What Do We Teach to Engineering Students: Embedded Ethics, Morality, and Politics.

In the past few years, calls for integrating ethics modules in engineering curricula have multiplied. Despite this positive trend, a number of issues with these 'embedded' programs remains. First, learning goals are underspecified. A second limitation is the conflation of different dimensions under the same banner, in particular confusion between ethics curricula geared towards addressing the ethics of individual conduct and curricula geared towards addressing ethics at the societal level. In this article, we propose a tripartite framework to overcome these difficulties. Our framework analytically decomposes an ethics module into three dimensions. First, there is the ethical dimension, which pertains to the learning goals. Second, there is the moral dimension, which addresses the moral relevance of engineers' conduct. Finally, there is the political dimension, which scales up issues of moral relevance at the civic level. All in all, our framework has two advantages. First, it provides analytic clarity, i.e. it enables course instructors to locate ethical dilemmas in either the moral or political realm and to make use of the tools and resources from moral and/or political philosophy. Second, it depicts a comprehensive ethical training, which enables students to both reason about moral issues in the abstract, and to socially contextualize potential solutions.

Co-emergence and Collapse: The Mesoscopic Approach for Conceptualizing and Investigating the Functional Integration of Organisms.

The fall of reductionist approaches to explanation leaves biology with an unescapable challenge: how to decipher complex systems. This entails a number of very critical questions, the most basic ones being: "What do we mean by 'complex'?" and "What is the system we should look for?" In complex systems, constraints belong to a higher level that the molecular one and their effect reduces and constrains the manifold of the accessible internal states of the system itself. Function is related but not deterministically imposed by the underlying structure. It is quite unlikely that such kind of complexity could be grasped by current approaches focusing on a single organization scale. The natural co-emergence of systems, parts and properties can be adopted as a hypothesis-free conceptual framework to understand functional integration of organisms, including their hierarchical or multilevel patterns, and including the way scientific practice proceeds in approaching such complexity. External, "driving" factors - order parameters and control parameters provided by the surrounding microenvironment - are always required to "push" the components' fate into well-defined developmental directions. In the negative, we see that in pathological processes such as cancer, organizational fluidity, collapse of levels and dynamic heterogeneity make it hard to even find a level of observation for a stable explanandum to persist in scientific practice. Parts and the system both lose their properties once the system is destabilized. The mesoscopic approach is our proposal to conceptualizing, investigating and explaining in biology. "Mesoscopic way of thinking" is increasingly popular in the epistemology of biology and corresponds to looking for an explanation (and possibly a prediction) where "non-trivial determinism is maximal": the "most microscopic" level of organization is not necessarily the place where "the most relevant facts do happen." A fundamental re-thinking of the concept of causality is also due for order parameters to be carefully and correctly identified. In the biological realm, entities have relational properties only, as they depend ontologically on the context they happen to be in. The basic idea of a relational ontology is that, in our inventory of the world, relations are somehow prior to the relata (i.e., entities).

Conceptual Challenges in the Theoretical Foundations of Systems Biology.

In the last decade, Systems Biology has emerged as a conceptual and explanatory alternative to reductionist-based approaches in molecular biology. However, the foundations of this new discipline need to be fleshed out more carefully. In this paper, we claim that a relational ontology is a necessary tool to ground both the conceptual and explanatory aspects of Systems Biology. A relational ontology holds that relations are prior-both conceptually and explanatory-to entities, and that in the biological realm entities are defined primarily by the context they are embedded within-and hence by the web of relations they are part of.

NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes.

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. Database URL: http://bio.ieo.eu/ncg/.