In silico

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A forest of synthetic pyramidal dendrites generated in silico using Cajal's laws of neuronal branching Forest of synthetic pyramidal dendrites grown using Cajal's laws of neuronal branching.png
A forest of synthetic pyramidal dendrites generated in silico using Cajal's laws of neuronal branching

In biology and other experimental sciences, an in silico experiment is one performed on a computer or via computer simulation software. The phrase is pseudo-Latin for 'in silicon' (correct Latin : in silicio), referring to silicon in computer chips. It was coined in 1987 as an allusion to the Latin phrases in vivo , in vitro , and in situ , which are commonly used in biology (especially systems biology). The latter phrases refer, respectively, to experiments done in living organisms, outside living organisms, and where they are found in nature.

Contents

History

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. [1] [2] 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 dissertation. [3]

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. [4] 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. [5]

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. [6] [7] 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).

As an example, the technique was utilized for a drug repurposing study in order to search for potential cures for COVID-19 (SARS-CoV-2). [8]

Cell models

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. [9] More work can be found that focus on modeling a particular cellular process such as the growth cycle of Caulobacter crescentus . [10]

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.

Genetics

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.

Other examples

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

See also

Related Research Articles

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<span class="mw-page-title-main">Structural biology</span> Study of molecular structures in biology

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<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

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<span class="mw-page-title-main">Mathematical and theoretical biology</span> Branch of biology

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<span class="mw-page-title-main">Docking (molecular)</span> Prediction method in molecular modeling

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<span class="mw-page-title-main">Virtual screening</span>

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High-content screening (HCS), also known as high-content analysis (HCA) or cellomics, is a method that is used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell in a desired manner. Hence high content screening is a type of phenotypic screen conducted in cells involving the analysis of whole cells or components of cells with simultaneous readout of several parameters. HCS is related to high-throughput screening (HTS), in which thousands of compounds are tested in parallel for their activity in one or more biological assays, but involves assays of more complex cellular phenotypes as outputs. Phenotypic changes may include increases or decreases in the production of cellular products such as proteins and/or changes in the morphology of the cell. Hence HCA typically involves automated microscopy and image analysis. Unlike high-content analysis, high-content screening implies a level of throughput which is why the term "screening" differentiates HCS from HCA, which may be high in content but low in throughput.

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<span class="mw-page-title-main">Cellular model</span>

A cellular model is a mathematical model of aspects of a biological cell, for the purposes of in silico research.

In silico medicine is the application of in silico research to problems involving health and medicine. It is the direct use of computer simulation in the diagnosis, treatment, or prevention of a disease. More specifically, in silico medicine is characterized by modeling, simulation, and visualization of biological and medical processes in computers with the goal of simulating real biological processes in a virtual environment.

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<span class="mw-page-title-main">Genome informatics</span>

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References

  1. ↑ "Google Groups". groups.google.com. Retrieved 2020-01-05.
  2. ↑ Hameroff, S. R. (2014-04-11). Ultimate Computing: Biomolecular Consciousness and NanoTechnology. Elsevier. ISBN   978-0-444-60009-7.
  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, CiteSeerX   10.1.1.637.3244 , doi:10.1016/0923-2508(91)90073-J, PMID   1784830
  5. ↑ Sieburg, H.B. (1990), "Physiological Studies in 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, PMID   20055453
  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 . PMC   2714070 . PMID   19680425.
  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. S2CID   206429690.
  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 . PMC   8518049 . PMID   33971086.
  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, PMC   1538902 , PMID   16845000
  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, CiteSeerX   10.1.1.66.8110 , doi:10.1016/S0022-2836(03)00888-X, PMID   12948494.
  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 , PMID   16698546.
  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, PMC   3764424 , PMID   17196978.
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