Submission of structural biology data for review purposes.

The editors discuss the submission of structural biology data

Strategies for calibrating models of biology

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise

Automated multi-objective calibration of biological agent-based simulations

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions

Lipopolysaccharide-induced interferon response networks at birth are predictive of severe viral lower respiratory infections in the first year of life

Appropriate innate immune function is essential to limit pathogenesis and severity of severe lower respiratory infections (sLRI) during infancy, a leading cause of hospitalization and risk factor for subsequent asthma in this age group. Employing a systems biology approach to analysis of multi-omic profiles generated from a high-risk cohort (n = 50), we found that the intensity of activation of an LPS-induced interferon gene network at birth was predictive of sLRI risk in infancy (AUC = 0.724). Connectivity patterns within this network were stronger among susceptible individuals, and a systems biology approach identified IRF1 as a putative master regulator of this response. These findings were specific to the LPS-induced interferon response and were not observed following activation of viral nucleic acid sensing pathways. Comparison of responses at birth versus age 5 demonstrated that LPS-induced interferon responses but not responses triggered by viral nucleic acid sensing pathways may be subject to strong developmental regulation. These data suggest that the risk of sLRI in early life is in part already determined at birth, and additionally that the developmental status of LPS-induced interferon responses may be a key determinant of susceptibility. Our findings provide a rationale for the identification of at-risk infants for early intervention aimed at sLRI prevention and identifies targets which may be relevant for drug development

Fire Promotes Pollinator Visitation: Implications for Ameliorating Declines of Pollination Services

Pollinators serve critical roles for the functioning of terrestrial ecosystems, and have an estimated annual value of over $150 billion for global agriculture. Mounting evidence from agricultural systems reveals that pollinators are declining in many regions of the world, and with a lack of information on whether pollinator communities in natural systems are following similar trends, identifying factors which support pollinator visitation and services are important for ameliorating the effects of the current global pollinator crisis. We investigated how fire affects resource structure and how that variation influences floral pollinator communities by comparing burn versus control treatments in a southeastern USA old-field system. We hypothesized and found a positive relationship between fire and plant density of a native forb, Verbesina alternifolia, as well as a significant difference in floral visitation of V. alternifolia between burn and control treatments. V. alternifolia density was 44% greater and floral visitation was 54% greater in burned treatments relative to control sites. When the density of V. alternifolia was experimentally reduced in the burn sites to equivalent densities observed in control sites, floral visitation in burned sites declined to rates found in control sites. Our results indicate that plant density is a proximal mechanism by which an imposed fire regime can indirectly impact floral visitation, suggesting its usefulness as a tool for management of pollination services. Although concerns surround the negative impacts of management, indirect positive effects may provide an important direction to explore for managing future ecological and conservation issues. Studies examining the interaction among resource concentration, plant apparency, and how fire affects the evolutionary consequences of altered patterns of floral visitation are overdue. DOI: 10.1371/journal.pone.007985

Striking the right balance in right whale conservation

Author Posting. © The Authors, 2009. This article is posted here by permission of NRC Research Press for personal use, not for redistribution. The definitive version was published in Canadian Journal of Fisheries and Aquatic Sciences 66 (2009): 1399-1403, doi:10.1139/F09-115.Despite many years of study and protection, the North Atlantic right whale (Eubalaena glacialis) remains on the brink of extinction. There is a crucial gap in our understanding of their habitat use in the migratory corridor along the eastern seaboard of the United States. Here, we characterize habitat suitability in migrating right whales in relation to depth, distance to shore, and the recently enacted ship speed regulations near major ports. We find that the range of suitable habitat exceeds previous estimates and that, as compared with the enacted 20 nautical mile buffer, the originally proposed 30 nautical mile buffer would protect more habitat for this critically endangered species.This work was supported in part by SERDP/DoD grant W912HQ-04-C-0011 to A.J. Read and P.N. Halpin as well as a James B. Duke Fellowship and a Harvey L. Smith Dissertation Year Fellowship to R.S. Schick

Psychopolitics: Peter Sedgwick’s legacy for mental health movements

This paper re-considers the relevance of Peter Sedgwick's Psychopolitics (1982) for a politics of mental health. Psychopolitics offered an indictment of ‘anti-psychiatry’ the failure of which, Sedgwick argued, lay in its deconstruction of the category of ‘mental illness’, a gesture that resulted in a politics of nihilism. ‘The radical who is only a radical nihilist’, Sedgwick observed, ‘is for all practical purposes the most adamant of conservatives’. Sedgwick argued, rather, that the concept of ‘mental illness’ could be a truly critical concept if it was deployed ‘to make demands upon the health service facilities of the society in which we live’. The paper contextualizes Psychopolitics within the ‘crisis tendencies’ of its time, surveying the shifting welfare landscape of the subsequent 25 years alongside Sedgwick's continuing relevance. It considers the dilemma that the discourse of ‘mental illness’ – Sedgwick's critical concept – has fallen out of favour with radical mental health movements yet remains paradigmatic within psychiatry itself. Finally, the paper endorses a contemporary perspective that, while necessarily updating Psychopolitics, remains nonetheless ‘Sedgwickian’

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography.

This editorial article is simultaneously published by the IUCr in the journal IUCrJ (doi:10.1107/S2052252519005918) and in the journal Journal of applied crystallography (doi:10.1107/S1600576719005922) and in the journal Acta Crystallographica Section F structural biology communications (doi: 10.1107/S2053230X19005909)</p

Understanding Dwarf Galaxies in order to Understand Dark Matter

Much progress has been made in recent years by the galaxy simulation community in making realistic galaxies, mostly by more accurately capturing the effects of baryons on the structural evolution of dark matter halos at high resolutions. This progress has altered theoretical expectations for galaxy evolution within a Cold Dark Matter (CDM) model, reconciling many earlier discrepancies between theory and observations. Despite this reconciliation, CDM may not be an accurate model for our Universe. Much more work must be done to understand the predictions for galaxy formation within alternative dark matter models.Comment: Refereed contribution to the Proceedings of the Simons Symposium on Illuminating Dark Matter, to be published by Springe