计算机科学与系统生物学杂志

The objective of the present article is a) to explain humans’ high creativity in non-mystic and unambiguous terms, b) to evaluate the performance of problem-solving computer programs and c) to make suggestions about future designs of heuristics. Unlike many previous attempts in the past century, we sought inspiration from two sources that had been neglected or excluded from considerations by experts: artificial intelligence and introspections of a number of highly creative individuals, who confessed that they had a penchant for visual thinking. Simonton’s chance-configuration model was refurbished accordingly. It is now possible for the refurbished model to explain a number of outstanding puzzles that had eluded our predecessors: a) what intuition is, b) why creators had no idea about their source of inspiration even after the fact, c) a peculiar event happening at the discovery time, known as the “aha” phenomenon, d) a type of accidental discoveries known as serendipity. Moreover, the elusive concept of abduction advanced by philosopher Charles Peirce is actually visual thinking in disguise. Blessed with this new understanding, we could evaluate the performance of a number of problem-solving computer programs from a cognitive point of view. It turned out that the common thread that links human creativity and computer-based creative problem solving is heuristic searching. Recognizing that a digital computer must perform heuristic searching in a digital environment, which is not the most user-friendly environment to do so, we made suggestions about how to circumvent the restrictions without sacrificing the principles in future designs of heuristics.

Cells communication is absolutely essential for multi-cellular organisms, but what if a cell fails to send out a signal at the proper time? Or what if a signal doesn’t reach its target? What if a target cell does not respond to a signal or a cell responds even though it has not received a signal? These cellular phenomena can lead to serious metabolic alterations, but of course there are molecules that block these errors and prevent a catastrophe. Apoptosis, a form of programmed cell death, is a genetically regulated cell-suicide mechanism that is essential for our well-being. In this process, cells acquire the means of their own destruction in the form of an arsenal of deadly proteins, which they turn upon themselves. There are different pathways pro-apoptotic and pro-survival that crosstalk. In this work, we simulated the incremental integration of pro-apoptotic and pro-survival signalling pathways in a tuple spacebased bioinformatics platform, which provides a robust working environment for in silico experimentation, allowing us to work both separately and together on these pathways, including/removing deadly or regulatory proteins, and observing the consequences of such changes for the overall system behaviour.

Alzheimer’s disease (AD) is a complex disease where the analysis of gene expression patterns relies on computational techniques to understand the cause and progression of the disease. Evidence postulates that the complexity of AD stems from the overlap of early-stage markers with normal aging. Furthermore, there is increasing evidence suggesting that gene co-regulation in AD plays a vital role in the progression of the disease. The aim of this work is to identify and track co-regulated genes from incipient to severe cases of AD i.e. samples that exhibited progression of AD. We hypothesize that co-expressed genes associated with two markers of AD (the cognitive marker-mini mental state examination (MMSE) and the pathological marker Neurofibrillary Tangles (NFT)), are conserved across AD progression. For our analysis we used the Blalock dataset and the prominent tool weighted correlation network analysis (WGCNA). Through our analysis we observed that genes GNA11 and MAP2K2 were consistently ranked through the progression of AD. The functional analysis of the identified co-regulated genes at the incipient stages of AD includes RNA and cofactor binding. Through this exploratory study we conclude that from incipient to severe stages of AD the gamut of co-regulated genes vary rather than being conserved across disease severity.

Mammalian spermatozoa gain their fertilizing ability only after they reside within the female genital tract, where important physical-chemical modifications, the “capacitation”, occur: the cytoskeleton reorganizes, the membranes become more instable and tend to fuse each other, the protein phosphorylation pattern changes, a new motility pattern, the hyperactivated motility, appears. These events are regulated by several signal transduction pathways, whose failure could have negative implication for fertility. Unfortunately, frequently it is impossible to issue a diagnosis, as in the case of “idiopathic infertility”. In our opinion, this inability could be not due to the scarcity of molecular data, but to the difficulty to manage them. Indeed, spermatozoa (like other cells) are constituted by heterogeneous components that interact collectively and nonlinearly, giving rise to a complex behavior in which the whole system is more than the sum of its single components. To overcome this problem, we adopted a holistic approach based on computational modeling. We represented the events occurring during human spermatozoa capacitation as a biological network of nodes (the molecules) and links (the interactions). Topological analyses of network showed that it has a scale free topology and that it is characterized by a high signaling efficiency and robustness against random failure. Interestingly, we found that the same topology is shared with other organisms, sea urchin and Caenorhabditis elegans, belonging to different Phyla and characterized by very different reproductive ecology. Further, we modelized boar sperm capacitation, separating the molecules depending on their subcellular compartment: model analysis suggested that actin cytoskeleton is not only a mechanical support, but it could participate to the coordination of the capacitation-related events. This hypothesis was, then, successfully validated in an in vitro experiment. In conclusion, from computational models it is possible to infer important information not otherwise obtainable, then improving the understanding of the spermatozoa biology complexity.

SAM/BAM alignment file formats are extensively used in virtually all the laboratories devoted to high-throughput sequencing. However, limited effort has been yet dedicated to the development of SAM/BAM quality reporting tools. To overcome this problem, we developed SAM-Profiler, a multiplatform tool dedicated to the advanced quality reporting of SAM/BAM files. SAM-Profiler performs qualitative analysis of SAM/BAM alignment data in the context of next-generation sequencing. It is implemented in C# and can be run under Windows, Linux and MacOS operative systems. Two versions are available: fully graphical, event-driven software and a command-line tool. SAM-Profiler is able to generate an extensive set of qualitative reports on SAM/BAM alignment data, among them: overall, per-base and per-chromosome read quality, mapping quality, duplicate and coverage analyses, bases distribution, perfect, proper and improper mapping, exonic, intronic, intergenic, 5` and 3` UTR coverage, mismatch distribution profile and CG distribution. In presence of paired-end sequencing experiments our tool is able to automatically report the insert size distribution and to analyze the relative pair mapping, reporting absolute and relative distribution of properly, improperly mapped, mapped/unmapped and unmapped pairs. Its modular architecture allows embedding additional analytical monitoring/reporting tools to the already developed list, allowing SAM-Profiler to grow according to the specific requests of the end-users.

RNA-protein interactions are important in a wide variety of cellular and developmental processes. Recently, high-throughput experiments have begun to provide valuable information about RNA partners and binding sites for many RNA-binding proteins (RBPs), but these experiments are expensive and time consuming. Thus, computational methods for predicting RNA-Protein interactions (RPIs) can be valuable tools for identifying potential interaction partners of a given protein or RNA, and for identifying likely interfacial residues in RNA-protein complexes. This review focuses on the “partner prediction” problem and summarizes available computational methods, web servers and databases that are devoted to it. New computational tools for addressing the related “interface prediction” problem are also discussed. Together, these computational methods for investigating RNA-protein interactions provide the basis for new strategies for integrating RNA-protein interactions into existing genetic and developmental regulatory networks, an important goal of future research.