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Relevant bibliographies by topics / Biological simulation

Academic literature on the topic 'Biological simulation'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Biological simulation"

1

Adeeb, Samer, and Walter Herzog. "Simulation of biological growth." Computer Methods in Biomechanics and Biomedical Engineering 12, no. 6 (December 2009): 617–26. http://dx.doi.org/10.1080/10255840902802909.

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Li, Yacong, Kuanquan Wang, Qince Li, and Henggui Zhang. "Biological pacemaker: from biological experiments to computational simulation." Journal of Zhejiang University-SCIENCE B 21, no. 7 (July 2020): 524–36. http://dx.doi.org/10.1631/jzus.b1900632.

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Rhodes, Oliver, Luca Peres, Andrew G. D. Rowley, Andrew Gait, Luis A. Plana, Christian Brenninkmeijer, and Steve B. Furber. "Real-time cortical simulation on neuromorphic hardware." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (December 23, 2019): 20190160. http://dx.doi.org/10.1098/rsta.2019.0160.

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Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm 2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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Read, Mark N., Kieran Alden, Louis M. Rose, and Jon Timmis. "Automated multi-objective calibration of biological agent-based simulations." Journal of The Royal Society Interface 13, no. 122 (September 2016): 20160543. http://dx.doi.org/10.1098/rsif.2016.0543.

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Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives ) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.

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Mehta, Shalin B., and Rudolf Oldenbourg. "Image simulation for biological microscopy: microlith." Biomedical Optics Express 5, no. 6 (May 13, 2014): 1822. http://dx.doi.org/10.1364/boe.5.001822.

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Dasgupta, Subinay. "A computer simulation for biological ageing." Journal de Physique I 4, no. 10 (October 1994): 1563–70. http://dx.doi.org/10.1051/jp1:1994207.

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Mattheck, C. "Computer simulation of adaptive biological growth." Journal of Biomechanics 25, no. 7 (July 1992): 780. http://dx.doi.org/10.1016/0021-9290(92)90513-z.

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Albi, Giacomo, Marco Artina, Massimo Foransier, and Peter A. Markowich. "Biological transportation networks: Modeling and simulation." Analysis and Applications 14, no. 01 (January 2016): 185–206. http://dx.doi.org/10.1142/s0219530515400059.

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We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

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Fischle, Andreas, Axel Klawonn, Oliver Rheinbach, and Jörg Schröder. "Parallel Simulation of Biological Soft Tissue." PAMM 12, no. 1 (December 2012): 767–68. http://dx.doi.org/10.1002/pamm.201210372.

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Woods, Christopher J., Muan Hong Ng, Steven Johnston, Stuart E. Murdock, Bing Wu, Kaihsu Tai, Hans Fangohr, et al. "Grid computing and biomolecular simulation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (July 26, 2005): 2017–35. http://dx.doi.org/10.1098/rsta.2005.1626.

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Biomolecular computer simulations are now widely used not only in an academic setting to understand the fundamental role of molecular dynamics on biological function, but also in the industrial context to assist in drug design. In this paper, two applications of Grid computing to this area will be outlined. The first, involving the coupling of distributed computing resources to dedicated Beowulf clusters, is targeted at simulating protein conformational change using the Replica Exchange methodology. In the second, the rationale and design of a database of biomolecular simulation trajectories is described. Both applications illustrate the increasingly important role modern computational methods are playing in the life sciences.

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Dissertations / Theses on the topic "Biological simulation"

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Miller, Thomas F. "Quantum simulation of biological molecules." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414234.

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Hoyles, Matthew, and Matthew Hoyles@anu edu au. "Computer Simulation of Biological Ion Channels." The Australian National University. Theoretical Physics, 2000. http://thesis.anu.edu.au./public/adt-ANU20010702.135814.

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This thesis describes a project in which algorithms are developed for the rapid and accurate solution of Poisson's equation in the presence of a dielectric boundary and multiple point charges. These algorithms are then used to perform Brownian dynamics simulations on realistic models of biological ion channels. An iterative method of solution, in which the dielectric boundary is tiled with variable sized surface charge sectors, provides the flexibility to deal with arbitrarily shaped boundaries, but is too slow to perform Brownian dynamics. An analytical solution is derived, which is faster and more accurate, but only works for a toroidal boundary. Finally, a method is developed of pre-calculating solutions to Poisson's equation and storing them in tables. The solution for a particular configuration of ions in the channel can then be assembled by interpolation from the tables and application of the principle of superposition. This algorithm combines the flexibility of the iterative method with greater speed even than the analytical method, and is fast enough that channel conductance can be predicted. The results of simulations for a model single-ion channel, based on the acetylcholine receptor channel, show that the narrow pore through the low dielectric strength medium of the protein creates an energy barrier which restricts the permeation of ions. They further show that this barrier can be removed by dipoles in the neck of the channel, but that the barrier is not removed by shielding by counter-ions. The results of simulations for a model multi-ion channel, based on a bacterial potassium channel, show that the model channel has conductance characteristics similar to those of real potassium channels. Ions appear to move through the model multi-ion channel via rapid transitions between a series of semi-stable states. This observation suggests a possible physical basis for the reaction rate theory of channel conductance, and opens up an avenue for future research.

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Corry, Ben Alexander, and ben corry@anu edu au. "Simulation Studies of Biological Ion Channels." The Australian National University. Research School of Physical Sciences and Engineering, 2003. http://thesis.anu.edu.au./public/adt-ANU20030423.162927.

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Biological ion channels are responsible for, and regulate the communication system in the body. In this thesis I develop, test and apply theoretical models of ion channels, that can relate their structure to their functional properties. Brownian dynamics simulations are introduced, in which the motions of individual ions are simulated as they move through the channel and in baths attached to each end. The techniques for setting boundary conditions which maintain ion concentrations in the baths and provide a driving potential are tested. Provided the bath size is large enough, all boundary conditions studied yield the same results. ¶ Continuum theories of electrolytes have previously been used to study ion permeation. However, I show that these continuum models do not accurately reproduce the physics taking place inside ion channels by directly comparing the results of both equilibrium Poisson-Boltzmann theory, and non-equilibrium Poisson-Nernst-Planck theory to simulations. In both cases spurious shielding effects are found to cancel out forces that play an important role in ion permeation. In particular, the `reaction field' created by the ion entering the narrow channel is underestimated. Attempts to correct these problems by adding extra force terms to account for this reaction field also fail. ¶ A model of the L-type calcium channel is presented and studied using Brownian dynamics simulations and electrostatic calculations. The mechanisms of permeation and selectivity are explained as the result of simple electrostatic interactions between ions and the fixed charges in the protein. The complex conductance properties of the channel, including the current-voltage and current-concentration relationships, the anomalous mole fraction behaviour between sodium and calcium ions, the attenuation of calcium currents by monovalent ions and the effects of mutating glutamate residues, are all reproduced. ¶ Finally, the simulation and electrostatic calculation methods are used to study the gramicidin A channel. It is found that the continuum electrostatic calculations break down in this narrow channel, as the concept of applying a uniform dielectric constant is not accurate in this situation. Thus, the permeation properties of the channel are examined using Brownian dynamics simulations without electrostatic calculations. Future applications and improvements of the Brownian dynamics simulation technique are also described.

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Rackauckas, Christopher Vincent. "Simulation and Control of Biological Stochasticity." Thesis, University of California, Irvine, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10827971.

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Stochastic models of biochemical interactions elucidate essential properties of the network which are not accessible to deterministic modeling. In this thesis it is described how a network motif, the proportional-reversibility interaction with active intermediate states, gives rise to the ability for the variance of biochemical signals to be controlled without changing the mean, a property designated as mean-independent noise control (MINC). This noise control is demonstrated to be essential for macro-scale biological processes via spatial models of the zebrafish hindbrain boundary sharpening. Additionally, the ability to deduce noise origin from the aggregate noise properties is shown.

However, these large-scale stochastic models of developmental processes required significant advances in the methodology and tooling for solving stochastic differential equations. Two improvements to stochastic integration methods, an efficient method for time stepping adaptivity on high order stochastic Runge-Kutta methods termed Rejection Sampling with Memory (RSwM) and optimal-stability stochastic Runge-Kutta methods, are combined to give over 1000 times speedups on biological models over previously used methodologies. In addition, a new software for solving differential equations in the Julia programming language is detailed. Its unique features for handling complex biological models, along with its high performance (routinely benchmarking as faster than classic C++ and Fortran integrators of similar implementations) and new methods, give rise to an accessible tool for simulation of large-scale stochastic biological models.

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Corry, Ben Alexander. "Simulation studies of biological ion channels." View thesis entry in Australian Digital Theses Program, 2002. http://thesis.anu.edu.au/public/adt-ANU20030423.162927/index.html.

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Yngve, Gary. "Visualization for biological models, simulation, and ontologies /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/6912.

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Wang, Eric Yiqing. "Comparison Between Deterministic and Stochastic Biological Simulation." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230732.

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Millar, Campbell. "3D simulation techniques for biological ion channels." Thesis, University of Glasgow, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401999.

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Mishra, Shikta. "Modeling and Simulation of Cutting in Soft Biological Tissues for Surgical Simulation." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352994028.

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Topkaya, Pinar. "Computer Simulation Of A Complete Biological Treatment Plant." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609708/index.pdf.

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Nitrogen and phosphorus removal is often required before discharge of treated wastewater to sensitive water bodies. Kayseri Wastewater Treatment Plant (KWWTP) is a biological wastewater treatment plant that includes nitrogen and phosphorus removal along with carbon removal. The KWWTP receives both municipal wastewater and industrial wastewaters. In this study, KWWTP was modeled by using a software called GPS-X, which is developed for modeling municipal and industrial wastewaters. The Activated Sludge Model No.2d (ASM2d) developed by the International Association on Water Quality (IAWQ) was used for the simulation of the treatment plant. In this model, carbon oxidation, nitrification, denitrification and biological phosphorus removal are simulated at the same time. During the calibration of the model, initially, sensitivities of the model parameters were analyzed. After sensitivity analysis, dynamic parameter estimation (DPE) was carried out for the optimization of the sensitive parameters. Real plant data obtained from KWWTP were used for DPE. The calibrated model was validated by using different sets of data taken from various seasons after necessary temperature adjustments made on the model. Considerably good fits were obtained for removal of chemical oxygen demand (COD), total suspended solids (TSS) and nitrogen related compounds. However, the results for phosphorus removal were not satisfactory, probably due to lack of information on volatile fatty acids concentration and alkalinity of the influent wastewater.

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Books on the topic "Biological simulation"

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Laubenbacher, Reinhard, ed. Modeling and Simulation of Biological Networks. Providence, Rhode Island: American Mathematical Society, 2007. http://dx.doi.org/10.1090/psapm/064.

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Bock, Gregory, and Jamie A. Goode, eds. ‘In Silico’ Simulation of Biological Processes. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470857897.

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A, Gruver W., and Grisell R. D, eds. Simulation and identification in biological science. San Bernardino, Calif: Borgo Press, 1986.

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Goel, Narendra S. Computer simulations of self-organization in biological systems. London: Croom Helm, 1988., 1988.

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Goel, Narendra S. Computer simulations of self-organization in biological systems. London: Croom Helm, 1988.

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Kholmurodov, Kholmirzo. Molecular simulation in material and biological research. Hauppauge, NY: Nova Science Publishers, 2009.

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Matthias, Ruth, ed. Modeling dynamic biological systems. New York: Springer, 1997.

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Datta, Ashim K. An introduction to modeling of transport processes: Applications to biomedical systems. Cambridge: Cambridge University Press, 2010.

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C, Pozrikidis, ed. Modeling and simulation of capsules and biological cells. Boca Raton, Fla: Chapman & Hall/CRC, 2003.

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Huang, Barney K. Computer simulation analysis of biological and agricultural systems. Boca Raton, FL: CRC Press, 1994.

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Book chapters on the topic "Biological simulation"

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Haefner, James W. "Simulation Paradigms." In Modeling Biological Systems, 101–17. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-4119-6_5.

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Shin, James J., David Elad, and Roger D. Kamm. "Simulation of Forced Breathing Maneuvers." In Biological Flows, 287–313. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-9471-7_15.

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Perktold, K., and G. Rappitsch. "Computer Simulation of Arterial Blood Flow." In Biological Flows, 83–114. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-9471-7_6.

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Kerckhoffs, E. J. H., and G. C. Vansteenkiste. "Parallel Processing In Biological Systems." In Advanced Simulation in Biomedicine, 1–9. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4419-8614-6_1.

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Huth, Andreas, and Christian Wissel. "The Movement of Fish Schools: A Simulation Model." In Biological Motion, 577–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51664-1_39.

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Nava, Alessandro, Edoardo Mazza, Oliver Haefner, and Michael Bajka. "Experimental Observation and Modelling of Preconditioning in Soft Biological Tissues." In Medical Simulation, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25968-8_1.

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Amar, Patrick, Muriel Baillieul, Dominique Barth, Bertrand LeCun, Franck Quessette, and Sandrine Vial. "Parallel Biological In Silico Simulation." In Information Sciences and Systems 2014, 387–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09465-6_40.

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Focardi, Stefano, Giacorao Santini, and Guido Chelazz. "A Simulation Model of Foraging Excursions in Intertidal Chitons." In Biological Motion, 319–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51664-1_23.

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Belykh, Evgenii, Michael A. Bohl, Kaith K. Almefty, Mark C. Preul, and Peter Nakaji. "Biological Models for Neurosurgical Training in Microanastomosis." In Comprehensive Healthcare Simulation: Neurosurgery, 91–102. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75583-0_7.

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Stouch, Terry R., and Donna Bassolino. "Movement of Small Molecules in Lipid Bilayers: Molecular Dynamics Simulation Studies." In Biological Membranes, 255–79. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4684-8580-6_8.

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Conference papers on the topic "Biological simulation"

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Hampton, Scott, Pratul K. Agarwal, Sadaf R. Alam, and Paul S. Crozier. "Towards microsecond biological molecular dynamics simulations on hybrid processors." In Simulation (HPCS). IEEE, 2010. http://dx.doi.org/10.1109/hpcs.2010.5547149.

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Harel, David. "Comprehensive and Realistic Modeling of Biological Systems." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.322936.

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Mustafee, Navonil. "Session details: Biological Systems." In SIGSIM-PADS '15: SIGSIM Principles of Advanced Discrete Simulation. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/3247425.

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Clark, Allan, Jane Hillston, Stephen Gilmore, and Peter Kemper. "VERIFICATION AND TESTING OF BIOLOGICAL MODELS." In 2010 Winter Simulation Conference - (WSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/wsc.2010.5679126.

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"Parsing and Model Generation for Biological Processes." In 2016 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2016. http://dx.doi.org/10.22360/springsim.2016.tmsdevs.047.

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Nicolae, Marian. "INFORMATIC SIMULATION OF PLANTS BIOLOGICAL PROCESSES." In 18th International Multidisciplinary Scientific GeoConference SGEM2018. Stef92 Technology, 2018. http://dx.doi.org/10.5593/sgem2018/2.1/s07.060.

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Pickover, Clifford A. "The Fractal Simulation Of Biological Shapes." In Biostereometrics '88: Fifth Intl Mtg, edited by Juerg U. Baumann and Robin E. Herron. SPIE, 1989. http://dx.doi.org/10.1117/12.950472.

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Burrage, Kevin, and Tianhai Tian. "Effective simulation techniques for biological systems." In Second International Symposium on Fluctuations and Noise, edited by Derek Abbott, Sergey M. Bezrukov, Andras Der, and Angel Sanchez. SPIE, 2004. http://dx.doi.org/10.1117/12.548672.

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Paltanea, Marius, Sabin Tabirca, Ernesc Scheiber, and Mark Tangney. "Logarithmic Growth in Biological Processes." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.29.

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Unger, Andrea, Susanne Biermann, Mathias John, Adelinde Uhrmacher, and Heidrun Schumann. "Visual support for modeling and simulation of cell biological systems." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419893.

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Reports on the topic "Biological simulation"

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Acosta, Felipe, Guillermo Riveros, Reena Patel, and Wayne Hodo. Numerical simulation of biological structures : paddlefish rostrum. Geotechnical and Structures Laboratory (U.S.), May 2019. http://dx.doi.org/10.21079/11681/32749.

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Комарова, Олена Володимирівна, and Альберт Армаїсович Азарян. Computer Simulation of Biological Processes at the High School. CEUR Workshop Proceedings (CEUR-WS.org), 2018. http://dx.doi.org/10.31812/123456789/2695.

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Abstract. Research goals: the necessity of study in high school of the law of Hardy – Weinberg as one of the fundamental genetic laws was justified. The peculiarities of using the method of model experiment in the study of the genetic and evolutionary processes in populations with the use of computer technology. Object of research: computer simulation of population genetic structure. Subject of research: computer simulation of genetic and evolutionary processes in ideal and real populations. Research methods: pedagogical experiment (survey), analysis of scientific publications on the use of the high school method of modelling genetic and evolutionary processes in populations, computer simulation. Results of the research: a web page for processing by the pupils of the modelling results of genetic and evolutionary processes in populations was created.

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Комарова, Олена Володимирівна, and Альберт Арамаїсович Азарян. Computer Simulation of Biological Processes at the High School. CEUR-WS.org, 2018. http://dx.doi.org/10.31812/123456789/2656.

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Research goals: the necessity of study in high school of the law of Hardy – Weinberg as one of the fundamental genetic laws was justified. The peculiarities of using the method of model experiment in the study of the genetic and evolutionary processes in populations with the use of computer technology. Object of research: computer simulation of population genetic structure. Subject of research: computer simulation of genetic and evolutionary processes in ideal and real populations. Research methods: pedagogical experiment (survey), analysis of scientific publications on the use of the high school method of modelling genetic and evolutionary processes in populations, computer simulation. Results of the research: a web page for processing by the pupils of the modelling results of genetic and evolutionary processes in populations was created.

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BOOKER, C. P., and W. G. LACEY. VIRTUAL SIMULATION TESTBED FOR BIOLOGICAL DEFENSE: A COMPOSITION EVNIRONMENT FOR SIMULATION DEVELOPMENT. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/787261.

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Chai, Fei, and Emmanuel Boss. Physical-Biological-Optics Model Development and Simulation for the Monterey Bay, California. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada516870.

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Chai, Fei, and Emmanuel Boss. Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada540700.

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Chai, Fei, and Emmanuel Boss. Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada541221.

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Chai, Fei, and Emmanuel Boss. Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572747.

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Chai, Fei, and Emmanuel Boss. Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557142.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

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