Continuous-Flow Microfluidic Devices (CFMDs), also known as biochips, provide automated and cost-effective solutions for biomolecular analysis in life science applications. Multi-operation biochemical reactions require 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 undesired flow risk. To overcome these challenges, we develop Vespa, an open-source validation framework that uses logic expressions to describe operation constraints with lower human labor costs. 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 an undesired flow risk mitigation mechanism, issuing warnings for incorrect constraints and potential fluid undesired flow 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 create case studies using a real-world CFMD. We demonstrate that Vespa eliminates over 90% of DOCC-related wet lab tests by detecting issues and making updates before fabrication.

Summary: Each cell is a phenotypically unique individual that is influenced by internal and external processes, operating in parallel. To characterize the dynamics of cellular processes one needs to observe many individual cells from multiple points of view and over time, so as to identify commonalities and variability. With this aim, we engineered a software, 'SCIP', to analyze multi-modal, multi-process, time-lapse microscopy morphological and functional images. SCIP is capable of automatic and/or manually corrected segmentation of cells and lineages, automatic alignment of different microscopy channels, as well as detect, count and characterize fluorescent spots (such as RNA tagged by MS2-GFP), nucleoids, Z rings, Min system, inclusion bodies, undefined structures, etc. The results can be exported into *mat files and all results can be jointly analyzed, to allow studying not only each feature and process individually, but also find potential relationships. While we exemplify its use on Escherichia coli, many of its functionalities are expected to be of use in analyzing other prokaryotes and eukaryotic cells as well. We expect SCIP to facilitate the finding of relationships between cellular processes, from small-scale (e.g. gene expression) to large-scale (e.g. cell division), in single cells and cell lineages. Availability and implementation: http://www.ca3-uninova.org/project_scip. Supplementary information: Supplementary data are available at Bioinformatics online.

Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.