Beckett Sterner

Discriminative learning for protein conformation sampling

Protein structure prediction without using templates (i.e., ab initio folding) is one of the most challenging problems in structural biology. In particular, conformation sampling poses as a major bottleneck of ab initio folding. This article presents CRFSampler, an extensible protein conformation sampler, built on a probabilistic graphical model Conditional Random Fields (CRFs). Using a discriminative learning method, CRFSampler can automatically learn more than ten thousand parameters quantifying the relationship among primary sequence, secondary structure, and (pseudo) backbone angles. Using only compactness and self‐avoiding constraints, CRFSampler can efficiently generate protein‐like conformations from primary sequence and predicted secondary structure. CRFSampler is also very flexible in that a variety of model topologies and feature sets can be defined to model the sequence‐structure relationship without worrying about parameter estimation. Our experimental results demonstrate that using a simple set of features, CRFSampler can generate decoys with much higher quality than the most recent HMM model. Proteins 2008.

To increase trust, change the social design behind aggregated biodiversity data

Abstract Growing concerns about the quality of aggregated biodiversity data are lowering trust in large-scale data networks. Aggregators frequently respond to quality concerns by recommending that biologists work with original data providers to correct errors ‘at the source.’ We show that this strategy falls systematically short of a full diagnosis of the underlying causes of distrust. In particular, trust in an aggregator is not just a feature of the data signal quality provided by the sources to the aggregator, but also a consequence of the social design of the aggregation process and the resulting power balance between individual data contributors and aggregators. The latter have created an accountability gap by downplaying the authorship and significance of the taxonomic hierarchies—frequently called ‘backbones’—they generate, and which are in effect novel classification theories that operate at the core of data-structuring process. The Darwin Core standard for sharing occurrence records plays an under-appreciated role in maintaining the accountability gap, because this standard lacks the syntactic structure needed to preserve the taxonomic coherence of data packages submitted for aggregation, potentially leading to inferences that no individual source would support. Since high-quality data packages can mirror competing and conflicting classifications, i.e. unsettled systematic research, this plurality must be accommodated in the design of biodiversity data integration. Looking forward, a key directive is to develop new technical pathways and social incentives for experts to contribute directly to the validation of taxonomically coherent data packages as part of a greater, trustworthy aggregation process.

The normative structure of mathematization in systematic biology

We argue that the mathematization of science should be understood as a normative activity of advocating for a particular methodology with its own criteria for evaluating good research. As a case study, we examine the mathematization of taxonomic classification in systematic biology. We show how mathematization is a normative activity by contrasting its distinctive features in numerical taxonomy in the 1960s with an earlier reform advocated by Ernst Mayr starting in the 1940s. Both Mayr and the numerical taxonomists sought to formalize the work of classification, but Mayr introduced a qualitative formalism based on human judgment for determining the taxonomic rank of populations, while the numerical taxonomists introduced a quantitative formalism based on automated procedures for computing classifications. The key contrast between Mayr and the numerical taxonomists is how they conceptualized the temporal structure of the workflow of classification, specifically where they allowed meta-level discourse about difficulties in producing the classification.

Moving Past the Systematics Wars

It is time to escape the constraints of the Systematics Wars narrative and pursue new questions that are better positioned to establish the relevance of the field in this time period to broader issues in the history of biology and history of science. To date, the underlying assumptions of the Systematics Wars narrative have led historians to prioritize theory over practice and the conflicts of a few leading theorists over the less-polarized interactions of systematists at large. We show how shifting to a practice-oriented view of methodology, centered on the trajectory of mathematization in systematics, demonstrates problems with the common view that one camp (cladistics) straightforwardly “won” over the other (phenetics). In particular, we critique David Hull’s historical account in Science as a Process by demonstrating exactly the sort of intermediate level of positive sharing between phenetic and cladistic theories that undermines their mutually exclusive individuality as conceptual systems over time. It is misleading, or at least inadequate, to treat them simply as holistically opposed theories that can only interact by competition to the death. Looking to the future, we suggest that the concept of workflow provides an important new perspective on the history of mathematization and computerization in biology after World War II.

Taxonomy - for Computers

We explore solutions for identifying and reconciling taxonomic concepts that take advantage of the powers of computational representation and reasoning without compromising the suitability of the Linnaean system of nomenclature for human communication. Using the model of the semiotic triangle, we show that taxonomic names must variously achieve reference to nomenclatural types, taxonomic concepts (human-created theories of taxonomic identities), and taxa (evolutionary entities in nature). Expansion of the reference models into temporally transitioning systems shows that the elements of each triangle, and provenance among elements across triangles, are only identifiable if taxonomic names and concepts are precisely contextualized. The Codes of nomenclature, by mandating identifier (name) reuse but not requiring concept-specific identifier granularity, leave the challenge of framing and aligning the symbol/reference instances to human communicators who have superior cognitive abilities in this regard. Computers, in turn, can process greater volumes of narrowly framed and logically aligned data. Comparative, taxonomically referenced biological data are becoming increasingly networked and reutilized in analyses that expand their original context of generation. If we expect our virtual comparative information environments to provide logically enabled taxonomic concept provenance services, then we must improve the syntax and semantics of human taxonomy making – for computers.

Review of Sabina Leonelli’s Data-Centric Biology: A Philosophical Study

Introduction. The word “data” is everywhere in current discussions of science. How we store and share things people label “data” has become a central concern for the Open Science movement, for example, and the National Science Foundation and National Institutes of Health have invested billions of dollars to create publicly accessible databases as a major new source of intellectual capital in science and industry. What is new here? Has there been a shift in what “data” means that is key to understanding the future of science? Sabina Leonelli’s new book,Data-Centric Biology: A Philosophical Study, argues for an important and fruitful answer outside the comfort zone of many philosophers. Along the way, she also delivers valuable insights into expanding efforts to standardize, automate, and communicate how scientists handle, share, reproduce, interpret, and store data. From the start, Leonelli rejects the idea that we can understand the significance of data for science in terms of intrinsic properties data possess as material traces of past processes. Similarly, the changes we are seeing in science are not driven simply by revolutionary technologies or methods. “The real source of innovation in current biology is the attention paid to data handling and dissemination practices and the ways in which such practices mirror economic and political modes of interaction and decision making” (1). In other words, data have moved to the center of intense social, economic, and political negotiations

The Practical Value of Biological Information for Research

Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place.