Microfluidic applications range from combinatorial chemical synthesis to high-throughput screening, with platforms integrating analog perfusion components, digitally controlled microvalves, and a range of sensors that demand a variety of communication protocols. A comprehensive solution for microfluidic control has to support an arbitrary combination of microfluidic components and to meet the demand for easy-to-operate system as it arises from the growing community of unspecialized microfluidics users. It should also be an easy to modify and extendable platform, which offer an adequate computational resources, preferably without a need for a local computer terminal for increased mobility. Here we will describe several implementation of microfluidics control technologies and propose a microprocessor-based unit that unifies them. Integrated control can streamline the generation process of complex perfusion sequences required for sensor-integrated microfluidic platforms that demand iterative operation procedures such as calibration, sensing, data acquisition, and decision making. It also enables the implementation of intricate optimization protocols, which often require significant computational resources. System integration is an imperative developmental milestone for the field of microfluidics, both in terms of the scalability of increasingly complex platforms that still lack standardization, and the incorporation and adoption of emerging technologies in biomedical research. Here we describe a modular integration and synchronization of a complex multicomponent microfluidic platform.
Part of the book: Advances in Microfluidics
Advancements in integrated neuroscience are often characterized with data-driven approaches for discovery; these progressions are the result of continuous efforts aimed at developing integrated frameworks for the investigation of neuronal dynamics at increasing resolution and in varying scales. Since insights from integrated neuronal models frequently rely on both experimental and computational approaches, simulations and data modeling have inimitable roles. Moreover, data sharing across the neuroscientific community has become an essential component of data-driven approaches to neuroscience as is evident from the number and scale of ongoing national and multinational projects, engaging scientists from diverse branches of knowledge. In this heterogeneous environment, the need to share neuroscientific data as well as to utilize it across different simulation environments drove the momentum for standardizing data models for neuronal morphologies, biophysical properties, and connectivity schemes. Here, I review existing data models in neuroinformatics, ranging from flat to hybrid object-hierarchical approaches, and suggest a framework with which these models can be linked to experimental data, as well as to established records from existing databases. Linking neuronal models and experimental results with data on relevant articles, genes, proteins, disease, etc., might open a new dimension for data-driven neuroscience.
Part of the book: Bioinformatics in the Era of Post Genomics and Big Data