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In silico

From Wikipedia, the free encyclopedia
A forest of syntheticpyramidaldendritesgeneratedin silicousingCajal's laws of neuronal branching

Inbiologyand other experimental sciences, anin silicoexperiment is one performed on a computer or viacomputer simulationsoftware. The phrase ispseudo-Latinfor 'in silicon' (correctLatin:in silicio), referring tosiliconin computer chips. It was coined in 1987 as an allusion to theLatin phrasesin vivo,in vitro,andin situ,which are commonly used inbiology(especiallysystems biology). The latter phrases refer, respectively, to experiments done in living organisms, outside living organisms, and where they are found in nature.

History

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The earliest known use of the phrase was byChristopher Langtonto describeartificial life,in the announcement of a workshop on that subject at the Center for Nonlinear Studies at theLos Alamos National Laboratoryin 1987.[1][2]The expressionin silicowas first used to characterize biological experiments carried out entirely in a computer in 1989, in the workshop "Cellular Automata: Theory and Applications" in Los Alamos, New Mexico, by Pedro Miramontes, a mathematician fromNational Autonomous University of Mexico(UNAM), presenting the report "DNAandRNAPhysicochemical Constraints, Cellular Automata and Molecular Evolution ". The work was later presented by Miramontes as hisdissertation.[3]

In silicohas been used inwhite paperswritten to support the creation of bacterial genome programs by the Commission of the European Community. The first referenced paper wherein silicoappears was written by a French team in 1991.[4]The first referenced book chapter wherein silicoappears was written by Hans B. Sieburg in 1990 and presented during a Summer School on Complex Systems at the Santa Fe Institute.[5]

The phrasein silicooriginally applied only to computer simulations that modeled natural or laboratory processes (in all the natural sciences), and did not refer to calculations done by computer generically.

Drug discovery with virtual screening

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In silico study in medicine is thought to have the potential to speed the rate of discovery while reducing the need for expensive lab work and clinical trials. One way to achieve this is by producing and screening drug candidates more effectively. In 2010, for example, using the protein docking algorithm EADock (seeProtein-ligand docking), researchers found potential inhibitors to an enzyme associated with cancer activityin silico.Fifty percent of the molecules were later shown to be active inhibitorsin vitro.[6][7]This approach differs from use of expensivehigh-throughput screening(HTS) robotic labs to physically test thousands of diverse compounds a day, often with an expected hit rate on the order of 1% or less, with still fewer expected to be real leads following further testing (seedrug discovery).

As an example, the technique was utilized for adrug repurposingstudy in order to search for potential cures forCOVID-19(SARS-CoV-2).[8]

Cell models

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Efforts have been made to establish computer models of cellular behavior. For example, in 2007 researchers developed an in silico model oftuberculosisto aid in drug discovery, with the prime benefit of its being faster than real time simulated growth rates, allowing phenomena of interest to be observed in minutes rather than months.[9]More work can be found that focus on modeling a particular cellular process such as the growth cycle ofCaulobacter crescentus.[10]

These efforts fall far short of an exact, fully predictive computer model of a cell's entire behavior. Limitations in the understanding ofmolecular dynamicsandcell biology,as well as the absence of available computer processing power, force large simplifying assumptions that constrain the usefulness of present in silico cell models.

Genetics

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Digital genetic sequencesobtained fromDNA sequencingmay be stored insequence databases,be analyzed (seeSequence analysis), be digitally altered or be used as templates for creating new actual DNA usingartificial gene synthesis.

Other examples

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In silico computer-based modeling technologies have also been applied in:

See also

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References

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  1. ^"Google Groups".groups.google.com.Retrieved2020-01-05.
  2. ^Hameroff, S. R. (2014-04-11).Ultimate Computing: Biomolecular Consciousness and NanoTechnology.Elsevier.ISBN978-0-444-60009-7.
  3. ^Miramontes P. (1992)Un modelo de autómata celular para la evolución de los ácidos nucleicos[A cellular automaton model for the evolution of nucleic acids]. PhD Thesis. UNAM.
  4. ^Danchin, A; Médigue, C; Gascuel, O; Soldano, H; Hénaut, A (1991), "From data banks to data bases",Research in Microbiology,142(7–8): 913–6,CiteSeerX10.1.1.637.3244,doi:10.1016/0923-2508(91)90073-J,PMID1784830
  5. ^Sieburg, H.B. (1990), "Physiological Studiesin silico",Studies in the Sciences of Complexity,12:321–342
  6. ^Röhrig, Ute F.; Awad, Loay; Grosdidier, AuréLien; Larrieu, Pierre; Stroobant, Vincent; Colau, Didier; Cerundolo, Vincenzo; Simpson, Andrew J. G.; et al. (2010), "Rational Design of Indoleamine 2,3-Dioxygenase Inhibitors",Journal of Medicinal Chemistry,53(3): 1172–89,doi:10.1021/jm9014718,PMID20055453
  7. ^Ludwig Institute for Cancer Research (2010, February 4).New computational tool for cancer treatment.ScienceDaily.Retrieved February 12, 2010.
  8. ^Lee, Vannajan Sanghiran; Chong, Wei Lim; Sukumaran, Sri Devi; Nimmanpipug, Pivarat; Letchumanan, Vengadesh; Goh, Bey Hing; Lee, Learn-Han; Md. Zain, Sharifuddin; Abd Rahman, Noorsaadah (2020)."Computational screening and identifying binding interaction of anti-viral and anti-malarial drugs: Toward the potential cure for SARS-CoV-2".Progress in Drug Discovery & Biomedical Science.3.doi:10.36877/pddbs.a0000065.
  9. ^University Of Surrey. June 25, 2007.In Silico Cell For TB Drug Discovery.ScienceDaily.Retrieved February 12, 2010.
  10. ^Li, S; Brazhnik, P; Sobral, B;Tyson, JJ(2009)."Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus".PLOS Comput Biol.5(8): e1000463.Bibcode:2009PLSCB...5E0463L.doi:10.1371/journal.pcbi.1000463.PMC2714070.PMID19680425.
  11. ^Lee, Vannajan Sanghiran; Chong, Wei Lim; Sukumaran, Sri Devi; Nimmanpipug, Pivarat; Letchumanan, Vengadesh; Goh, Bey Hing; Lee, Learn-Han; Md. Zain, Sharifuddin; Abd Rahman, Noorsaadah (2020)."Computational screening and identifying binding interaction of anti-viral and anti-malarial drugs: Toward the potential cure for SARS-CoV-2".Progress in Drug Discovery & Biomedical Science.3.doi:10.36877/pddbs.a0000065.
  12. ^Athanaileas, Theodoros; et al. (2011). "Exploiting grid technologies for the simulation of clinical trials: the paradigm of in silico radiation oncology".SIMULATION: Transactions of the Society for Modeling and Simulation International.87(10): 893–910.doi:10.1177/0037549710375437.S2CID206429690.
  13. ^Chua, Physilia Y. S.; Crampton-Platt, Alex; Lammers, Youri; Alsos, Inger G.; Boessenkool, Sanne; Bohmann, Kristine (2021)."Metagenomics: A viable tool for reconstructing herbivore diet".Molecular Ecology Resources.21(7): 2249–2263.doi:10.1111/1755-0998.13425.PMC8518049.PMID33971086.
  14. ^Liu, Y; Kuhlman, B (July 2006), "RosettaDesign server for protein design",Nucleic Acids Research,34(Web Server issue): W235–8,doi:10.1093/nar/gkl163,PMC1538902,PMID16845000
  15. ^Dantas, Gautam; Kuhlman, Brian; Callender, David; Wong, Michelle; Baker, David (2003), "A Large Scale Test of Computational Protein Design: Folding and Stability of Nine Completely Redesigned Globular Proteins",Journal of Molecular Biology,332(2): 449–60,CiteSeerX10.1.1.66.8110,doi:10.1016/S0022-2836(03)00888-X,PMID12948494.
  16. ^Dobson, N; Dantas, G; Baker, D; Varani, G (2006), "High-Resolution Structural Validation of the Computational Redesign of Human U1A Protein",Structure,14(5): 847–56,doi:10.1016/j.str.2006.02.011,PMID16698546.
  17. ^Dantas, G; Corrent, C; Reichow, S; Havranek, J; Eletr, Z; Isern, N; Kuhlman, B; Varani, G; et al. (2007), "High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design",Journal of Molecular Biology,366(4): 1209–21,doi:10.1016/j.jmb.2006.11.080,PMC3764424,PMID17196978.
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