Continuous-Flow Microfluidic Devices (CFMDs), also known as biochips, provide automated and cost-effective solutions for biomolecular analysis in life science applications. Multi-step biochemical reactions have required incorporating microfluidic components into CFMDs, which may raise their design complexity and lead to Design-Objective-Constraint Compatibility (DOCC) conflicts. Existing validation methods excel in optimizing Control-Layer Pressurization Protocol (CLPP) for small networks. However, they face challenges in describing operation constraints for microfluidic large-scale integration (mLSI) experiments and addressing the leakage risk. For that, we developed Vespa, an open-source validation framework using logic expressions to describe operation constraints on a lower human labor cost. For each operation, taking an objective list, a constraint list, a design netlist, and a super-parameter as inputs, Vespa validates the DOCC by constructing a target fluid transportation path. In addition, it introduces a leakage risk mitigation mechanism, issuing warnings for incorrect constraints and potential fluid leakage risks. The work includes over 900 real-world and synthetic benchmarks in three complexity ranges to support researchers in the field. As a result, more than 85% of benchmark experiments yield correct results within 0.3 seconds, enabling Vespa for real-time validation and integration into interactive CFMD design tools. Finally, to show Vespa's practical efficacy and real-world impact, we created case studies using a real-world CFMD. We demonstrated that Vespa eliminates over 90% of DOCC-related wet lab tests by detecting issues and making updates before fabrication.
Download the repository from GitHub, create a venv environment, and install the necessary packages. Here we use python3.8 as the python interpreter.
$ git clone https://github.com/zyrrron/VeSpA-Algorithm.git
$ cd Vespa-Algorithm
$ python3 -m venv venv/
$ venv/bin/pip3 install -r requirements.txtAfter you install all the packages, you are able to run the algorithm.
You can run this algorithm to test the given random benchmarks with this command:
$ venv/bin/python3 TestAlgorithm.pyIf you want to generate random benchmarks by your self, you can use this command to generate random designs:
$ venv/bin/python3 RandomCaseGenerator.pyIf it is the first time to test the random benchmarks, this command can automatically generate random Constraints by calling ConstraintMaker.py and save it for the next time:
$ venv/bin/python3 TestAlgorithm.pyAll Experiment objectives for Random Constraints are set to ['F1', 'F2']. User can edit it in TestAlgorithm.py.
You can run this algorithm to test the given literature review benchmarks with this command:
$ venv/bin/python3 lrbtest.pyYou can also create your own biochip design by editing LRB_new.py. And Create Constraint and Experiment objectives in Constraint_UR_lrb_new.csv.
Feel free to dive in! Open an issue or submit PRs.
This project exists thanks to all the people who contribute.