No-Boundary Thinking in Bioinformatics Research

Currently there are definitions from many agencies and research societies defining bioinformatics as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT)

The Foundational Model of Anatomy Ontology

Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data

Determination of Nonlinear Genetic Architecture using Compressed Sensing

We introduce a statistical method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. The computational and data resource requirements are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. Our method uses a generalization of compressed sensing (L1-penalized regression) applied to nonlinear functions of the sensing matrix. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using both real and simulated human genomes.Comment: 20 pages, 8 figures. arXiv admin note: text overlap with arXiv:1408.342

Agile Requirements Engineering: A systematic literature review

Nowadays, Agile Software Development (ASD) is used to cope with increasing complexity in system development. Hybrid development models, with the integration of User-Centered Design (UCD), are applied with the aim to deliver competitive products with a suitable User Experience (UX). Therefore, stakeholder and user involvement during Requirements Engineering (RE) are essential in order to establish a collaborative environment with constant feedback loops. The aim of this study is to capture the current state of the art of the literature related to Agile RE with focus on stakeholder and user involvement. In particular, we investigate what approaches exist to involve stakeholder in the process, which methodologies are commonly used to present the user perspective and how requirements management is been carried out. We conduct a Systematic Literature Review (SLR) with an extensive quality assessment of the included studies. We identified 27 relevant papers. After analyzing them in detail, we derive deep insights to the following aspects of Agile RE: stakeholder and user involvement, data gathering, user perspective, integrated methodologies, shared understanding, artifacts, documentation and Non-Functional Requirements (NFR). Agile RE is a complex research field with cross-functional influences. This study will contribute to the software development body of knowledge by assessing the involvement of stakeholder and user in Agile RE, providing methodologies that make ASD more human-centric and giving an overview of requirements management in ASD.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Towards technological rules for designing innovation networks: a dynamic capabilities view.

Inter-organizational innovation networks provide opportunities to exploit complementary resources that reside beyond the boundary of the firm. The shifting locus of innovation and value creation away from the “sole firm as innovator” poses important questions about the nature of these resources and the capabilities needed to leverage them for competitive advantage. The purpose of this paper is to describe research into producing design-oriented knowledge, for configuring inter-organizational networks as a means of accessing such resources for innovation

No-Boundary Thinking in Bioinformatics

The following sections are included:Bioinformatics is a Mature DisciplineThe Golden Era of Bioinformatics Has BegunNo-Boundary Thinking in BioinformaticsReference

Team Building Without Boundaries

Team building can be challenging when participants are from the same discipline or sub-discipline, but needs special attention when participants use a different vocabulary and have different cultural views on what constitutes viable problems and solutions. Essential to No Boundary Thinking (NBT) teams is proper formulation of the problem to be solved, and a basic tenant is that the NBT team must come together with diverse perspectives to decide the problem before solutions can be considered. Given that participants come with different views on problem formulation and solution, it is important to consider a robust process for team formation and maintenance. This takes extra effort and time, but scholars studying teams of experts with diverse training have found that they are better positioned to be successful in solving even deep and difficult problems especially if they have learned to work well with each other. At this workshop we will discuss principles that scholars who have worked in NBT teams have discovered as effective. We will then engage with the workshop participants to consider discuss these principles and brainstorm to consider other approaches

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex