1932

Abstract

Multicellular model organisms, such as (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-050423-054647
2024-07-03
2024-10-08
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/26/1/annurev-bioeng-050423-054647.html?itemId=/content/journals/10.1146/annurev-bioeng-050423-054647&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Wilson EB. 1896.. The Cell in Development and Inheritance. New York:: Macmillan
    [Google Scholar]
  2. 2.
    Kitano H. 2007.. Towards a theory of biological robustness. . Mol. Syst. Biol. 3::137
    [Crossref] [Google Scholar]
  3. 3.
    Bruggeman FJ, Westerhoff HV. 2007.. The nature of systems biology. . Trends Microbiol. 15::4550
    [Crossref] [Google Scholar]
  4. 4.
    Jayamohan H, Romanov V, Li H, Son J, Samuel R, et al. 2017.. Advances in microfluidics and lab-on-a-chip technologies. . In Molecular Diagnostics, ed. GP Patrinos , pp. 197217. Amsterdam:: Elsevier. , 3rd ed..
    [Google Scholar]
  5. 5.
    Scheler O, Postek W, Garstecki P. 2019.. Recent developments of microfluidics as a tool for biotechnology and microbiology. . Curr. Opin. Biotechnol. 55::6067
    [Crossref] [Google Scholar]
  6. 6.
    Ma J, Wang Y, Liu J. 2017.. Biomaterials meet microfluidics: from synthesis technologies to biological applications. . Micromachines 8::255
    [Crossref] [Google Scholar]
  7. 7.
    Crane MM, Chung K, Stirman J, Lu H. 2010.. Microfluidics-enabled phenotyping, imaging, and screening of multicellular organisms. . Lab Chip 10::150917
    [Crossref] [Google Scholar]
  8. 8.
    Sivagnanam V, Gijs MAM. 2013.. Exploring living multicellular organisms, organs, and tissues using microfluidic systems. . Chem. Rev. 113::321447
    [Crossref] [Google Scholar]
  9. 9.
    Shum A. 2009.. Hybrid semiconductor/biological systems. PhD Thesis , Univ. Wash., Seattle, WA:
    [Google Scholar]
  10. 10.
    Ijspeert AJ. 2014.. Biorobotics: using robots to emulate and investigate agile locomotion. . Science 346::196203
    [Crossref] [Google Scholar]
  11. 11.
    Webb B. 2001.. Can robots make good models of biological behaviour?. Behav. Brain Sci. 24::103350
    [Crossref] [Google Scholar]
  12. 12.
    Jafferis NT, Helbling EF, Karpelson M, Wood RJ. 2019.. Untethered flight of an insect-sized flapping-wing microscale aerial vehicle. . Nature 570:(7762):49195
    [Crossref] [Google Scholar]
  13. 13.
    Morgan TH. 1910.. Sex limited inheritance in Drosophila. . Science 32::12022
    [Crossref] [Google Scholar]
  14. 14.
    Jennings BH. 2011.. Drosophila—a versatile model in biology & medicine. . Mater. Today 14::19095
    [Crossref] [Google Scholar]
  15. 15.
    Konno M, Asai A, Kitagawa T, Yabumoto M, Ofusa K, et al. 2020.. State-of-the-art technology of model organisms for current human medicine. . Diagnostics 10::392
    [Crossref] [Google Scholar]
  16. 16.
    Wu Q, Kumar N, Velagala V, Zartman JJ. 2019.. Tools to reverse-engineer multicellular systems: case studies using the fruit fly. . J. Biol. Eng. 13::33
    [Crossref] [Google Scholar]
  17. 17.
    Horowitz LF, Rodriguez AD, Ray T, Folch A. 2020.. Microfluidics for interrogating live intact tissues. . Microsyst. Nanoeng. 6::69
    [Crossref] [Google Scholar]
  18. 18.
    Aldaz S, Escudero LM, Freeman M. 2010.. Live imaging of Drosophila imaginal disc development. . PNAS 107::1421722
    [Crossref] [Google Scholar]
  19. 19.
    Witzberger MM, Fitzpatrick JAJ, Crowley JC, Minden JS. 2008.. End-on imaging: a new perspective on dorsoventral development in Drosophila embryos. . Dev. Dyn. 237::325259
    [Crossref] [Google Scholar]
  20. 20.
    Schmied C, Tomancak P. 2016.. Sample preparation and mounting of Drosophila embryos for multiview light sheet microscopy. . In Drosophila: Methods and Protocols, ed. C Dahmann , pp. 189202. New York:: Springer. , 2nd ed..
    [Google Scholar]
  21. 21.
    Nienhaus U, Aegerter-Wilmsen T, Aegerter CM. 2012.. In-vivo imaging of the Drosophila wing imaginal disc over time: novel insights on growth and boundary formation. . PLOS ONE 7::e47594
    [Crossref] [Google Scholar]
  22. 22.
    Heemskerk I, Lecuit T, LeGoff L. 2014.. Dynamic clonal analysis based on chronic in vivo imaging allows multiscale quantification of growth in the Drosophila wing disc. . Development 141::233948
    [Crossref] [Google Scholar]
  23. 23.
    Sandstrom DJ. 2008.. Isoflurane reduces excitability of Drosophila larval motoneurons by activating a hyperpolarizing leak conductance. . Anesthesiology 108::43446
    [Crossref] [Google Scholar]
  24. 24.
    Sandstrom DJ. 2004.. Isoflurane depresses glutamate release by reducing neuronal excitability at the Drosophila neuromuscular junction. . J. Physiol. 558::489502
    [Crossref] [Google Scholar]
  25. 25.
    Kakanj P, Eming SA, Partridge L, Leptin M. 2020.. Long-term in vivo imaging of Drosophila larvae. . Nat. Protoc. 15::115887
    [Crossref] [Google Scholar]
  26. 26.
    Ardeshiri R, Hosseini L, Amini N, Rezai P. 2016.. Cardiac screening of intact Drosophila melanogaster larvae under exposure to aqueous and gaseous toxins in a microfluidic device. . RSC Adv. 6::6571424
    [Crossref] [Google Scholar]
  27. 27.
    Zeng F, Rohde CB, Yanik MF. 2008.. Sub-cellular precision on-chip small-animal immobilization, multi-photon imaging and femtosecond-laser manipulation. . Lab Chip 8::65356
    [Crossref] [Google Scholar]
  28. 28.
    Hulme SE, Shevkoplyas SS, Apfeld J, Fontana W, Whitesides GM. 2007.. A microfabricated array of clamps for immobilizing and imaging C. elegans. . Lab Chip 7::151523
    [Crossref] [Google Scholar]
  29. 29.
    Guo SX, Bourgeois F, Chokshi T, Durr NJ, Hilliard MA, et al. 2008.. Femtosecond laser nanoaxotomy lab-on-a-chip for in vivo nerve regeneration studies. . Nat. Methods 5::53133
    [Crossref] [Google Scholar]
  30. 30.
    Mondal S, Ahlawat S, Koushika SP. 2012.. Simple microfluidic devices for in vivo imaging of C. elegans, Drosophila and zebrafish. . J. Vis. Exp. 67::e3780
    [Google Scholar]
  31. 31.
    Ghannad-Rezaie M, Wang X, Mishra B, Collins C, Chronis N. 2012.. Microfluidic chips for in vivo imaging of cellular responses to neural injury in Drosophila larvae. . PLOS ONE 7::e29869
    [Crossref] [Google Scholar]
  32. 32.
    Mishra B, Ghannad-Rezaie M, Li J, Wang X, Hao Y, et al. 2014.. Using microfluidics chips for live imaging and study of injury responses in Drosophila larvae. . J. Vis. Exp. 84::e50998
    [Google Scholar]
  33. 33.
    Chaudhury AR, Insolera R, Hwang R-D, Fridell Y-W, Collins C, Chronis N. 2017.. On chip cryo-anesthesia of Drosophila larvae for high resolution in vivo imaging applications. . Lab Chip 17::230322
    [Crossref] [Google Scholar]
  34. 34.
    Ghaemi R, Acker M, Stosic A, Jacobs R, Selvaganapathy PR. 2023.. Bending Drosophila larva using a microfluidic device enables imaging of its brain and nervous system at single neuronal resolution. . Lab Chip 23::295305
    [Crossref] [Google Scholar]
  35. 35.
    Reddy AN, Maheshwari N, Sahu DK, Ananthasuresh GK. 2010.. Miniature compliant grippers with vision-based force sensing. . IEEE Trans. Robot. 26::86777
    [Crossref] [Google Scholar]
  36. 36.
    Gizzi AMC, Cattoni DI, Fiche J-B, Espinola SM, Gurgo J, et al. 2019.. Microscopy-based chromosome conformation capture enables simultaneous visualization of genome organization and transcription in intact organisms. . Mol. Cell 74::21222
    [Crossref] [Google Scholar]
  37. 37.
    Gizzi AMC, Espinola SM, Gurgo J, Houbron C, Fiche J-B, et al. 2020.. Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M. . Nat. Protoc. 15::84076
    [Crossref] [Google Scholar]
  38. 38.
    Zhu H, Shen W, Luo C, Liu F. 2022.. An integrated microfluidic device for multiplexed imaging of spatial gene expression patterns of Drosophila embryos. . Lab Chip 22::408192
    [Crossref] [Google Scholar]
  39. 39.
    Nasir M, Dickinson M, Liepmann D. 2006.. Measurement of insect flight forces using a MEMS based physical sensor. . In Solid-State Sensors, Actuators, and Microsystems Workshop, pp. 3003. San Diego, CA:: Transducer Res. Found.
    [Google Scholar]
  40. 40.
    Sugiura H, Dickinson MH. 2009.. The generation of forces and moments during visual-evoked steering maneuvers in flying Drosophila. . PLOS ONE 4::e4883
    [Crossref] [Google Scholar]
  41. 41.
    Shum AJ, Parviz BA. 2007.. Vacuum microfabrication on live fruit fly. . In Proc. 2007 IEEE 20th International Conference on Micro Electro Mechanical Systems (MEMS), pp. 17982. New York:: IEEE
    [Google Scholar]
  42. 42.
    Bernstein RW, Zhang X, Zappe S, Fish M, Scott M, Solgaard O. 2002.. Positioning and immobilization of Drosophila embryos in 2-D arrays for drug injection. . In Proc. Micro Total Analysis Systems 2002, ed. Y Baba, S Shoji, A Berg , pp. 79395. Dordrecht, Neth:.: Springer
    [Google Scholar]
  43. 43.
    Srinivasan U, Liepmann D, Howe RT. 2001.. Microstructure to substrate self-assembly using capillary forces. . J. Microelectromech. Syst. 10::1724
    [Crossref] [Google Scholar]
  44. 44.
    Zhang X, Chen C-C, Bernstein RW, Zappe S, Scott MP, Solgaard O. 2005.. Microoptical characterization and modeling of positioning forces on Drosophila embryos self-assembled in two-dimensional arrays. . J. Microelectromech. Syst. 14::118797
    [Crossref] [Google Scholar]
  45. 45.
    Bernstein RW, Scott M, Solgaard O. 2004a.. BioMEMS for high-throughput handling and microinjection of embryos. . In Proc. SPIE, Vol. 5641, MEMS/MOEMS Technologies and Applications II, pp. 6773. Bellingham, WA:: International Society for Optics and Photonics
    [Google Scholar]
  46. 46.
    Bernstein RW, Zhang X, Zappe S, Fish M, Scott M, Solgaard O. 2004b.. Characterization of fluidic microassembly for immobilization and positioning of Drosophila embryos in 2-D arrays. . Sens. Actuators A Phys. 114::19196
    [Crossref] [Google Scholar]
  47. 47.
    Levario TJ, Zhan M, Lim B, Shvartsman SY, Lu H. 2013.. Microfluidic trap array for massively parallel imaging of Drosophila embryos. . Nat. Protoc. 8::721
    [Crossref] [Google Scholar]
  48. 48.
    Kanodia JS, Rikhy R, Kim Y, Lund VK, DeLotto R, et al. 2009.. Dynamics of the Dorsal morphogen gradient. . PNAS 106::2170712
    [Crossref] [Google Scholar]
  49. 49.
    Levario TJ, Lim B, Shvartsman SY, Lu H. 2016.. Microfluidics for high-throughput quantitative studies of early development. . Annu. Rev. Biomed. Eng. 18::285309
    [Crossref] [Google Scholar]
  50. 50.
    Goyal Y, Levario TJ, Mattingly HH, Holmes S, Shvartsman SY, Lu H. 2017.. Parallel imaging of Drosophila embryos for quantitative analysis of genetic perturbations of the Ras pathway. . Dis. Models Mech. 10::92329
    [Google Scholar]
  51. 51.
    Kim Y, Andreu MJ, Lim B, Chung K, Terayama M, et al. 2011.. Gene regulation by MAPK substrate competition. . Dev. Cell 20::88087
    [Crossref] [Google Scholar]
  52. 52.
    Kanodia JS, Liang H-L, Kim Y, Lim B, Zhan M, et al. 2012.. Pattern formation by graded and uniform signals in the early Drosophila embryo. . Biophys. J. 102::42733
    [Crossref] [Google Scholar]
  53. 53.
    Lim B, Samper N, Lu H, Rushlow C, Jiménez G, Shvartsman SY. 2013.. Kinetics of gene derepression by ERK signaling. . PNAS 110::1033035
    [Crossref] [Google Scholar]
  54. 54.
    Dsilva CJ, Lim B, Lu H, Singer A, Kevrekidis IG, Shvartsman SY. 2015.. Temporal ordering and registration of images in studies of developmental dynamics. . Development 142::171724
    [Google Scholar]
  55. 55.
    Lim B, Dsilva CJ, Levario TJ, Lu H, Schüpbach T, et al. 2015.. Dynamics of inductive ERK signaling in the Drosophila embryo. . Curr. Biol. 25::178490
    [Crossref] [Google Scholar]
  56. 56.
    Shorr AZ, Sönmez UM, Minden JS, LeDuc PR. 2019.. High-throughput mechanotransduction in Drosophila embryos with mesofluidics. . Lab Chip 19::114152
    [Crossref] [Google Scholar]
  57. 57.
    Donoughe S, Kim C, Extavour CG. 2018.. High-throughput live-imaging of embryos in microwell arrays using a modular specimen mounting system. . Biol. Open 7::bio031260
    [Crossref] [Google Scholar]
  58. 58.
    Morel M, Bartolo D, Galas J-C, Dahan M, Studer V. 2009.. Microfluidic stickers for cell- and tissue-based assays in microchannels. . Lab Chip 9::101113
    [Crossref] [Google Scholar]
  59. 59.
    Fan A, Tofangchi A, De Venecia M, Saif T. 2018.. A simple microfluidic platform for the partial treatment of insuspendable tissue samples with orientation control. . Lab Chip 18::73542
    [Crossref] [Google Scholar]
  60. 60.
    Ardeshiri R, Rezai P. 2016.. Lab-on-chips for manipulation of small-scale organisms to facilitate imaging of neurons and organs. . In Proc. 8th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 574952. New York:: IEEE
    [Google Scholar]
  61. 61.
    Furlong EEM, Profitt D, Scott MP. 2001.. Automated sorting of live transgenic embryos. . Nat. Biotechnol. 19::153
    [Crossref] [Google Scholar]
  62. 62.
    Merten C, Tseng Q, Utharala R, Frese L. 2020.. Microfluidic sorting device. US Patent US10569272B2
    [Google Scholar]
  63. 63.
    Gu W, Zhu X, Futai N, Cho BS, Takayama S. 2004.. Computerized microfluidic cell culture using elastomeric channels and Braille displays. . PNAS 101::1586166
    [Crossref] [Google Scholar]
  64. 64.
    Utharala R, Tseng Q, Furlong EEM, Merten CA. 2018.. A versatile, low-cost, multiway microfluidic sorter for droplets, cells, and embryos. . Anal. Chem. 90::598288
    [Crossref] [Google Scholar]
  65. 65.
    Pulak R. 2006.. Techniques for analysis, sorting, and dispensing of C. elegans on the COPAS™ flow-sorting system. . In C. Elegans: Methods and Applications, ed. K Strange , pp. 27586. New York:: Springer
    [Google Scholar]
  66. 66.
    Thompson JGP, Schedl P, Pulak R. 2004.. Sex-specific GFP-expression in Drosophila embryos and sorting by COPAS™ flow cytometry technique. Paper presented at the 45th Annual Drosophila Research Conference, Washington, DC:, March 24–28
    [Google Scholar]
  67. 67.
    Halfon MS, Gisselbrecht S, Lu J, Estrada B, Keshishian H, Michelson AM. 2002.. New fluorescent protein reporters for use with the Drosophila Gal4 expression system and for vital detection of balancer chromosomes. . Genesis 34::13538
    [Crossref] [Google Scholar]
  68. 68.
    Gasque G, Conway S, Huang J, Rao Y, Vosshall LB. 2013.. Small molecule drug screening in Drosophila identifies the 5HT2A receptor as a feeding modulation target. . Sci. Rep. 3::srep02120
    [Crossref] [Google Scholar]
  69. 69.
    Marty F, Rockel-Bauer C, Simigdala N, Brunner E, Basler K. 2014.. Large-scale imaginal disc sorting: a protocol for “omics”-approaches. . Methods 68::26064
    [Crossref] [Google Scholar]
  70. 70.
    Marty F, Rago G, Smith DF, Gao X, Eijkel GB, et al. 2017.. Combining time-of-flight secondary ion mass spectrometry imaging mass spectrometry and CARS microspectroscopy reveals lipid patterns reminiscent of gene expression patterns in the wing imaginal disc of Drosophila melanogaster. . Anal. Chem. 89::966470
    [Crossref] [Google Scholar]
  71. 71.
    Rodriguez TP, Mast JD, Hartl T, Lee T, Sand P, Perlstein EO. 2018.. Defects in the neuroendocrine axis contribute to global development delay in a Drosophila model of NGLY1 deficiency. . G3 8::2193204
    [Crossref] [Google Scholar]
  72. 72.
    Sokolowski MB. 2001.. Drosophila: genetics meets behaviour. . Nat. Rev. Genet. 2::87990
    [Crossref] [Google Scholar]
  73. 73.
    Zhang Y, Thapaliya ER, Tang S, Baker JD, Raymo FM. 2016.. Supramolecular delivery of fluorescent probes in developing embryos. . RSC Adv. 6::7275660
    [Crossref] [Google Scholar]
  74. 74.
    Alegria AD, Joshi AS, Mendana JB, Khosla K, Smith K, et al. 2023.. A machine vision guided robot for fully automated embryonic microinjection. . bioRxiv 2023.04.25.538280. https://doi.org/10.1101/2023.04.25.538280
  75. 75.
    Zappe S, Fish M, Scott MP, Solgaard O. 2006.. Automated MEMS-based Drosophila embryo injection system for high-throughput RNAi screens. . Lab Chip 6::101219
    [Crossref] [Google Scholar]
  76. 76.
    Zappe S, Zhang XJ, Jung IW, Bernstein RW, Furlong EEM, et al. 2002.. Micromachined hollow needle with integrated pressure sensor for precise, calibrated injection into cells and embryos. . In Micro Total Analysis Systems 2002, ed. Y Baba, S Shoji, A van den Berg , pp. 68284. Dordrecht, Neth:.: Springer
    [Google Scholar]
  77. 77.
    Zhang XJ, Zappe S, Bernstein RW, Sahin O, Chen CC, et al. 2004.. Micromachined silicon force sensor based on diffractive optical encoders for characterization of microinjection. . Sens. Actuators A Phys. 114::197203
    [Crossref] [Google Scholar]
  78. 78.
    Delubac D, Highley CB, Witzberger-Krajcovic M, Ayoob JC, Furbee EC, et al. 2012.. Microfluidic system with integrated microinjector for automated Drosophila embryo injection. . Lab Chip 12::491119
    [Crossref] [Google Scholar]
  79. 79.
    Zheng M, Tian SZ, Capurso D, Kim M, Maurya R, et al. 2019.. Multiplex chromatin interactions with single-molecule precision. . Nature 566::55862
    [Crossref] [Google Scholar]
  80. 80.
    Zheng GXY, Lau BT, Schnall-Levin M, Jarosz M, Bell JM, et al. 2016.. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. . Nat. Biotechnol. 34::30311
    [Crossref] [Google Scholar]
  81. 81.
    Li H. 2021.. Single-cell RNA sequencing in Drosophila: technologies and applications. . Wiley Interdiscip. Rev. Dev. Biol. 10::e396
    [Crossref] [Google Scholar]
  82. 82.
    De Rop FV, Ismail JN, González-Blas CB, Hulselmans GJ, Flerin CC, et al. 2022.. Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads. . eLife 11::e73971
    [Crossref] [Google Scholar]
  83. 83.
    Markow TA, O'Grady P. 2008.. Reproductive ecology of Drosophila. . Funct. Ecol. 22::74759
    [Crossref] [Google Scholar]
  84. 84.
    Rand MD, Kearney AL, Dao J, Clason T. 2010.. Permeabilization of Drosophila embryos for introduction of small molecules. . Insect Biochem. Mol. Biol. 40::792804
    [Crossref] [Google Scholar]
  85. 85.
    Makos MA, Kuklinski NJ, Heien ML, Ewing AG, Berglund EC. 2009.. Chemical measurements in Drosophila. . Trends Analyt. Chem. 28::122334
    [Crossref] [Google Scholar]
  86. 86.
    Berthier E, Young EWK, Beebe D. 2012.. Engineers are from PDMS-land, biologists are from Polystyrenia. . Lab Chip 12::122437
    [Crossref] [Google Scholar]
  87. 87.
    Wang Z, Oppegard SC, Eddington DT, Cheng J. 2017.. Effect of localized hypoxia on Drosophila embryo development. . PLOS ONE 12::e0185267
    [Crossref] [Google Scholar]
  88. 88.
    Fakhoury JR, Sisson JC, Zhang X. 2009.. Microsystems for controlled genetic perturbation of live Drosophila embryos: RNA interference, development robustness and drug screening. . Microfluid. Nanofluid. 6::299313
    [Crossref] [Google Scholar]
  89. 89.
    McGorty R, Liu H, Kamiyama D, Dong Z, Guo S, Huang B. 2015.. Open-top selective plane illumination microscope for conventionally mounted specimens. . Opt. Express 23::1614253
    [Crossref] [Google Scholar]
  90. 90.
    Jeon NL, Dertinger SKW, Chiu DT, Choi IS, Stroock AD, Whitesides GM. 2000.. Generation of solution and surface gradients using microfluidic systems. . Langmuir 16::831116
    [Crossref] [Google Scholar]
  91. 91.
    Ghaemi R, Arefi P, Stosic A, Acker M, Raza Q, et al. 2017.. A microfluidic microinjector for toxicological and developmental studies in Drosophila embryos. . Lab Chip 17::3898908
    [Crossref] [Google Scholar]
  92. 92.
    Zabihihesari A, Hilliker AJ, Rezai P. 2020.. Localized microinjection of intact Drosophila melanogaster larva to investigate the effect of serotonin on heart rate. . Lab Chip 20::34355
    [Crossref] [Google Scholar]
  93. 93.
    Zabihihesari A, Parand S, Coulthard AB, Molnar A, Hilliker AJ, Rezai P. 2022.. An in-vivo microfluidic assay reveals cardiac toxicity of heavy metals and the protective effect of metal responsive transcription factor (MTF-1) in Drosophila model. . 3 Biotech 12::279
    [Crossref] [Google Scholar]
  94. 94.
    Zabihihesari A, Khalili A, Hilliker AJ, Rezai P. 2021.. Open access tool and microfluidic devices for phenotypic quantification of heart function of intact fruit fly and zebrafish larvae. . Comput. Biol. Med. 132::104314
    [Crossref] [Google Scholar]
  95. 95.
    Si G, Kanwal JK, Hu Y, Tabone CJ, Baron J, et al. 2019.. Structured odorant response patterns across a complete olfactory receptor neuron population. . Neuron 101::95062
    [Crossref] [Google Scholar]
  96. 96.
    Leung JCK, Hilliker AJ, Rezai P. 2016.. An integrated hybrid microfluidic device for oviposition-based chemical screening of adult Drosophila melanogaster. . Lab Chip 16::70919
    [Crossref] [Google Scholar]
  97. 97.
    van Giesen L, Neagu-Maier GL, Kwon JY, Sprecher SG. 2016.. A microfluidics-based method for measuring neuronal activity in Drosophila chemosensory neurons. . Nat. Protoc. 11::2389
    [Crossref] [Google Scholar]
  98. 98.
    Choi J, Yu S, Choi MS, Jang S, Han IJ, et al. 2020.. Cellular basis of bitter-driven aversive behaviors in Drosophila larva. . Eneuro 7:(2):ENEURO.0510-19.2020
    [Crossref] [Google Scholar]
  99. 99.
    Fan A, Joy MSH, Saif T. 2019.. A connected cytoskeleton network generates axonal tension in embryonic Drosophila. . Lab Chip 19::313339
    [Crossref] [Google Scholar]
  100. 100.
    Kong Q, Able RA, Dudu V, Vazquez M. 2010.. A microfluidic device to establish concentration gradients using reagent density differences. . J. Biomech. Eng. 132::121012
    [Crossref] [Google Scholar]
  101. 101.
    Pena CD, Zhang S, Majeska R, Venkatesh T, Vazquez M. 2019a.. Invertebrate retinal progenitors as regenerative models in a microfluidic system. . Cells 8::1301
    [Crossref] [Google Scholar]
  102. 102.
    Pena CD, Zhang S, Markey M, Venkatesh T, Vazquez M. 2019b.. Collective behaviors of Drosophila-derived retinal progenitors in controlled microenvironments. . PLOS ONE 14::e0226250
    [Crossref] [Google Scholar]
  103. 103.
    Zhang S, Markey M, Pena CD, Venkatesh T, Vazquez M. 2020.. A micro-optic stalk (μOS) system to model the collective migration of retinal neuroblasts. . Micromachines 11::363
    [Crossref] [Google Scholar]
  104. 104.
    Ingber DE. 2018.. From mechanobiology to developmentally inspired engineering. . Philos. Trans. R. Soc. B Biol. Sci. 373::20170323
    [Crossref] [Google Scholar]
  105. 105.
    Pouille P-A, Ahmadi P, Brunet A-C, Farge E. 2009.. Mechanical signals trigger Myosin II redistribution and mesoderm invagination in Drosophila embryos. . Sci. Signal. 2::ra16
    [Crossref] [Google Scholar]
  106. 106.
    Farge E. 2003.. Mechanical induction of Twist in the Drosophila foregut/stomodeal primordium. . Curr. Biol. 13::136577
    [Crossref] [Google Scholar]
  107. 107.
    Lenne P-F, Munro E, Heemskerk I, Warmflash A, Bocanegra-Moreno L, et al. 2021.. Roadmap for the multiscale coupling of biochemical and mechanical signals during development. . Phys. Biol. 18::041501
    [Crossref] [Google Scholar]
  108. 108.
    Zhang W, Sobolevski A, Li B, Rao Y, Liu X. 2015.. An automated force-controlled robotic micromanipulation system for mechanotransduction studies of Drosophila larvae. . IEEE Trans. Autom. Sci. Eng. 13::78997
    [Crossref] [Google Scholar]
  109. 109.
    Zhang W, Dong X, Liu X. 2016.. Switched fuzzy-PD control of contact forces in robotic microbiomanipulation. . IEEE Trans. Biomed. Eng. 64::116977
    [Crossref] [Google Scholar]
  110. 110.
    Narciso CE, Contento NM, Storey TJ, Hoelzle DJ, Zartman JJ. 2017.. Release of applied mechanical loading stimulates intercellular calcium waves in Drosophila wing discs. . Biophys. J. 113::491501
    [Crossref] [Google Scholar]
  111. 111.
    He L, Si G, Huang J, Samuel ADT, Perrimon N. 2018.. Mechanical regulation of stem-cell differentiation by the stretch-activated Piezo channel. . Nature 555::1036
    [Crossref] [Google Scholar]
  112. 112.
    Shiomi A, Nagao K, Yokota N, Tsuchiya M, Kato U, et al. 2021.. Extreme deformability of insect cell membranes is governed by phospholipid scrambling. . Cell Rep. 35::109219
    [Crossref] [Google Scholar]
  113. 113.
    Shiwarski DJ, Tashman JW, Tsamis A, Bliley JM, Blundon MA, et al. 2020.. Fibronectin-based nanomechanical biosensors to map 3D surface strains in live cells and tissue. . Nat. Commun. 11::5883
    [Crossref] [Google Scholar]
  114. 114.
    Ghaemi R, Rezai P, Iyengar BG, Selvaganapathy PR. 2015.. Microfluidic devices for imaging neurological response of Drosophila melanogaster larva to auditory stimulus. . Lab Chip 15::111622
    [Crossref] [Google Scholar]
  115. 115.
    Jiang L, Kraft R, Restifo LL, Zohar Y. 2015.. Dissociation of brain tissue into viable single neurons in a microfluidic device. . In 2015 9th IEEE International Conference on Nano/Molecular Medicine & Engineering (NANOMED), pp. 2932. New York:: IEEE
    [Google Scholar]
  116. 116.
    Leung JCK, Taylor-Kamall RW, Hilliker AJ, Rezai P. 2015.. Agar-polydimethylsiloxane devices for quantitative investigation of oviposition behaviour of adult Drosophila melanogaster. . Biomicrofluidics 9::034112
    [Crossref] [Google Scholar]
  117. 117.
    Kuntz SG, Eisen MB. 2014.. Drosophila embryogenesis scales uniformly across temperature in developmentally diverse species. . PLOS Genet. 10::e1004293
    [Crossref] [Google Scholar]
  118. 118.
    Lucchetta EM, Munson MS, Ismagilov RF. 2006.. Characterization of the local temperature in space and time around a developing Drosophila embryo in a microfluidic device. . Lab Chip 6::18590
    [Crossref] [Google Scholar]
  119. 119.
    Lucchetta EM, Lee JH, Fu LA, Patel NH, Ismagilov RF. 2005.. Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics. . Nature 434::113438
    [Crossref] [Google Scholar]
  120. 120.
    Lucchetta EM, Vincent ME, Ismagilov RF. 2008.. A precise Bicoid gradient is nonessential during cycles 11–13 for precise patterning in the Drosophila blastoderm. . PLOS ONE 3::e3651
    [Crossref] [Google Scholar]
  121. 121.
    Lucchetta EM, Carthew RW, Ismagilov RF. 2009.. The endo-siRNA pathway is essential for robust development of the Drosophila embryo. . PLOS ONE 4::e7576
    [Crossref] [Google Scholar]
  122. 122.
    Esposito E, Lim B, Guessous G, Falahati H, Levine M. 2016.. Mitosis-associated repression in development. . Genes Dev. 30::15038
    [Crossref] [Google Scholar]
  123. 123.
    Bai Z, Bao H, Yuan Y, Yang X, Xi Y, Wang M. 2017.. Real-time observation of perturbation of a Drosophila embryo's early cleavage cycles with microfluidics. . Anal. Chim. Acta 982::13137
    [Crossref] [Google Scholar]
  124. 124.
    Falahati H, Hur W, Di Tali S, Wieschaus EF. 2021.. Temperature-induced uncoupling of cell cycle regulators. . Dev. Biol. 470::14753
    [Crossref] [Google Scholar]
  125. 125.
    Falahati H, Wieschaus E. 2017.. Independent active and thermodynamic processes govern the nucleolus assembly in vivo. . PNAS 114::133540
    [Crossref] [Google Scholar]
  126. 126.
    Dagani GT, Monzo K, Fakhoury JR, Chen C-C, Sisson JC, Zhang X. 2007.. Microfluidic self-assembly of live Drosophila embryos for versatile high-throughput analysis of embryonic morphogenesis. . Biomed. Microdev. 9::68194
    [Crossref] [Google Scholar]
  127. 127.
    Zhu H, Cui Y, Luo C, Liu F. 2020.. Quantifying temperature compensation of Bicoid gradients with a fast T-tunable microfluidic device. . Biophys. J. 119:(6):P1193203
    [Crossref] [Google Scholar]
  128. 128.
    Kim D, Alvarez M, Lechuga LM, Louis M. 2017.. Species-specific modulation of food-search behavior by respiration and chemosensation in Drosophila larvae. . eLife 6::e27057
    [Crossref] [Google Scholar]
  129. 129.
    Navawongse R, Choudhury D, Raczkowska M, Stewart JC, Lim T, et al. 2016.. Drosophila learn efficient paths to a food source. . Neurobiol. Learn. Memory 131::17681
    [Crossref] [Google Scholar]
  130. 130.
    Skinner BF. 1930.. On the conditions of elicitation of certain eating reflexes. . PNAS 16::43338
    [Crossref] [Google Scholar]
  131. 131.
    Eriksson A, Raczkowska M, Navawongse R, Choudhury D, Stewart JC, et al. 2017.. Neuromodulatory circuit effects on Drosophila feeding behaviour and metabolism. . Sci. Rep. 7::8839
    [Crossref] [Google Scholar]
  132. 132.
    Ro J, Harvanek ZM, Pletcher SD. 2014.. FLIC: high-throughput, continuous analysis of feeding behaviors in Drosophila. . PLOS ONE 9::e101107
    [Crossref] [Google Scholar]
  133. 133.
    Musso P-Y, Junca P, Jelen M, Feldman-Kiss D, Zhang H, et al. 2019.. Closed-loop optogenetic activation of peripheral or central neurons modulates feeding in freely moving Drosophila. . eLife 8::e45636
    [Crossref] [Google Scholar]
  134. 134.
    Moreira J-M, Itskov PM, Goldschmidt D, Baltazar C, Steck K, et al. 2019.. optoPAD, a closed-loop optogenetics system to study the circuit basis of feeding behaviors. . eLife 8::e43924
    [Crossref] [Google Scholar]
  135. 135.
    May CE, Vaziri A, Lin YQ, Grushko O, Khabiri M, et al. 2019.. High dietary sugar reshapes sweet taste to promote feeding behavior in Drosophila melanogaster. . Cell Rep. 27::167585
    [Crossref] [Google Scholar]
  136. 136.
    Seong K-H, Matsumura T, Shimada-Niwa Y, Niwa R, Kang S. 2020.. The Drosophila individual activity monitoring and detection system (DIAMonDS). . eLife 9::e58630
    [Crossref] [Google Scholar]
  137. 137.
    Bohbot JD, Vernick S. 2020.. The emergence of insect odorant receptor-based biosensors. . Biosensors 10::26
    [Crossref] [Google Scholar]
  138. 138.
    Mitsuno H, Sakurai T, Kanzaki R. 2020.. Application of olfactory detection systems in sensing technologies. . In Insect Sex Pheromone Research and Beyond, ed. Y Ishikawa , pp. 22140. Singapore:: Springer
    [Google Scholar]
  139. 139.
    Grill SW. 2011.. Growing up is stressful: biophysical laws of morphogenesis. . Curr. Opin. Genet. Dev. 21::64752
    [Crossref] [Google Scholar]
  140. 140.
    Dickinson M. 2006.. Insect flight. . Curr. Biol. 16::R30914
    [Crossref] [Google Scholar]
  141. 141.
    Lye CM, Sanson B. 2011.. Tension and epithelial morphogenesis in Drosophila early embryos. . In Current Topics in Developmental Biology, Vol. 95, ed. M Labouesse , pp. 14587. Amsterdam:: Elsevier
    [Google Scholar]
  142. 142.
    Heisenberg C-P, Bellaïche Y. 2013.. Forces in tissue morphogenesis and patterning. . Cell 153::94862
    [Crossref] [Google Scholar]
  143. 143.
    Herrera-Perez RM, Kasza KE. 2019.. Manipulating the patterns of mechanical forces that shape multicellular tissues. . Physiology 34::38191
    [Crossref] [Google Scholar]
  144. 144.
    Shen Y, Wejinya UC, Xi N, Pomeroy CA. 2007.. Force measurement and mechanical characterization of living Drosophila embryos for human medical study. . Proc. Inst. Mech. Eng. H 221::99112
    [Crossref] [Google Scholar]
  145. 145.
    Chen Z, Shen Y, Xi N, Tan X. 2007.. Integrated sensing for ionic polymer–metal composite actuators using PVDF thin films. . Smart Mater. Struct. 16::S262
    [Crossref] [Google Scholar]
  146. 146.
    Levis M, Kumar N, Apakian E, Moreno C, Hernandez U, et al. 2019.. Microfluidics on the fly: inexpensive rapid fabrication of thermally laminated microfluidic devices for live imaging and multimodal perturbations of multicellular systems. . Biomicrofluidics 13::024111
    [Crossref] [Google Scholar]
  147. 147.
    Rotstein B, Paululat A. 2016.. On the morphology of the Drosophila heart. . J. Cardiovasc. Dev. Dis. 3::15
    [Google Scholar]
  148. 148.
    Zabihihesari A, Parand S, Rezai P. 2023.. PDMS-based microfluidic capillary pressure-driven viscometry and application to Drosophila melanogaster’s hemolymph. . Microfluid. Nanofluid. 27::8
    [Crossref] [Google Scholar]
  149. 149.
    Lowe GDO, Barbenel JC. 2019.. Plasma and blood viscosity. . In Clinical Blood Rheology, ed. GDO Lowe , pp. 1144. Boca Raton, FL:: CRC Press
    [Google Scholar]
  150. 150.
    Olejnik DA, Muijres FT, Karásek M, Camilo LH, De Wagter C, de Croon GC. 2022.. Flying into the wind: insects and bio-inspired micro-air-vehicles with a wing-stroke dihedral steer passively into wind-gusts. . Front. Robot. AI 9::820363
    [Crossref] [Google Scholar]
  151. 151.
    Dickinson MH, Tu MS. 1997.. The function of dipteran flight muscle. . Comp. Biochem. Physiol. A Mol. Integr. Physiol. 116::22338
    [Crossref] [Google Scholar]
  152. 152.
    Gu M, Wu J, Zhang Y. 2020.. Wing rapid responses and aerodynamics of fruit flies during headwind gust perturbations. . Bioinspir. Biomimet. 15::056001
    [Crossref] [Google Scholar]
  153. 153.
    Götz KG. 1968.. Flight control in Drosophila by visual perception of motion. . Kybernetik 4::199208
    [Crossref] [Google Scholar]
  154. 154.
    Bogue R. 2007.. MEMS sensors: past, present and future. . Sensor Rev. 27::713
    [Crossref] [Google Scholar]
  155. 155.
    Sun Y, Nelson BJ. 2007.. MEMS capacitive force sensors for cellular and flight biomechanics. . Biomed. Mater. 2::S1622
    [Crossref] [Google Scholar]
  156. 156.
    Sun Y, Fry SN, Potasek DP, Bell DJ, Nelson BJ. 2005.. Characterizing fruit fly flight behavior using a microforce sensor with a new comb-drive configuration. . J. Microelectromech. Syst. 14::411
    [Crossref] [Google Scholar]
  157. 157.
    Graetzel CF, Fry SN, Beyeler F, Sun Y, Nelson BJ. 2008.. Real-time microforce sensors and high speed vision system for insect flight control analysis. . In Proc. 10th International Symposium on Experimental Robotics, ed. O Khatib, V Kumar, D Rus , pp. 45160. Berlin:: Springer
    [Google Scholar]
  158. 158.
    Graetzel CF, Nelson BJ, Fry SN. 2008.. Reverse-engineering lift control in fruit flies. . In Proc. 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 4027. New York:: IEEE
    [Google Scholar]
  159. 159.
    Graetzel CF, Nelson BJ, Fry SN. 2010.. Frequency response of lift control in Drosophila. . J. R. Soc. Interface 7::160316
    [Crossref] [Google Scholar]
  160. 160.
    Hao Y, Miller MS, Swank DM, Liu H, Bernstein SI, et al. 2006.. Passive stiffness in Drosophila indirect flight muscle reduced by disrupting paramyosin phosphorylation, but not by embryonic myosin S2 hinge substitution. . Biophys. J. 91::45006
    [Crossref] [Google Scholar]
  161. 161.
    Gravish N, Lauder GV. 2018.. Robotics-inspired biology. . J. Exp. Biol. 221::jeb138438
    [Crossref] [Google Scholar]
  162. 162.
    Karásek M, Muijres FT, De Wagter C, Remes BDW, de Croon GCHE. 2018.. A tailless aerial robotic flapper reveals that flies use torque coupling in rapid banked turns. . Science 361::108994
    [Crossref] [Google Scholar]
  163. 163.
    Chen Y, Zhao H, Mao J, Chirarattananon P, Helbling EF, et al. 2019.. Controlled flight of a microrobot powered by soft artificial muscles. . Nature 575::32429
    [Crossref] [Google Scholar]
  164. 164.
    Serrano JC, Gupta SK, Kamm RD, Guo M. 2021.. In pursuit of designing multicellular engineered living systems: a fluid mechanical perspective. . Annu. Rev. Fluid Mech. 53::41137
    [Crossref] [Google Scholar]
  165. 165.
    Shum AJ, Parviz BA. 2007.. Drosophila as an unconventional substrate for microfabrication. . In Proc. SPIE, Vol. 6464, MEMS/MOEMS Components and Their Applications IV, 646403 . Bellingham, WA:: International Society for Optics and Photonics
    [Google Scholar]
  166. 166.
    Shum AJ, Crest J, Schubiger G, Parviz BA. 2007.. Drosophila as a live substrate for solid-state microfabrication. . Adv. Mater. 19::360812
    [Crossref] [Google Scholar]
  167. 167.
    Sun L, Yu Y, Chen Z, Bian F, Ye F, et al. 2020.. Biohybrid robotics with living cell actuation. . Chem. Soc. Rev. 49::404369
    [Crossref] [Google Scholar]
  168. 168.
    Yamatsuta E, Beh SP, Uesugi K, Tsujimura H, Morishima K. 2019.. A micro peristaltic pump using an optically controllable bioactuator. . Engineering 5::58085
    [Crossref] [Google Scholar]
  169. 169.
    Iyer V, Nandakumar R, Wang A, Fuller SB, Gollakota S. 2019.. Living IoT: a flying wireless platform on live insects. . In MobiCom '19: The 25th Annual International Conference on Mobile Computing and Networking, pp. 115. New York:: ACM
    [Google Scholar]
  170. 170.
    Paydar OH, Chung A, Niknam D, Fung AO, Matthews B, et al. 2011.. MEMS-enabled multi-unit neural recording from Drosophila melanogaster. . In Proc. 2011 IEEE 24th International Conference on Micro Electro Mechanical Systems, pp. 92427. New York:: IEEE
    [Google Scholar]
  171. 171.
    Rees HR, Anderson SE, Privman E, Bau HH, Venton BJ. 2015.. Carbon nanopipette electrodes for dopamine detection in Drosophila. . Anal. Chem. 87::384955
    [Crossref] [Google Scholar]
  172. 172.
    Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. 2018.. Next-generation machine learning for biological networks. . Cell 173::158192
    [Crossref] [Google Scholar]
  173. 173.
    Nawaz AA, Urbanska M, Herbig M, Nötzel M, Kräter M, et al. 2020.. Intelligent image-based deformation-assisted cell sorting with molecular specificity. . Nat. Methods 17::59599
    [Crossref] [Google Scholar]
  174. 174.
    Riordon J, Sovilj D, Sanner S, Sinton D, Young EWK. 2019.. Deep learning with microfluidics for biotechnology. . Trends Biotechnol. 37::31024
    [Crossref] [Google Scholar]
  175. 175.
    Krzic U, Gunther S, Saunders TE, Streichan SJ, Hufnagel L. 2012.. Multiview light-sheet microscope for rapid in toto imaging. . Nat. Methods 9::73033
    [Crossref] [Google Scholar]
  176. 176.
    Fowlkes CC, Hendriks CLL, Keränen SVE, Weber GH, Rübel O, et al. 2008.. A quantitative spatiotemporal atlas of gene expression in the Drosophila blastoderm. . Cell 133::36474
    [Crossref] [Google Scholar]
  177. 177.
    Han X, Su Y, White H, O'Neill KM, Morgan NY, et al. 2021.. A polymer index-matched to water enables diverse applications in fluorescence microscopy. . Lab Chip 21::154962
    [Crossref] [Google Scholar]
  178. 178.
    Memeo R, Paiè P, Sala F, Castriotta M, Guercio C, et al. 2021.. Automatic imaging of Drosophila embryos with light sheet fluorescence microscopy on chip. . J. Biophoton. 14::e202000396
    [Crossref] [Google Scholar]
  179. 179.
    Paiè P, Memeo R, Sala F, Bassi A, Osellame R, Bragheri F. 2020.. Biological analysis in 3D optofluidic devices fabricated by femtosecond laser micromachining. . In Proc. SPIE, Vol. 11268, Laser-based Micro- and Nanoprocessing XIV, 1126806 . Bellingham, WA:: International Society for Optics and Photonics
    [Google Scholar]
  180. 180.
    Paiè P, Memeo R, Sala F, Castriotta M, Vaccari T, et al. 2020.. Dual-color on-chip light sheet microscopy of drosophila embryos. . In Proc. SPIE, Vol. 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII, 112430K . Bellingham, WA:: International Society for Optics and Photonics
    [Google Scholar]
  181. 181.
    Chen X, Ping J, Sun Y, Yi C, Liu S, et al. 2021.. Deep-learning on-chip light-sheet microscopy enabling video-rate volumetric imaging of dynamic biological specimens. . Lab Chip 21::342028
    [Crossref] [Google Scholar]
  182. 182.
    Zhao Y, Demirci U, Chen Y, Chen P. 2020.. Multiscale brain research on a microfluidic chip. . Lab Chip 20::153143
    [Crossref] [Google Scholar]
  183. 183.
    Peimani AR, Zoidl G, Rezai P. 2018.. A microfluidic device to study electrotaxis and dopaminergic system of zebrafish larvae. . Biomicrofluidics 12::014113
    [Crossref] [Google Scholar]
  184. 184.
    Rezai P, Siddiqui A, Selvaganapathy PR, Gupta BP. 2010.. Electrotaxis of Caenorhabditis elegans in a microfluidic environment. . Lab Chip 10::22026
    [Crossref] [Google Scholar]
  185. 185.
    Tong J. 2014.. Chemical and genetic screening applications of a microfluidic electrotaxis assay using nematode Caenorhabditis elegans. PhD Thesis , McMaster Univ., Hamilton, Ont., Can:.
    [Google Scholar]
  186. 186.
    Badhiwala KN, Gonzales DL, Vercosa DG, Avants BW, Robinson JT. 2018.. Microfluidics for electrophysiology, imaging, and behavioral analysis of Hydra. . Lab Chip 18::252339
    [Crossref] [Google Scholar]
  187. 187.
    Riedl J. 2013.. Identification of neurons controlling orientation behavior in the Drosophila melanogaster larva. PhD Thesis , Univ. Pompeu Fabra, Barcelona, Spain:
    [Google Scholar]
  188. 188.
    Gonzales DL, Badhiwala KN, Avants BW, Robinson JT. 2020.. Bioelectronics for millimeter-sized model organisms. . iScience 23::100917
    [Crossref] [Google Scholar]
  189. 189.
    Ismagilov RF, Maharbiz MM. 2007.. Can we build synthetic, multicellular systems by controlling developmental signaling in space and time?. Curr. Opin. Chem. Biol. 11::60411
    [Crossref] [Google Scholar]
  190. 190.
    Chang T-Y, Pardo-Martin C, Allalou A, Wählby C, Yanik MF. 2012.. Fully automated cellular-resolution vertebrate screening platform with parallel animal processing. . Lab Chip 12::71116
    [Crossref] [Google Scholar]
  191. 191.
    Stirman JN, Harker B, Lu H, Crane MM. 2014.. Animal microsurgery using microfluidics. . Curr. Opin. Biotechnol. 25::2429
    [Crossref] [Google Scholar]
  192. 192.
    Floreano D, Pericet-Camara R, Viollet S, Ruffier F, Brückner A, et al. 2013.. Miniature curved artificial compound eyes. . PNAS 110::926772
    [Crossref] [Google Scholar]
  193. 193.
    Zhai Y, Han Q, Niu J, Liu J, Yang B. 2021.. Microfabrication of bioinspired curved artificial compound eyes: a review. . Microsyst. Technol. 27::324162
    [Crossref] [Google Scholar]
  194. 194.
    Dickinson MH, Gotz KG. 1993.. Unsteady aerodynamic performance of model wings at low Reynolds numbers. . J. Exp. Biol. 174::4564
    [Crossref] [Google Scholar]
  195. 195.
    Dickinson MH, Lehmann F-O, Sane SP. 1999.. Wing rotation and the aerodynamic basis of insect flight. . Science 284::195460
    [Crossref] [Google Scholar]
  196. 196.
    Zhan M, Chingozha L, Lu H. 2013.. Enabling systems biology approaches through microfabricated systems. . Anal. Chem. 85::888294
    [Crossref] [Google Scholar]
  197. 197.
    Yapici N, Cohn R, Schusterreiter C, Ruta V, Vosshall LB. 2016.. A taste circuit that regulates ingestion by integrating food and hunger signals. . Cell 165:(3):71529
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-bioeng-050423-054647
Loading
/content/journals/10.1146/annurev-bioeng-050423-054647
Loading

Data & Media loading...

Supplemental Materials

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error