In silico (Pseudo-Latin for "in silicon", alluding to the mass use of silicon for computer chips) is an expression meaning "performed on computer or via computer simulation" in reference to biological experiments. The phrase was coined in 1987 as an allusion 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 living organisms, and where they are found in nature, respectively.
The earliest known use of the phrase was by Christopher Langton to describe artificial life, in the announcement of a workshop on that subject at the Center for Nonlinear Studies at the Los Alamos National Laboratory in 1987. The expression in silico was 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 from National Autonomous University of Mexico (UNAM), presenting the report "DNA and RNA Physicochemical Constraints, Cellular Automata and Molecular Evolution". The work was later presented by Miramontes as his PhD dissertation.
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. 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.
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.
Drug discovery with virtual screening
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 (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. 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).
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 its being faster than real time simulated growth rates, allowing phenomena of interest to be observed in minutes rather than months. More work can be found that focus on modeling a particular cellular process such as the growth cycle of Caulobacter crescentus.
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 cell models, which are very important for in silico cancer research .
Digital genetic sequences obtained from DNA sequencing may be stored in sequence databases, be analyzed (see Sequence analysis), be digitally altered or be used as templates for creating new actual DNA using artificial gene synthesis.
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
- Simulation of oncological clinical trials exploiting grid computing infrastructures, such as the European Grid Infrastructure, for improving the performance and effectiveness of the simulations.
- 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 development and free for academic use.
- Virtual screening
- Computational biology
- Computational biomodeling
- Computer experiment
- Cellular model
- Nonclinical studies
- In silico molecular design programs
- In silico medicine
- Dry lab
- "Google Groups". groups.google.com. Retrieved 2020-01-05.
- Hameroff, S. R. (2014-04-11). Ultimate Computing: Biomolecular Consciousness and NanoTechnology. Elsevier. ISBN 978-0-444-60009-7.
- 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.
- 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, CiteSeerX 10.1.1.637.3244, doi:10.1016/0923-2508(91)90073-J, PMID 1784830
- Sieburg, H.B. (1990), "Physiological Studies in silico", Studies in the Sciences of Complexity, 12: 321–342
- 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
- Ludwig Institute for Cancer Research (2010, February 4). New computational tool for cancer treatment. ScienceDaily. Retrieved February 12, 2010.
- University Of Surrey. June 25, 2007. In Silico Cell For TB Drug Discovery. ScienceDaily. Retrieved February 12, 2010.
- 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. PMC 2714070. PMID 19680425.
- JeanQuartier, Claire; Jeanquartier, Fleur; Jurisica, Igor; Holzinger, Andreas (2018). "In silico cancer research towards 3R". Springer/Nature BMC Cancer. 18 (1): e408. doi:10.1186/s12885-018-4302-0. PMC 5897933. PMID 29649981.
- 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.
- 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
- 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, CiteSeerX 10.1.1.66.8110, doi:10.1016/S0022-2836(03)00888-X, PMID 12948494.
- 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, PMID 16698546.
- 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.
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- World Wide Words: In silico
- CADASTER Seventh Framework Programme project aimed to develop in silico computational methods to minimize experimental tests for REACH Registration, Evaluation, Authorisation and Restriction of Chemicals
- In Silico Biology. Journal of Biological Systems Modeling and Simulation
- In Silico Pharmacology