From Scrum to Agile: A Journey to Tackle the Challenges of Distributed Development in an Agile Team

Background: Agile and distributed software development are two trends that continue to increase rapidly in today's software industry. Even though the benefits achievable by combining them are potentially many, the intrinsic challenges of such marriage often lead to severe complications that can jeopardize the successful completion of software projects. Method: To investigate empirically how these two trends can coexist without compromising on the agile core values and principles, we conducted an exploratory holistic case study. Focusing on the development team of a Danish SME having both distributed offices as well as teleworking arrangements, we showcase (the evolution of) their practices. Results: The case is an example of the effective application of the agile reflective culture that allowed the company to evolve to a level in which the collocation restrictions of agile software development are overcome by a continuously evolving software process geared towards reducing waste to achieve speed and simplicity. Conclusions: Even though results need to be considered carefully due to the single nature of the reported case, we highlight five elements that have been fundamental in such journey: agile servant-leader, agile team, trust, virtual work environment, inspect & adapt, and reduce waste. Extensive information is provided to frame the context and to allow meaningful future comparisons

Virtual by Design: How a Work Environment Can Support Agile Distributed Software Development

Even though agile methods have been flourishing in the last decades,their implementation in (globally) distributed arrangements stillpresents hard challenges. Due to this tension, practices are eithermodified or added to compensate with the additional control requiredby the setup. In this paper, we present a case study that, byembracing the characteristics of distributed development, managedto incrementally design a process that does not compromise thefoundations of the agile philosophy. We show how a virtual workenvironment has been crafted by continuously improving practicesand carefully selecting technologies to allow each team member ofthe development team to fully participate regardless of the actualphysical location. Aware of the single nature limitation of the reportedcase, we present extensive information to frame the contextallowing meaningful comparisons by researchers and providingconcrete examples for practitioners

CRMAGE: CRISPR Optimized MAGE Recombineering

A bottleneck in metabolic engineering and systems biology approaches is the lack of efficient genome engineering technologies. Here, we combine CRISPR/Cas9 and λ Red recombineering based MAGE technology (CRMAGE) to create a highly efficient and fast method for genome engineering of Escherichia coli. Using CRMAGE, the recombineering efficiency was between 96.5% and 99.7% for gene recoding of three genomic targets, compared to between 0.68% and 5.4% using traditional recombineering. For modulation of protein synthesis (small insertion/RBS substitution) the efficiency was increased from 6% to 70%. CRMAGE can be multiplexed and enables introduction of at least two mutations in a single round of recombineering with similar efficiencies. PAM-independent loci were targeted using degenerate codons, thereby making it possible to modify any site in the genome. CRMAGE is based on two plasmids that are assembled by a USER-cloning approach enabling quick and cost efficient gRNA replacement. CRMAGE furthermore utilizes CRISPR/Cas9 for efficient plasmid curing, thereby enabling multiple engineering rounds per day. To facilitate the design process, a web-based tool was developed to predict both the λ Red oligos and the gRNAs. The CRMAGE platform enables highly efficient and fast genome editing and may open up promising prospective for automation of genome-scale engineering

Network reconstruction of the mouse secretory pathway applied on CHO cell transcriptome data

BACKGROUND: Protein secretion is one of the most important processes in eukaryotes. It is based on a highly complex machinery involving numerous proteins in several cellular compartments. The elucidation of the cell biology of the secretory machinery is of great importance, as it drives protein expression for biopharmaceutical industry, a 140 billion USD global market. However, the complexity of secretory process is difficult to describe using a simple reductionist approach, and therefore a promising avenue is to employ the tools of systems biology. RESULTS: On the basis of manual curation of the literature on the yeast, human, and mouse secretory pathway, we have compiled a comprehensive catalogue of characterized proteins with functional annotation and their interconnectivity. Thus we have established the most elaborate reconstruction (RECON) of the functional secretion pathway network to date, counting 801 different components in mouse. By employing our mouse RECON to the CHO-K1 genome in a comparative genomic approach, we could reconstruct the protein secretory pathway of CHO cells counting 764 CHO components. This RECON furthermore facilitated the development of three alternative methods to study protein secretion through graphical visualizations of omics data. We have demonstrated the use of these methods to identify potential new and known targets for engineering improved growth and IgG production, as well as the general observation that CHO cells seem to have less strict transcriptional regulation of protein secretion than healthy mouse cells. CONCLUSIONS: The RECON of the secretory pathway represents a strong tool for interpretation of data related to protein secretion as illustrated with transcriptomic data of Chinese Hamster Ovary (CHO) cells, the main platform for mammalian protein production. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-017-0414-4) contains supplementary material, which is available to authorized users

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

AbstractDeficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.</jats:p

Glucagon-like peptide-1 analogs against antipsychotic-induced weight gain: potential physiological benefits

BACKGROUND: Antipsychotic-induced weight gain constitutes a major unresolved clinical problem which may ultimately be associated with reducing life expectancy by 25 years. Overweight is associated with brain deterioration, cognitive decline and poor quality of life, factors which are already compromised in normal weight patients with schizophrenia. Here we outline the current strategies against antipsychotic-induced weight gain, and we describe peripheral and cerebral effects of the gut hormone glucagon-like peptide-1 (GLP-1). Moreover, we account for similarities in brain changes between schizophrenia and overweight patients. DISCUSSION: Current interventions against antipsychotic-induced weight gain do not facilitate a substantial and lasting weight loss. GLP-1 analogs used in the treatment of type 2 diabetes are associated with significant and sustained weight loss in overweight patients. Potential effects of treating schizophrenia patients with antipsychotic-induced weight gain with GLP-1 analogs are discussed. CONCLUSIONS: We propose that adjunctive treatment with GLP-1 analogs may constitute a new avenue to treat and prevent metabolic and cerebral deficiencies in schizophrenia patients with antipsychotic-induced weight gain. Clinical research to support this idea is highly warranted

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Recombinant protein production coopts the host cell machinery to provide high protein yields of industrial enzymes or biotherapeutics. However, since protein translation is energetically expensive and tightly controlled, it is unclear if highly expressed recombinant genes are translated as efficiently as host genes. Furthermore, it is unclear how the high expression impacts global translation. Here, we present the first genome-wide view of protein translation in an IgG-producing CHO cell line, measured with ribosome profiling. Through this we found that our recombinant mRNAs were translated as efficiently as the host cell transcriptome, and sequestered up to 15% of the total ribosome occupancy. During cell culture, changes in recombinant mRNA translation were consistent with changes in transcription, demonstrating that transcript levels influence specific productivity. Using this information, we identified the unnecessary resistance marker NeoR to be a highly transcribed and translated gene. Through siRNA knock-down of NeoR, we improved the production- and growth capacity of the host cell. Thus, ribosomal profiling provides valuable insights into translation in CHO cells and can guide efforts to enhance protein production