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The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a "sweet spot" in the taxonomy—a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the "inverse Conway maneuver" to shape a research team so as to allow dedicated software engineers to interface with researchers working in this "sweet spot." We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity. [ABSTRACT FROM AUTHOR]

This essay builds on the literatures on ‘biocapitalism’ and ‘informationalism’ (or ‘informational capitalism’) to develop the concept of ‘bio-informational capitalism’ in order to articulate an emergent form of capitalism that is self-renewing in the sense that it can change and renew the material basis for life and capital as well as program itself. Bio-informational capitalism applies and develops aspects of the new biology to informatics to create new organic forms of computing and self-reproducing memory that in turn has become the basis of bioinformatics. The paper begins with a review of the successes of the ‘new biology’, focusing on Craig Venter’s digitizing of biology and, as he remarks, the creation of new life from the digital universe. The paper then provides a brief account of bioinformatics before brokering and discussing the term ‘bioinformational capitalism’. [ABSTRACT FROM PUBLISHER]

We recently reviewed the current progress in the use of high-throughput molecular "omics" data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed "Pathway Instability" and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator (FloWPS), has the potential to significantly enhance the value of pathway activation profiles as biomarkers of cancer response to treatments. It reduces the minimum required number of training samples needed to construct a machine-learning-based classifier. Finally, several documented clinical cases have been recently published, in which gene-expression-based pathway analysis was successfully used for personalized off-label prescription of target drugs to metastatic cancer patients. [ABSTRACT FROM AUTHOR]