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Microfluidic biochips have recently emerged with significant promise and versatility in automating a variety of biochemical protocols on a tiny chip. Sample preparation, which involves the mixing of fluids with a specified target ratio in the minuscule scale, is an essential component of these protocols. Algorithms that optimize on-chip sample-preparation cost and time are closely intertwined with the underlying mixing model, mixing sequence, and fluidic architecture. Although numerous mixing models have been studied in the literature, their impact on the dynamics of mixing steps is hitherto not fully understood. In this article, we show that various mixing models can be envisaged in the light of prime factorization of integers thus establishing a connection among mixing algorithms, chip architectures, and performance. This insight has led to the development of the proposed factorization-based dilution algorithm (FacDA) considering a generalized mixing model suitable for micro-electrode-dot-array (MEDA) biochips. It further leads to target volume oriented dilution algorithm (TVODA) to cater to user’s demand for an output with a given volume. We formulate the optimization problem on the fabric of the satisfiability modulo theory (SMT) while determining mixing sequences. Simulation results on a large number of test-cases reveal that FacDA and TVODA outperform the state-of-the-art dilution algorithms for MEDA biochips with respect to reactant cost, mixing time, and waste production. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic-search algorithm called Memory-bounded Anytime Window \A^\ast\ (\MAWA^\ast), which is complete, anytime, and memory bounded. \MAWA^\ast uses the window-bounded anytime-search methodology of \AWA^\ast as the basic framework and combines it with the memory-bounded \A^\ast-like approach to handle restricted memory situations. Simple and efficient versions of \MAWA^\ast targeted for tree search have also been presented. Experimental results of the sliding-tile puzzle problem and the traveling-salesman problem show the significant advantages of the proposed algorithm over existing methods. [ABSTRACT FROM AUTHOR]

• Detection of HMWP by SEC-MS. • Online sequencing of highly charged peptide by ETD MS/MS. • Easy tool for assessment of biosimilarity. High molecular weight protein aggregates present in a recombinant human insulin and analogs are conventionally quantified by SEC-HPLC and identified by SEC-MALS as oligomeric population which lacks precise identification of species. The limitation of these two techniques is vanquished though simultaneous separation and identification by SEC coupled with MS. The identification was established with organic solvent based isocratic elution followed by MS for parallel separation and identification of HMWP species. The developed SEC-MS method is validated to establish the method capability and variability. Further investigations under stress conditions of Insulin analogues revealed the capability of the method to separate higher order oligomeric (Trimeric, and Tetrameric) species alongside covalent dimeric species. Additionally, the method holds good in separating and sequencing protamine peptides used in suspension (Neutral Protamine Hagedorn) and biphasic/mixed (70/30) formulations of Human insulin using ETD-MSMS. The data presented here shows insight towards utilization of state-of-the-art SEC-MS technique in the biopharmaceutical field as a tool to establish the structural comparability of higher order species in biosimilars and to investigate the lot to lot batch variability for protamine sulphate in-terms of sequence confirmation. [ABSTRACT FROM AUTHOR]