reach food using an insignificant amount of energy, similar to rational decision-making in humans.
Recently, the 2018 PLOS Computational Biology Research Prize program awarded top awards to three exemplary studies that were published last year. Launched last year, the program celebrates some of most outstanding research articles in the journal.
This year, winners stood out from many studies voted by the public in three classes: Public Impact, Exemplary Methods/Software, and breakthrough Advance/Innovation. A committee composed of editorial board members selected the final winners in each category.
A study that addressed structure of proteins using ideas from the ground-breaking computer science field called deep learning took won the top Breakthrough Advance/Innovation award. Led by Sheng Wang, the researchers developed a novel deep learning technique that improves estimates of how proteins assemble and fold into their final 3-D forms, helping to reveal new biological insights.
In the Exemplary Methods/Software class, a new program known as Unicycler, which collects bacterial genomes from DNA sequencing data took the prize. Developed by Ryan Wick and other researchers at The University of Melbourne, the program effectively combines accurate but short sequences of DNA with longer but error-prone data, outperforming other hybrid approaches.
The top study in the Public Impact category presented a new statistical analysis approach for finding cancer-causing genetic mutations. Thomas Peterson and colleagues used their new approach to identify numerous mutations associated with cancer.
Computational biology makes use of algorithms to establish relations between various biological systems. By creating sophisticated simulation models for pharmacokinetics and pharmacodynamics, several drug developments and discovery have made use of bioinformatics with successful results. Consequently, thus has encouraged the adoption of this method among many companies in the pharmaceuticals industry globally. Computational biology is important because it helps reduce the number of human candidates needed to test drugs in the development stage.
According to Transparency Market Research, the lack of widespread standardization in techniques and processed impact on the growth of the computational biology market. Similarly, the availability of non-predictive models could fail to come up with the desired results, resulting in consumers losing confidence in computational biology.
The world’s computational biology market is segmented on the basis of tools, application and geography. Application is further divided into simulation applications and disease modeling. Simulation applications and disease modeling is further divided into drug development and drug discovery.
The findings of the study show that by application, the growth of the drug discovery areas will be the fastest in the bioinformatics market, with a CAGR of more than 25 per cent through the report’s forecast period.
Biology is a science of life and living organisms. This study entails everything in the living things like the development, structures and physical evolution. Computers are studying tools that are used in the study of biology. These machines of study are of different kinds.
We shall use an example of study of Microbiome. Microbiomes are enzymes that are found in the tract of a human being. So far, the importance of the enzymes is not yet identified. They are expected that they participate and play a major role in the life of a human being existence. Proteins found in this microbial are used to carry out chemical reactions in a living organism. Composition of these enzymes is macromolecular biology catalysts and enzymes. The yet still unknown role of these microbiome is suspected to be crucial in the health of a human being.
The innovation published in the science of a certain machine forms a solution. Chemical biology Emily Balskus in collaboration with Curtis Huttenhowe professor at Harvard school of public health made the machine invention. The tool is expected to help in the identifying the role of the numerous enzymes in Microbiome. It will involve in explaining the roles and the number of the content. With the help of this machine, there will be differentiation of work of each enzyme since they share most similarities and characteristics.
For scientists studying the possible links between human health and the microbes that live our system, what makes the work exciting is also what makes it challenging.
Despite many years of work, including sequencing microbes in the gut of volunteers, the roles of proteins available in this microbial community still remain a mystery. Majority of these proteins are enzymes, macromolecular biological catalysts that allow living organisms to carry out chemical reactions. Although currently unrecognized, enzymes in the human tract microbiome could be performing chemical processes that are critical for human health
A new tool developed by Chemical Biology Emily Balskus and Morris Kahn Associate Professor of Chemistry in collaboration with Curtis Huttenhowe, a Professor of Computational Biology and Bioinformatics at the Harvard T.H. Chan School of Public Health, might help scientists more accurately find enzymes present in microbiomes and put a figure on their relative abundances. The findings were published in Science.
It has already proven useful– with the new approach, Huttenhower and Balskus were able, for the first time; appreciate the importance of uncharacterized glycyl radical enzyme in a human gut microbiome. In addition, they were able to explain what this enzyme actually does.
Without their tool, Huttenhower and Balskus believe it is incredibly difficult to discover novel chemistry in the gut microbiome because of the similarities shared by numerous enzymes.
During growth, when a fertilized egg changes its shape dramatically into a body of an animal, cell populations making tissues are in a fluid state and therefore the tissue can be easily deformed. During development, cells also produce mechanical shape tissues and organs. But how do organisms proceed/secure with proper formation of tissue and organ formation in these surroundings? In order to understand the process of organ and tissue formations, it is important to understand how physical features of tissues responding to mechanical forces influence organogenesis process.
To tackle this problem, researchers from the National Institute for Basic Biology (NIBB) and Kyoto University have developed a unique non-invasive method of measuring the tissues hardness by merging physical modeling of the tissues with arithmetical estimation.
The effectiveness of the suggested method has been experimentally confirmed with monolayer-cultured cells. The team of researchers also showed that the stiffness/ hardness of the tissues can be altered depending on the physical properties of materials comprising the cell’s activities controlled by molecular motor, etc. The team hopes that this work will serve as an imperative step towards more understanding of the mechanism of organogenesis. The findings of this research were published in the journal, PLoS Computational Biology.
Ants carrying a large piece of food together get around obstacles by either squeezing the morsel through a hole or seeking a path around the barrier. These findings were published in PLOS Computational Biology.
When ants carry food items such as maggots or worms, they always find obstacles in their path. Working together, the ants need to reach an agreement and decide on a new path to their nest. Although previous studies have shown that alternating, side-to-side movements can help ants to go around an obstacle, what drives this movement and determines whether it will occur has been unclear.
Jonathan Ron of the Weizmann Institute, Israel, and colleagues built a mathematical model that simulates the behavior of ants when facing a fixed barrier with a narrow hole. The simulations show that ants switch between two modes of motion, one in which sideways movements allow them to find their way around the obstacle, and another in which they dwell close to the hole, providing a chance to move food through it.
The model also shows that size of the ant group, depending on the size of food that is being carried, determines the mode of movement that will dominate. To get around the barrier, larger groups of ants perform more sideways motions while small groups spend more time near the hole.
Social network analysis can mine data from the internet to potentially influence elections or target advertisements. But what if people could use those same tools not for the political or economic gain of a few people, but for the health of all humankind? Researchers now can use the tools of social network analysis to understand links between genes, a development that one day could lead to medical advancements.
Recently, Dr. Megha Padi, director of the UA Cancer Center Bioinformatics Shared Resource, developed a computer algorithm known as ALPACA that shows which gene networks are stimulated in a diseased cell – a method that could result in better treatments for a variety of diseases. The findings were published in the open-access Nature Partner journal Systems Biology and Applications 19th April 2018.
Usually, cancer researchers focus on specific genes when comparing tumor cells to healthy cells, a method that does not entirely explain what happen behind the scenes to cause cancer.
Since you cannot understand what makes a vehicle run until you understand how various parts are connected, it is important to study how genes function together as part of a larger network. Therefore, Dr. Padi is studying these gene communities in the similar way one would study a social network composed of networks among people who know one another.
ALPACA’s incorporation of social network study is an innovative use of tools most commonly related to marketing, not medical research.
The Computational Biology group at the Indian Institute of Technology Roorkee (IIT) has developed a heart app. The new app remotely monitors people at risk of heart failure and offer them medical assistance. In case of any changes in patient’s data showing a possibility of heart failure, the app automatically sends a notification to both the patient and the doctor.
Referred to as Dhadkan, the app is expected to be of great help in India, where there are about 10 million people at risk of heart failure. Available for free, the app is designed to be ‘easy-to-use,’ allowing all people to benefit from it.
According to Deepak Sharma, Assistant Professor, Department of Biotechnology, who led the team, the app collects patient’s data including blood pressure, weight, heart rate, and transmits it to the authorized care-giver (a nurse or doctor) who is linked to the patient during initial registration.
The app also offers two-way communication between patients and doctors. In addition, it allows patients to send report(s) to the doctor. The app eliminates the need for manual monitoring of patients by the doctor.
Speaking on its significance, Sharma said, the app will be of great help to people who live in distant areas and cannot visit hospitals at regular intervals.