Skip to main content
SpringerLink
Log in

SIMOP: A SIMulation-Guided OPtimization Mechanism for Sample Preparation with Digital Microfluidic Biochip

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Increased use of digital microfluidic (DMF) biochips has fueled the replacement of expensive healthcare and biochemical laboratory procedures with low-cost, fully-automated, miniaturized integrated systems. Dilution and mixing of fluid samples in a certain ratio are two fundamental primitives needed in sample preparation, which is an essential component of almost all protocols. Most of the existing dilution algorithms used in droplet-based microfluidic systems deploy a sequence of (1 : 1) mix-split steps, where two unit volume droplets of different concentrations are mixed, followed by a balanced split operation to obtain two equal-sized droplets. In this work, we introduce a simulation-guided optimization procedure (SIMOP) for achieving the target concentrations while optimizing multiple factors according to user-specified priority levels. The SIMOP algorithm produces a given concentration while optimizing each criterion as desired. Experimental results favorably demonstrate the performance of the proposed procedure compared to many previous algorithms used for single-target dilution. We also study the impact of errors caused by unbalanced droplet splitting on the accuracy of target concentration obtained by SIMOP sequences. Experimental study reveals that SIMOP outperforms previous algorithms from the perspective of volumetric error management that may occur in reaction paths. The proposed technique may find many potential applications to robust sample preparation needed in diverse areas of biomedical engineering and healthcare domains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Notes

  1. Note that comparison of the above parameters (waste, sample and buffer) directly with WD dilution scheme will not be fair, since in the last step, the WD scheme may generate either 2 droplets or 3 droplets of output, whereas SIMOP being a balanced scheme always generates 2 droplets of output. Therefore, to make an apple to apple comparison between SIMOP and WD schemes, we should ideally normalize the average values of the waste, sample and buffer by dividing each of them with 2 for SIMOP and a factor x for WD, where x is the number of output droplets averaged over all the fractions (\(2~<~x~<~3\)). Hence, though we have also presented the values for WD in the tables, we have not directly compared them with SIMOP values.

References

  1. Abdelgawad M, Wheeler AR. The digital revolution: a new paradigm for digital microfluidic biochips. Adv Mater. 2009;21(8):920–5.

    Article  Google Scholar 

  2. Alistar M, Maftei E, Pop P, Madsen J. Synthesis of biochemical applications on Digital Microfluidic Biochips with operation variability. In: Proceedings of the symposium on design test integration and packaging of MEMS/MOEMS; 2010. pp. 350–7.

  3. Alistar M, Pop P, Madsen J. Online synthesis for error recovery in digital microfluidic biochips with operation variability. In: Proc. of DTIP (MEMS/MOEMS); 2012. pp. 53–58.

  4. Bera N, Majumder S, Bhattacharya BB. Analysis of concentration errors in sample dilution algorithms on a digital microfluidic biochip. In: Proceedings of the first international conference on applied algorithms (LNCS), Vol. 8321 Springer; 2014. pp. 89–100.

  5. Bera N, Majumder S, Bhattacharya BB. Simulation-based method for optimum microfluidic sample dilution using weighted mix-split of droplets. IET Comput Dig Tech. 2016;10(3):119–27.

    Article  Google Scholar 

  6. Chakrabarty K, Xu T. Digital microfluidic biochips: design and optimization. Boca Raton: CRC Press; 2010.

    Book  Google Scholar 

  7. Chiang T-W, Liu C-H, Huang J. Graph-based optimal reactant minimization for sample preparation on digital microfluidic biochips. In: International symposium on VLSI design, automation, and test (VLSI-DAT); 2013. pp. 1–4.

  8. Das S, Mukherjee S, Aryani N, Majumder S, Bera N, Bhattacharya BB. SIMOP-A fast tool for generating optimum dilutions of microfluidic samples using simulation. In: Proceedings of 2016 IEEE international conference on distributed computing, VLSI, electrical circuits and robotics (DISCOVER), Vol. 3 IEEE Xplore; 2016. pp. 1–6.

  9. Dinh TA, Yamashita S, Ho TY. A network-flow-based optimal sample preparation algorithm for digital microfluidic biochips. In: 19th Asia and South Pacific Design Automation Conference (ASP-DAC); 2014. pp. 225–30.

  10. Fair RB, Srinivasan V, Ren H, Paik P, Pamula VK, Pollack MG. Electrowetting-based on-chip Sample processing for integrated microfluidics. In: Proceedings of IEEE IEDM Tech. Dig; 2003. pp. 32.5.1–32.5.4.

  11. Fan K-Yi, Yamashita S, Huang J-D. Reactant minimization for multi-target sample preparation on digital microfluidic biochips using network flow models, VLSI-DAT; 2019. pp. 1–4.

  12. Hsieh YL, Ho TY, Chakrabarty K. On-chip biochemical sample preparation using digital microfluidics. In: IEEE biomedical circuits and systems conference (BioCAS); 2011. pp. 297–300.

  13. Hsieh YL, Ho TY, Chakrabarty K. Design methodology for sample preparation on digital microfluidic biochips. In: IEEE 30th international conference on computer design (ICCD); 2012. pp. 189–94.

  14. Hsieh Y-L, Ho T-Y, Chakrabarty K. Biochip synthesis and dynamic error recovery for sample preparation using digital microfluidics. IEEE Trans CAD. 2014;33(2):183–96.

    Article  Google Scholar 

  15. Huang J, Liu C-H, Chiang T-W. Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees. In: Proc of IEEE/ACM ICCAD; 2012. pp. 377–84.

  16. Mohamed I, Krishnendu C. Error recovery in digital microfluidics for personalized medicine. In: Proc of DATE; 2015. pp. 247–52.

  17. Li Z, et al. Efficient and adaptive error recovery in a micro-electrode-dot-array digital microfluidic biochip. IEEE Trans CAD. 2018;37(3):601–14.

    Article  Google Scholar 

  18. Luo Y, Chakrabarty K, Ho T-Y. Error recovery in cyberphysical digital microfluidic biochips. IEEE Trans CAD. 2013;32(1):59–72.

    Article  Google Scholar 

  19. Luo Y, Chakrabarty K, Ho T-Y. Real-time error recovery in cyberphysical digital-microfluidic biochips using a compact dictionary. IEEE Trans CAD. 2013;32(12):1839–52.

    Article  Google Scholar 

  20. Paik P, Pamula VK, Pollack MG, Fair RB. Electrowetting-based droplet mixers for microfluidic systems. Lab Chip. 2003;3(1):28–33.

    Article  Google Scholar 

  21. Poddar S, Ghoshal S, Chakrabarty K, Bhattacharya BB. Error correcting sample preparation with cyberphysical digital microfluidic lab-on-chip. In: ACM Trans. Des. Autom. Electron. Syst, 22, 1; 2016. pp. 2:1–2:29.

  22. Poddar S, Wille R, Rahaman H, Bhattacharya BB. Error-oblivious sample preparation with digital microfluidic lab-on-chip. In: IEEE transactions on computer-aided design of integrated circuits and systems; 2018. p. 14.

  23. Poddar S, Wille R, Rahaman H, Bhattacharya BB. dilution with digital microfluidic biochips: how unbalanced splits corrupt target-concentration. 2019. https://doi.org/10.13140/RG.2.2.35616.02562.

  24. Pollack MG, Shenderov AD, Fair RB. Electrowetting-based actuation of droplets for integrated microfluidics. Lab Chip. 2002;2(1):96–101.

    Article  Google Scholar 

  25. Ren H, Srinivasan V, Fair RB. Design and testing of an interpolating mixing architecture for electro wetting-based droplet-on-chip chemical dilution. In: Proc. Actuators Microsyst (Tranducers): Int. Conf. Solid-state Sensors; 2003. p. 619–22.

  26. Roy S, Bhattacharya BB, Chakrabarty K. Optimization of dilution and mixing of biochemical samples using digital microfluidic biochips. In: IEEE Trans. on CAD of integrated circuits and systems vol. 29; 2010. pp. 1696–708.

  27. Roy S, Bhattacharya BB, Chakrabarty K. Waste-aware dilution and mixing of biochemical samples with digital microfluidic biochips. In: Proceedings of the DATE; 2011. pp. 1059–64.

  28. Shao L, Yang Y, Yao H, Ho TY, Cai Y. LUTOSAP: lookup table based online sample preparation in microfluidic biochips. In: Proceedings of the on great lakes symposium on VLSI (GLSVLSI); 2017. pp. 447–50.

  29. Shao L, Li W, Ho T-Y, Roy S, Yao H. Lookup table based fast reliability-aware sample preparation using digital microfluidic biochips. IEEE Trans Comput-Aided Design Integr Circ (TCAD). 2020;39(10):2708–21. https://doi.org/10.1109/TCAD.2019.2948907.

    Article  Google Scholar 

  30. Srinivasan V, Pamula VK, Pollack MG, Fair RB. Clinical diagnostics on human whole blood, plasma, serum, urine, saliva, sweat, and tears on a digital microfluidic platform. In: Proceedings of MicroTAS; 2003. pp. 1287–90.

  31. Thies W, Srinivasan V, Urbanski JP, Thorsen T, Amarasinghe SP. Abstraction layers for scalable microfluidic biocomputing. Natural Comput. 2008;7(2):255–75.

    Article  MathSciNet  Google Scholar 

  32. Yadav A, Dinh TA, Kitagawa D, Yamashita S. ILP-based synthesis for sample preparation applications on digital microfluidic biochips. In: VLSI Design; 2016. pp. 355–60.

  33. Zipeng L, Yi-Tse LK, Chakrabarty K, Ho T-Y, Lee C-Y. Droplet size-aware and error-correcting sample preparation using micro-electrode-dot-array digital microfluidic biochips. IEEE Trans BioCAS. 2017;11(6):1380–91.

    Google Scholar 

  34. Zhao Y, Chakrabarty K. Cross-contamination avoidance for droplet routing in digital microfluidic biochips. IEEE Trans CAD. 2012;31(6):817–30.

    Article  Google Scholar 

  35. Zhao Y, Xu T, Chakrabarty K. Integrated control-path design and error recovery in the synthesis of digital microfluidic lab-on-chip. In: ACM JETC 6, vol. 3; 2010. pp. 11:1–11:28.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, West Bengal, 700 107, India

    Nilina Bera & Subhashis Majumder

  2. Yahoo! Japan, Tokyo, Japan

    Sutirtha Das

  3. PwC, Bangalore, Karnataka, India

    Sweta Mukherjee

  4. SolCen Technology Pvt. Ltd, Bangalore, Karnataka, India

    Neha Aryani

  5. Indian Institute of Technology Kharagpur, Kharagpur, India

    Bhargab B. Bhattacharya

Authors
  1. Nilina Bera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhashis Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sutirtha Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sweta Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Neha Aryani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bhargab B. Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nilina Bera.

Ethics declarations

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A preliminary version of this paper appeared in Proc. DISCOVER, Page No. \(1-6\) (2016) [8].

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bera, N., Majumder, S., Das, S. et al. SIMOP: A SIMulation-Guided OPtimization Mechanism for Sample Preparation with Digital Microfluidic Biochip. SN COMPUT. SCI. 2, 84 (2021). https://doi.org/10.1007/s42979-021-00506-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00506-x

Keywords

Search

Navigation