Evolutionary Computation is a collective term for several problem-solving methods based on biological evolution principles, such as genetic inheritance and natural selection. In computer science, evolutionary computation is a family of algorithms inspired by biological evolution and artificial intelligence studying these algorithms.
In evolutionary computations, the first set of candidate solutions is produced and iteratively updated. A new generation is generated by stochastically eliminating less needed solutions, and introducing some small random changes. Biologically, a population of solutions is exposed to artificial selection (or natural section) and mutation. Consequently, the population gradually evolves to increase in fitness.
Evolutionary computing methods mainly involve metaheuristic optimization algorithms. The field includes ant colony optimization, artificial bee colony algorithm, artificial immune systems, artificial life, bees algorithm, cultural algorithms, differential evolution, dual-phase evolution, evolutionary algorithms, evolutionary programming, evolution strategy, gene expression programming, genetic algorithm, genetic programming, grammatical evolution, harmony search, learnable evolution model, learning classifier systems, particle swarm optimization, self-organization swarm and intelligence.
Evolutionary computation methods can generate highly optimized solutions in many problem settings. Numerous variants and extensions exist, suitable for more specific problems and data structures. Sometimes, evolutionary computation is also used in evolutionary biology. For instance, it is used in the silicon experimental process to study common features of general evolutionary processes.
Many diseases that are caused by genetic mutations are challenging to diagnose. Currently, sequencing of the entire genome of a patient—the genome part used to build proteins—offers a strategy to identify culprit mutations and consequently make an accurate diagnosis. Unfortunately, the software that is needed to examine these sequences is often too complex or costly for many medics to use.
To solve these problems, Cardenas’ team made a new software tool that is known as Mendel, MD. The scientist developed this tool specifically for easy use by doctors, free of charge. Using web-based interface, users upload the genome sequence of a patient, and the sequence is examined and filtered using different computational database and tools of disease-causing mutations. As a result, users get a list of candidate mutations that is clinically examined to arrive at a final diagnosis.
The scientists validated the new tool using previously-published clinical cases. The researchers also had it tested by students at their university, as well as at the Children’s University Hospital in Dublin, and Núcleo de Genética Médica (GENE) in Brazil. The results show that the tool is simple, reliable and efficient in detecting disease-causing mutations in patients.
Computational anatomy focuses on the study of anatomical form or shape at the gross or visible anatomical scale of morphology. It involves the application and development of mathematical, data-analytical and computational methods for modeling and simulations of biological structures.
Broadly defined, the field of computational anatomy includes foundations in applied mathematics and pure mathematics, anatomy, statistics, probability, physics, neuroscience, medical imaging, computational science, computational mechanics and machine learning. Also, it has strong connections with geometric mechanics and fluid mechanics. Also, it complements interdisciplinary fields such as neuroinformatics and bioinformatics in the sense that metadata from the original sensor imaging modalities is used in its interpretation.
Computational anatomy uses diffeomorphism group to study various coordinate systems through coordinate transformations as produced through the Lagrangian and Eulerian velocities of flow in R 3. In computational anatomy, the flows between coordinates are forced to be geodesic flows. Consequently, this satisfies the principle of least action for the Kinetic energy of the flow. In this case, kinetic energy is defined using a Sobolev smoothness norm.
Computational anatomy overlaps with the study of nonlinear global analysis and Riemannian manifolds, where diffeomorphisms groups are the main focus. In the field of computational anatomy, many studies concentrate on emerging high-dimensional theories of shape.
Computer modeling is the use of the computer to study the behavior of complex systems using computer science, physics, and mathematics. A computational model has many variables that describe the system under study. Simulation is carried out by changing each of these variables and observing how adjustments affect the results. The outcomes of models simulations assist researchers to make predictions about what will occur in the real system in response to varying conditions.
Today, a key feature of computational models is that they can study a biological system at many levels, including cell to cell interactions, molecular processes, and how those interactions and processes result in changes at the tissue or organ level. The study of a system at multiple levels is referred to as multiscale modeling.
Computational modeling is important because it enables researchers to simulate variations efficiently by computers, saving money, materials and time. It is used to study many complex systems including forecasting the weather, building better airplanes and conducting biomedical research.
For a long time, computational biology has been improving Medicare. Researchers have developed models of blood vessels, heart valves, and blood flow. These models are used in many ways, including enhancing the design of implanted devices. Computational models also help in the making of decision tools that doctors use to treat cardiovascular disease.
Bioinformatics is the application of statistics as well as computer science to the arena of molecular biology. It also referred to as the creation of statistical and computational techniques. Bioinformatics uses theories to give answers to practical and formal issues that arise from the management and examination of biological data. On the hand, computational biology is the use of various mathematical models and computational simulation models. Computational Biology field includes fundamentals in genetics, statistics, biochemistry, and computer science among others.
There are many courses available in both fields of computational biology and bioinformatics. Most of the students from bioinformatics field are employed in pharmaceutical and biotech companies. People interested in teaching get jobs as professors or lecturers in colleges that teach bioinformatics subject. On the other hand, students who study computational biology get jobs in institutes of research as consultants, research scientist or as Business Development Executives.
Both computational biology and bioinformatics have their uses in industries today. While bioinformatics is the development of tools to solve problems that arise in the biological data assessment, computation biology is the use of computational techniques to examine biology. While bioinformatics deals with engineering, computational biology is concerned with science.
Greek scientists have come up with a new bioinformatics tool that identifies potential therapies for chronic inflammatory diseases. The researchers used this approach to identify and confirm therapeutic potential of two molecules to target a protein known as Tumor Necrosis Factor (TNF) that is active in rheumatoid arthritis, multiple sclerosis and other diseases.
TNF is not only a key protein in almost all inflammatory processes but also have negative effects in chronic inflammatory diseases. For long, drug companies have been trying to develop anti-TNF treatments that target the protein, blocking TNF function. However existing therapies can be lethal and cause negative side effects. In addition, not all patients respond well to approve anti-TNF therapies.
Recently, the scientists developed a bioinformatics approach to virtually screen nearly 15,000 small molecules whose activities are not known. Concentrating on the protein chemical structures and compound, this new method identified all molecules that could interrupt Tumor Necrosis Factor and its receptor interaction.
Since both Tumor Necrosis Factor shares structural characteristics with RANK, another protein that is also involved in inflammatory processes, the researchers identified compounds that target pro-inflammatory proteins using their virtual screen tool. The scientists identified two small molecules (T8 and T23) that could interact with both TNF and RANKL.
According to a study in PLOS Computational Biology, different characteristics of brain tissue that surround the origin site of seizure’s may determine which of two major patterns of brain activity are seen before an epileptic seizure begin.
At the start of an epileptic seizure, the brain’s electrical activity follows either a “high amplitude slow” pattern or a “low amplitude fast” pattern. After surgical treatment, patients who have seizures that come after the high amplitude slow pattern are more likely to experience continuing seizures. However, the mechanisms that underlie these differing onset patterns are not clear.
To understand the onset of the patterns, a team of researchers from Newcastle University, U.K. used a previously made computer model that simulated brain activity at the onset of a seizure. The output of the model suggested that the initial seizure may be determined by characteristics of the neighbouring “healthy” brain tissue and not by brain tissue at the spot where the seizure originates.
The simulation suggested that the high amplitude slow pattern happens when the surrounding tissue of the brain is characterized by higher excitability; meaning that the brain cell strongly responds to stimulation and reacts immediately to seizure initiation. In the meantime, the low amplitude fast pattern occurs when surrounding brain tissue has lower excitability. Consequently, seizure activity penetrates them slowly.
The study shows why different treatment outcomes are associated with the different onset patterns. Surgically removing seizure-stimulating brain tissue may be enough to stop seizure activity in neighbouring low-excitability tissue. High-excitability tissue, however, may still be triggered by alternative trigger spots after surgery.
Each human house thousands of various types of microbes called the microbiota. In other terms, microbiotas are microbes in the human body that are involved in biological systems of the human body. For example, natural procedures help determine novel group of species in every individual
According to a research that was published in Science Daily, microbial species in the human body vary from individual to individual and this variation is as result of the variation in resources that are accessible to the microbes in the body.
Conducted by Charles Fisher and his colleagues from the Ecole Normale Superieure in Paris, the study examines how the microbes in the human body are impacted by the resource availability in the human body. The researchers developed a scientific mathematical model that shows the relationship between the abundance of different microbiota species and the resource accessibility they use inside the human body.
The analysis of the research on the microbes within the human body also showed that the species that are closely related in terms of taxonomy share the common resources. The research was focused on the microbiota from people who were healthy. The study can further assist scientists to examine the role of shared resources in controlling diseases that are related to the microbiota within the human body.
Researchers have identified two molecules that could treat inflammatory disease. Referred to as T23 and T8, these molecules inhibit the function of the protein known as tumour necrosis factor, which is involved in inflammatory diseases such as multiple sclerosis, psoriasis, Crohn’s disease, rheumatoid arthritis, and more. According to a paper that was published in PLOS Computational Biology, the scientists identified the molecules using a drug screening method they developed.
Led by Georgia Melagraki, the researchers from Greece and Cyprus came up with a new computer-based drug screening platform that aided them to discover better tumour necrosis factor inhibitor drugs. The platform integrates proprietary molecular characteristics shared between tumour necrosis factor and another protein known as RANK, which also plays a role in chronic inflammatory diseases.
The scientists developed the platform based on several advanced computational tools. Then, the platform was used to screen almost 15, 0000 molecules with unidentified activity. After that, they predicted the interactions of the molecules with RANKL proteins and tumour necrosis factor; particularly, how the molecules might interrupt the protein-protein interactions leading to activation of these vital proteins. Out of thousands of candidates, the experiment identified nine potential molecules.
To further evaluate the potential of the molecules, the researchers studied how the nine molecules interacted with RANKL and tumour necrosis factor in real-world laboratory experiments. T23 and T8 were identified as strong tumour necrosis factor inhibitor.
It is not easy to find a bioinformatics book that provides all the information in computing, biology, and mathematics fields. The few books available are very expensive. Books on computational biology can be grouped into books of general interest, those for biologists interested in bioinformatics and those best suited to individuals from the mathematical or computational background.
If you are interested in learning human genome, look for Matt Ridley’s “Genome”. The book offers an interesting introduction to issues raised by the revolution of bioinformatics. If you’re non-scientists, go for James Watson’s “The Double Helix“. The book enables the reader to understand the structure of the DNA. A broad introduction to key computational ideas applied in bioinformatics is provided in “Bioinformatics Algorithms: An Active Learning Approach” by Pavel Pevzner and Phillip Compeau.
If you are interested in mathematical/ Computational aspects, Michael Waterman’s “Introduction to Computational Biology“ is the best book for you. The book describes the computational the structure of biological data, especially from chromosomes and sequences. The text exposes the reader to structure of biological data and describes how to treat associated combinatorial and statistical problems.
One outstanding general book for biologists is “Bioinformatics” by David W. Mount. Although the book is quite expensive, it is one of the best books if you want to study bioinformatics.