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PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular structure (NORS) and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions, disulfide-bonds, sub-cellular localization and functional annotations. Upon request fold recognition by prediction-based threading, CHOP domain assignments, predictions of transmembrane strands and inter-residue contacts are also available. For all services, users can submit their query either by electronic mail or interactively via the World Wide Web.

Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic responses. Although substantial effort has been made to define the genomic constitution of cancer cell line panels, the transcriptome remains understudied. Here we describe RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines. We report comprehensive analyses of transcriptome features including gene expression, mutations, gene fusions and expression of non-human sequences. Of the 2,200 gene fusions catalogued, 1,435 consist of genes not previously found in fusions, providing many leads for further investigation. We combine multiple genome and transcriptome features in a pathway-based approach to enhance prediction of response to targeted therapeutics. Our results provide a valuable resource for studies that use cancer cell lines.

BackgroundMicrofluidics is a science and technology that precisely manipulates and processes microscale fluids. It is commonly used to precisely control microfluidic (10 −9 to 10 −18 L) fluids using channels that range in size from tens to hundreds of microns and is known as a "lab-on-a-chip" [1][2][3][4]. The microchannel is small, but has a large surface area and high mass transfer, favoring its use in microfluidic technology applications including low regent usage, controllable volumes, fast mixing speeds, rapid responses, and precision control of physical and chemical properties [1,5,6]. Microfluidics integrate sample preparation, reactions, separation, detection, and basic operating units such as cell culture, sorting and cell lysis [7]. For these reasons, interest in OOAC has intensified [8]. OOAC combines a range of chemical, biological and material science disciplines [9] and was selected as one of the "Top Ten Emerging Technologies" in the World Economic Forum [10].OOAC is a biomimetic system that can mimic the environment of a physiological organ, with the ability to regulate key parameters including AbstractThe organ-on-a-chip (OOAC) is in the list of top 10 emerging technologies and refers to a physiological organ biomimetic system built on a microfluidic chip. Through a combination of cell biology, engineering, and biomaterial technology, the microenvironment of the chip simulates that of the organ in terms of tissue interfaces and mechanical stimulation. This reflects the structural and functional characteristics of human tissue and can predict response to an array of stimuli including drug responses and environmental effects. OOAC has broad applications in precision medicine and biological defense strategies. Here, we introduce the concepts of OOAC and review its application to the construction of physiological models, drug development, and toxicology from the perspective of different organs. We further discuss existing challenges and provide future perspectives for its application.

In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed-loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady-state-unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunovbased techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed-loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady-state. In this operation mode, suitable Lyapunov-based constraints are incorporated in the economic MPC design to guarantee that the closed-loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed-loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations.