As farmers plan to plant, various questions come to mind: How does changing row spacing affect yields? How many plants germinated in my field last year? Is there any difference if I plant my rows east to west or north to south? Now a new3-D model can answer those by relating numerous virtual fields with diverse planting densities, orientations, and row spacing.
The Partner Institute for Computational Biology in Shanghai and the University of Illinois developed a computer model that calculates the yield of different crop cultivars in various planting conditions. The model shows the growth of 3D plants, integrating models of the biophysical and biochemical processes that underlie productivity.
Working closely with the University of Sao Paulo in Brazil, researchers from both universities used the model to answer questions for sugarcane producers. Double row spacing is recommended for sugarcane plants. However, this type of spacing sacrifices up to 10 percent of yield. Using the model, the researchers discovered that double-row spacing costs farmers 10% of yield compared to traditional row spacing.
Researchers predict that this model could be applied to various crops to predict best planting strategies for specific environments.
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.