A forest of syntheticpyramidaldendrites generatedin silico usingCajal's laws of neuronal branching
Inbiology and other experimental sciences, anin silico experiment is one performed on a computer or viacomputer simulation software. The phrase ispseudo-Latin for 'in silicon' (correctLatin:in silicio), referring tosilicon in 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.
The earliest known use of the phrase was byChristopher Langton to describeartificial life, in the announcement of a workshop on that subject at the Center for Nonlinear Studies at theLos Alamos National Laboratory in 1987.[1][2] The expressionin silico was first used to characterize biological experiments carried out entirely in a computer in 1989, in the workshop "Cellular Automata: Theory and Applications" inLos Alamos, New Mexico, by Pedro Miramontes, amathematician fromNational Autonomous University of Mexico (UNAM), presenting the report "DNA andRNA Physicochemical Constraints, Cellular Automata and Molecular Evolution". The work was later presented by Miramontes as hisdissertation.[3]
In silico has been used inwhite papers written to support the creation of bacterial genome programs by the Commission of the European Community. The first referenced paper wherein silico appears was written by a French team in 1991.[4] The first referenced book chapter wherein silico appears was written by Hans B. Sieburg in 1990 and presented during a Summer School on Complex Systems at theSanta Fe Institute.[5]
The phrasein 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 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 repurposing study in order to search for potential cures forCOVID-19 (SARS-CoV-2).[8]
Efforts have been made to establish computer models of cellular behavior. For example, in 2007 researchers developed an in silico model oftuberculosis 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.[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 dynamics andcell 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.
Bioprocess development and optimization e.g. optimization of product yields
Simulation of oncological clinical trials exploitinggrid computing infrastructures, such as theEuropean Grid Infrastructure, for improving the performance and effectiveness of the simulations.[12]
^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.
^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.
^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.
^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.
^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.
