In silico

From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses, see In silico (disambiguation).
A forest of synthetic pyramidal dendrites grown in silico using Cajal's laws of neuronal branching

In silico is an expression used to mean "performed on computer or via computer simulation." The phrase was coined in 1989 as an analogy to the Latin phrases in vivo, in vitro, and in situ, which are commonly used in biology (see also systems biology) and refer to experiments done in living organisms, outside of living organisms, and where they are found in nature, respectively.

Drug discovery with virtual screening[edit]

Main article: virtual screening

In silico research 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 (see Protein-ligand docking), researchers found potential inhibitors to an enzyme associated with cancer activity in silico. Fifty percent of the molecules were later shown to be active inhibitors in vitro.[1][2] This approach differs from use of expensive high-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 (see drug discovery).

Cell models[edit]

Efforts have been made to establish computer models of cellular behavior. For example, in 2007 researchers developed an in silico model of tuberculosis to aid in drug discovery, with the prime benefit of being faster than real time simulated growth rates, allowing phenomena of interest to be observed in minutes rather than months.[3] More work can be found that focus on modeling a particular cellular process such as the growth cycle of Caulobacter crescentus.[4]

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

Genetics[edit]

Digital genetic sequences obtained from DNA sequencing may be stored in sequence databases, be analyzed (see Sequence analysis), be digitally altered and/or be used as templates for creating new actual DNA using artificial gene synthesis.

Other examples[edit]

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

  • Whole cell analysis of prokaryotic and eukaryotic hosts e.g. E. coli, B. subtilis, yeast, CHO- or human cell lines
  • Bioprocess development and optimization e.g. optimization of product yields
  • Analysis, interpretation and visualization of heterologous data sets from various sources e.g. genome, transcriptome or proteome data
  • Protein design. One example is RosettaDesign a software package, under active development and free for academic use, that has seen extensive successful use.[5][6][7][8] RosettaDesign is accessible via a web server.[9]

History[edit]

The expression in silico was first used in public in 1989 in the workshop "Cellular Automata: Theory and Applications" in Los Alamos, New Mexico. Pedro Miramontes, a mathematician from National Autonomous University of Mexico (UNAM) presented the report "DNA and RNA Physicochemical Constraints, Cellular Automata and Molecular Evolution". In his talk, Miramontes used the term "in silico" to characterize biological experiments carried out entirely in a computer. The work was later presented by Miramontes as his PhD dissertation.[10]

In silico has been used in white papers written to support the creation of bacterial genome programs by the Commission of the European Community. The first referenced paper where "in silico" appears was written by a French team in 1991.[11] The first referenced book chapter where "in silico" appears was written by Hans B. Sieburg in 1990 and presented during a Summer School on Complex Systems at the Santa Fe Institute.[12]

The phrase "in silico" originally 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.

In silico versus in silicio[edit]

"In silico" was briefly challenged by "in silicio," which is correct Latin for "in silicon". The Latin term for silicon, silicium, was created at the beginning of the 19th century by Berzelius. Silex, meaning flint, is a third declension Latin word in the nominative case, thus with the root silic- for the other cases, from which words like silica are derived in English. The phrase "in silice" means "in flint". However, the adjective in Latin meaning flint-like is silicius,a,um. Many names of elements with the ending -ium come from this adjectival form, e.g. calx (limestone), calcis (of limestone), calcium (limestone-like). In the end, "in silico" appears as an end rhyme on the words "in vivo" and "in vitro" making the word catchier based on similarity and not sounding odd given the general disappearance of classical languages in the present curricula.[citation needed] "In silico" is now almost universal; it even occurs in a journal title (In Silico Biology: http://www.bioinfo.de/isb/).

Although the preposition in is Latin and en Greek, in silico is reasonable from the viewpoint of (ancient) Greek case endings; the "-on" ending for certain elements is from Greek. In Greek, "silicon" would take the form "silico" in such a phrase. Latin typically uses the correct Greek forms for Greek words when they are used with Latin prepositions.[citation needed]

Another possible reason for that preference is that English speakers find it easier to pronounce "in silico" than "in silicio".[citation needed]

See also[edit]

References[edit]

  1. ^ 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, PMID 20055453 
  2. ^ Ludwig Institute for Cancer Research (2010, February 4). New computational tool for cancer treatment. ScienceDaily. Retrieved February 12, 2010, from http://www.sciencedaily.com/releases/2010/01/100129151756.htm
  3. ^ University Of Surrey (2007, June 25). In Silico Cell For TB Drug Discovery. ScienceDaily. Retrieved February 12, 2010, from http://www.sciencedaily.com/releases/2007/06/070624135714.htm
  4. ^ 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. doi:10.1371/journal.pcbi.1000463
  5. ^ 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, PMC 1538902, PMID 16845000 
  6. ^ 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, doi:10.1016/S0022-2836(03)00888-X, PMID 12948494. 
  7. ^ 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, doi:10.1016/j.str.2006.02.011, PMID 16698546. 
  8. ^ 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, PMC 3764424, PMID 17196978. 
  9. ^ http://rosettadesign.med.unc.edu/
  10. ^ Miramontes P. Un modelo de autómata celular para la evolución de los ácidos nucleicos [A cellular automaton model for the evolution of nucleic acids]. Tesis de doctorado en matemáticas. UNAM. 1992.
  11. ^ 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, doi:10.1016/0923-2508(91)90073-J, PMID 1784830 
  12. ^ Sieburg, H.B. (1990), "Physiological Studies in silico", Studies in the Sciences of Complexity 12: 321–342 

External links[edit]