Why is it important that the scientific method is used in human growth and development research?

1. Ayala FJ. Darwin and the scientific method. Proc Nat Acad Sci USA. 2009;106(Supp. 1):10033–9. [PMC free article] [PubMed] [Google Scholar]

2. Gauch HGJ. Scientific Method in Brief. Cambridge, UK: Cambridge University Press; 2012. [Google Scholar]

3. Gimbel S (Ed). Exploring the Scientific Method: Cases and Questions. Chicago, IL: The University of Chicago Press; 2011. [Google Scholar]

4. Gorini R. Al-Haytham, the man of experience. First steps in the science of vision. J Int Soc Hist Islam Med. 2003;2:53–5. [Google Scholar]

5. Lambert CG, Black LJ. Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics. 2012;13(2):195–203. 10.1093/biostatistics/kxr055 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

6. Tu S-M, Bilen MA, Tannir NM. The scientific method: pillar and pitfall of cancer research. Cancer Medicine. 2014;3(4):1035–7. 10.1002/cam4.248 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

7. Willis BH, Beebee H, Lasserson DS. Philosophy of science and the diagnostic process. Fam Pract. 2013;30(5):501–5. 10.1093/fampra/cmt031 [PubMed] [CrossRef] [Google Scholar]

8. McLelland CV. The nature of science and the scientific method Boulder, CO: The Geological Society of America; 2006. [Google Scholar]

9. Ladyman J. Understanding Philosophy of Science. Abington, Oxon: Routledge; 2002. [Google Scholar]

10. Allen JF. Hypothesis, induction and background knowledge. Data do not speak for themselves. Replies to Donald A. Gillies, Lawrence A. Kelly and Michael Scott. BioEssays. 2001;23(9):861–2. [Google Scholar]

11. Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5(1):7–11. 10.1187/cbe.05-12-0134 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

12. Vick BM, Pollak A, Welsh C, Liang JO. Learning the scientific method using GloFish. Zebrafish. 2012;9(4):226–41. 10.1089/zeb.2012.0758 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. Manuel SL, Johnson BW, Frevert CW, Duncan FE. Revisiting the scientific method to improve rigor and reproducibility of immunohistochemistry in reproductive science. Biol Reprod. 2018;99(4):673–7. 10.1093/biolre/ioy094 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

14. Noseda M, McLean GR. Where did the scientific method go? Nat Biotechnol. 2008;26(1):28–9. 10.1038/nbt0108-28 [PubMed] [CrossRef] [Google Scholar]

15. Begley CG, Ellis LM. Drug development: raise standard for preclinical cancer research. Nature Genetics. 2012;483:531–3. [PubMed] [Google Scholar]

16. Popper KR. Conjectures and Refutations: The Growth of Scientific Knowledge. Abingdon, Oxon: Routledge and Kegan Paul; 1963. [Google Scholar]

17. Popper KR. The Logic of Scientific Discovery Abingdon, Oxon: Routledge; 2002. [Google Scholar]

18. Wagensberg J. On the Existence and Uniqueness of the Scientific Method. Biol Theory. 2014;9(3):331–46. 10.1007/s13752-014-0166-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

19. Gillies DA. Popper and computer induction. BioEssays. 2001;23(9):859–860. 10.1002/bies.1123 [PubMed] [CrossRef] [Google Scholar]

20. Kelley LA, Scott M. On John Allen's critique of induction. Bioessays. 2001;23(9):860–861. 10.1002/bies.1124 [PubMed] [CrossRef] [Google Scholar]

21. Harding SE. Can theories be refuted?: Essays on the Duhem-Quine thesis Dordrecht-Holland / Boston, MA: D. Reidel Publ. Co; 1976. [Google Scholar]

22. Kuhn TS. The Structure of Scientific Revolutions Chicago, IL: University of Chicago Press; 1962. [Google Scholar]

23. Spalding A. Colour, humour and scientific method. Clin Exp Optom. 2010;93(3):129–30. 10.1111/j.1444-0938.2010.00460.x [PubMed] [CrossRef] [Google Scholar]

24. Michie S, Fixsen D, Grimshaw JM, Eccles MP. Specifying and reporting complex behaviour change interventions: the need for a scientific method. Implement Sci. 2009;4:40 10.1186/1748-5908-4-40 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

25. Chamberlin TC. The Method of Multiple Working Hypotheses: With this method the dangers of parental affection for a favorite theory can be circumvented. Science. 1965;148(3671):754–9. 10.1126/science.148.3671.754 [PubMed] [CrossRef] [Google Scholar]

26. Platt JR. Strong inference. Science, New Series. 1964;146(3642):347–53. [PubMed] [Google Scholar]

27. Beard DA, Kushmerick MJ. Strong inference for systems biology. PLoS Comput Biol. 2009;5(8):e1000459 10.1371/journal.pcbi.1000459 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

28. Wilkinson MD, McCarthy L, Vandervalk B, Withers D, Kawas E, Samadian S. SADI, SHARE, and the in silico scientific method. BMC Bioinformatics. 2010;11 Suppl 12:S7. [PMC free article] [PubMed] [Google Scholar]

29. Editorial. Just the facts. Communications Biology. 2018;1:24 10.1038/s42003-018-0030-x [PMC free article] [PubMed] [CrossRef] [Google Scholar]

30. Okagaki LH, Dean RA. The influence of funding sources on the scientific method. Mol Plant Pathol. 2016;17(5):651–3. 10.1111/mpp.12380 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Egilman DS. Scientific method questioned. Int J Occup Envir health. 2006;12(3):290–3. [Google Scholar]

32. Brown PO, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet. 1999;21(1 Suppl):33–7. [PubMed] [Google Scholar]

33. Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND. Molecular signatures from omics data: from chaos to consensus. Biotechnol J. 2012;7(8):946–57. 10.1002/biot.201100305 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

34. Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays. 2004;26(1):99–105. 10.1002/bies.10385 [PubMed] [CrossRef] [Google Scholar]

35. Allen JF. In silico veritas. Data-mining and automated discovery: the truth is in there. EMBO Rep. 2001;2(7):542–4. 10.1093/embo-reports/kve139 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

36. Allen JF. Bioinformatics and discovery: induction beckons again. BioEssays. 2001;23(1):104–7. 10.1002/1521-1878(200101)23:1<104::AID-BIES1013>3.0.CO;2-2 [PubMed] [CrossRef] [Google Scholar]

37. Hume D. An enquiry concerning human understanding. Oxford, U.K: Oxford University Press; 1748/1999. [Google Scholar]

38. Popper KR. Objective knowledge. An evolutionary approach. Oxford, U.K: Oxford University Press; 1972. [Google Scholar]

39. Lander ES. Array of hope. Nature Genetics. 1999;21:3–4. 10.1038/4427 [PubMed] [CrossRef] [Google Scholar]

40. Sternberg MJ, King RD, Lewis RA, Muggleton S. Application of machine learning to structural molecular biology. Philos Trans R Soc Lond B Biol Sci. 1994;344(1310):365–71. 10.1098/rstb.1994.0075 [PubMed] [CrossRef] [Google Scholar]

41. Anderson C. The end of theory: The data deluge makes the scientific method obsolete. Wired, Science. 2008; [cited 2019 1 July] Available from https://www.wired.com/2008/06/pb-theory/?mbid=email_onsiteshare. 10.1126/science.1159276 [CrossRef] [Google Scholar]

42. Succi S, Coveney PV. Big data: the end of the scientific method? Philos Trans A Math Phys Eng Sci. 2019;377(2142):20180145 10.1098/rsta.2018.0145 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

43. Janes KA, Chandran PL, Ford RM, Lazzara MJ, Papin JA, Peirce SM, et al. An engineering design approach to systems biology. Integr Biol (Camb). 2017;9(7):574–83. 10.1039/c7ib00014f [PMC free article] [PubMed] [CrossRef] [Google Scholar]

44. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001;29(4):365–71. 10.1038/ng1201-365 [PubMed] [CrossRef] [Google Scholar]

45. Lotka A. Elements of Physical Biology. Baltimore: Williams and Wilkins; reprinted as 'Elements of Mathematical Biology'. Dover, New York, 1956; 1924. [Google Scholar]

46. von Bertalanffy L. Der Organismus als physikalisches System betrachtet. Die Naturwissenschaften. 1940;28(33):521–31. [Google Scholar]

47. von Bertalanffy L. General System Theory: Foundations, Development, Applications New York: George Braziller; 1968. [Google Scholar]

48. May RM. Stability and Complexity in Model Ecosystems: Princeton: University Press; 1973. [Google Scholar]

49. Searls DB. The linguistics of DNA. American Scientist. 1992;80(6):579–91. [Google Scholar]

50. Qi Z, Voit EO. Inference of cancer mechanisms through computational systems analysis Mol BioSystems. 2017;13(3):489–97. [PMC free article] [PubMed] [Google Scholar]

51. Tang Y, Gupta A, Garimalla S, The MaHPIC Consortium, Galininski MR, Styczynski MP, et al. Interpretation of transcriptomic changes during a complex disease through metabolic modeling. Biochimica et Biophysica Acta–Molecular Basis of Disease 2017;1864(6 Pt. B):2329–40. [PMC free article] [PubMed] [Google Scholar]

52. Fonseca LL, Joyner C, Consortium TM, Galininski MR, Voit EO. A model of Plasmodium vivax concealment based on Plasmodium cynomolgi infections in Macaca mulatta. Malaria J 2017;16:375. [PMC free article] [PubMed] [Google Scholar]

53. Alves R, Sorribas A. Special issue on biological design principles. Mathematical biosciences. 2011;231(1):1–2. 10.1016/j.mbs.2011.03.009 [PubMed] [CrossRef] [Google Scholar]

54. Savageau MA. A theory of alternative designs for biochemical control systems. Biomedica biochimica acta. 1985;44(6):875–80. [PubMed] [Google Scholar]

55. Voit EO. Design principles and operating principles: the yin and yang of optimal functioning. Mathematical biosciences. 2003;182(1):81–92. [PubMed] [Google Scholar]

56. Alves R, Savageau MA. Effect of overall feedback inhibition in unbranched biosynthetic pathways. Biophysical journal. 2000;79(5):2290–304. 10.1016/S0006-3495(00)76475-7 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

57. Savageau MA. Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology Reading, Mass: Addison-Wesley Pub. Co. Advanced Book Program; (reprinted 2009); 1976. [Google Scholar]

58. Dolatshahi S, Fonseca LL, Voit EO. New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model. Mol Biosyst. 2016;12(1):23–36. 10.1039/c5mb00331h [PubMed] [CrossRef] [Google Scholar]

59. Dolatshahi S, Fonseca LL, Voit EO. New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. II. Inference of the precisely timed control system regulating glycolysis. Mol Biosyst. 2016;12(1):37–47. 10.1039/c5mb00726g [PubMed] [CrossRef] [Google Scholar]

60. Reither F. Über das Denken mit Analogien und Modellen In: Schaefer G, Trommer G, editors. Denken in Modellen. Braunschweig, Germany: Georg Westermann Verlag; 1977. [Google Scholar]

61. Voit EO. A First Course in Systems Biology, 2nd Ed New York, NY: Garland Science; 2018. [Google Scholar]

62. Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol. 2007;3(10):1871–8. 10.1371/journal.pcbi.0030189 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

63. Jia G, Stephanopoulos GN, Gunawan R. Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method. Bioinformatics. 2011;27(14):1964–70. 10.1093/bioinformatics/btr293 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

64. Bridgman PW. Reflections of a Physicist New York, NY: reprinted by Kessinger Legacy Reprints, 2010; 1955. [Google Scholar]

65. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic netowrk. Science. 2001;292(5518):929–34. 10.1126/science.292.5518.929 [PubMed] [CrossRef] [Google Scholar]

66. Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO. Integrating high-throughput and computational data elucidates bacterial networks. Nature Genetics. 2004;429(6987):92–6. [PubMed] [Google Scholar]

67. Bonneau R. Learning biological networks: from modules to dynamics. Nature Chemical Biology. 2008;4(11):658–64. 10.1038/nchembio.122 [PubMed] [CrossRef] [Google Scholar]

68. Chandrasekaran S. A Protocol for the construction and curation of genome-scale integrated metabolic and regulatory network models. Methods Mol Biol. 2019;1927:203–14. 10.1007/978-1-4939-9142-6_14 [PubMed] [CrossRef] [Google Scholar]

69. Thiele I, Palsson BO. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93–121. 10.1038/nprot.2009.203 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

70. Chandrasekaran S, Price ND. Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput Biol. 2013;9(12):e1003370 10.1371/journal.pcbi.1003370 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

71. Imam S, Schauble S, Brooks AN, Baliga NS, Price ND. Data-driven integration of genome-scale regulatory and metabolic network models. Front Microbiol. 2015;6:409 10.3389/fmicb.2015.00409 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

72. Lee S, Zhang C, Kilicarslan M, Piening BD, Bjornson E, Hallstrom BM, et al. Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metab. 2016;24(1):172–84. 10.1016/j.cmet.2016.05.026 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

73. Dunphy LJ, Yen P, Papin JA. Integrated Experimental and Computational analyses reveal differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa. Cell Syst. 2019;8(1):3–14 e3. 10.1016/j.cels.2018.12.002 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

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Why is it important that the scientific method is used in human growth and development research?

Traditional scientific method: Hypothesis-based deduction.

The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

  • Why is it important that the scientific method is used in human growth and development research?
  • Why is it important that the scientific method is used in human growth and development research?
  • Why is it important that the scientific method is used in human growth and development research?
  • Why is it important that the scientific method is used in human growth and development research?

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