Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red‐listed grasshopper

Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we use genomic data (restriction site‐associated DNA sequencing) to test biologically informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross‐backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species; (b) distributional shifts resulting from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles; and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation framework supported spatial configuration of topographic barriers to dispersal and human‐driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model‐based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.     

A layover in Europe: Reconstructing the invasion route of asexual lineages of a New Zealand snail to North America

Non‐native invasive species are threatening ecosystems and biodiversity worldwide. High genetic variation is thought to be a critical factor for invasion success. Accordingly, the global invasion of a few clonal lineages of the gastropod Potamopyrgus antipodarum is thus both puzzling and has the potential to help illuminate why some invasions succeed while others fail. Here, we used SNP markers and a geographically broad sampling scheme (N = 1617) including native New Zealand populations and invasive North American and European populations to provide the first widescale population genetic assessment of the relationships between and among native and invasive P. antipodarum. We used a combination of traditional and Bayesian molecular analyses to demonstrate that New Zealand populations harbour very high diversity relative to the invasive populations and are the source of the two main European genetic lineages. One of these two European lineages was in turn the source of at least one of the two main North American genetic clusters of invasive P. antipodarum, located in Lake Ontario. The other widespread North American group had a more complex origin that included the other European lineage and two New Zealand clusters. Altogether, our analyses suggest that just a small handful of clonal lineages of P. antipodarum were responsible for invasion across continents. Our findings provide critical information for prevention of additional invasions and control of existing invasive populations and are of broader relevance towards understanding the establishment and evolution of asexual populations and the forces driving biological invasion.     

Performance of a new dynamic model for predicting diurnal time courses of stomatal conductance at the leaf level

Under natural conditions, plants are subjected to continuous changes of irradiance that drive variations of stomatal conductance to water vapour (g_s). We propose a dynamic model to predict the temporal response of g_s at the leaf level using an asymmetric sigmoid function with a unique parameter describing time constants for increasing and decreasing g_s. The model parameters were adjusted to observed data using Approximate Bayesian Computation. We tested the model performance for (1) instant changes of irradiance; or (2) continuous and controlled variations of irradiance simulating diurnal time courses. Compared with the two mostly used steady‐state models, our dynamic model described daily time courses of g_s with a higher accuracy. In particular, it was able to describe the hysteresis of g_s responses to increasing/decreasing irradiance and the resulting rapid variations of intrinsic water‐use efficiency. Compared to the mechanistic model of temporal responses of g_s by Kirschbaum, Gross & Pearcy, for which time constants were estimated with a large variance, our model estimated time constants with a higher precision. It is expected to improve predictions of water loss and water‐use efficiency in higher scale models by using a small number of parameters.     

Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hot spot: a statistical phylogeographic study using a low-dispersal organism

The relative importance of the processes that generate and maintain biodiversity is a major and controversial topic in evolutionary biology with large implications for conservation management. The Atlantic Forest of Brazil, one of the world''s richest biodiversity hot spots, is severely damaged by human activities. To formulate an efficient conservation policy, a good understanding of spatial and temporal biodiversity patterns and their underlying evolutionary mechanisms is required. With this aim, we performed a comprehensive phylogeographic study using a low-dispersal organism, the land planarian species Cephaloflexa bergi (Platyhelminthes, Tricladida). Analysing multi-locus DNA sequence variation under the Approximate Bayesian Computation framework, we evaluated two scenarios proposed to explain the diversity of Southern Atlantic Forest (SAF) region. We found that most sampled localities harbour high levels of genetic diversity, with lineages sharing common ancestors that predate the Pleistocene. Remarkably, we detected the molecular hallmark of the isolation-by-distance effect and little evidence of a recent colonization of SAF localities; nevertheless, some populations might result from very recent secondary contacts. We conclude that extant SAF biodiversity originated and has been shaped by complex interactions between ancient geological events and more recent evolutionary processes, whereas Pleistocene climate changes had a minor influence in generating present-day diversity. We also demonstrate that land planarians are an advantageous biological model for making phylogeographic and, particularly, fine-scale evolutionary inferences, and propose appropriate conservation policies.     

Linkage and pyranosyl ring twisting in cyclodextrins

Acylated beta-cyclodextrins (beta-CDs) were studied to gain perspective on maltose octapropanoate, the crystal structure of which was reported in the preceding paper in this issue. Acylated beta-CDs are distorted so we looked at other CDs and gained increased understanding of distortion in CDs and possibly, shapes in starch. Classic CDs have six to eight glucose residues in a doughnut shape that is stabilized by a ring of inter-residue O3,,,O2' hydrogen bonds. On a phi,psi energy map for a maltose analog that does not form hydrogen bonds, classic CD linkages have higher energies than structures that are stabilized by the exo-anomeric effect. In distorted beta-CDs, which lack hydrogen bonding, some linkages attain low-energies from the exo-anomeric effect and acyl stacking. Those linkages result in left-handed helical geometry so other linkages are forced by the CD macrocycle to have counter-balancing right-handed character. Permethylated gamma-CDs have two 'flipping' linkages as do some larger native CDs. Flipping linkages allow two left-handed segments to join into a macrocycle, thus avoiding the higher-energy, right-handed forms. Some glucose rings in derivatized beta-CDs have substantial positive twists of the pseudo torsion angle O1-C1...C4-O4, adding right-handed character to balance the left-handed linkages. In substituted gamma-CD, all residues have negative twists, giving extra left-handed character to the short, pseudo-helical segments. In non-macrocyclic molecules the twists ranged from -14 degrees to +2 degrees , averaging -6.1 degrees. In these beta- and gamma-CDs, the twists ranged from -22 degrees to +16 degrees for (4)C(1) rings, and the (O)S(2) ring in acetylated beta-CD has a twist of +34 degrees . Glucose residues in other CDs were less twisted.     

Computational analysis of CFSE proliferation assay

CFSE based tracking of the lymphocyte proliferation using flow cytometry is a powerful experimental technique in immunology allowing for the tracing of labelled cell populations over time in terms of the number of divisions cells undergone. Interpretation and understanding of such population data can be greatly improved through the use of mathematical modelling. We apply a heterogenous linear compartmental model, described by a system of ordinary differential equations similar to those proposed by Kendall. This model allows division number-dependent rates of cell proliferation and death and describes the rate of changes in the numbers of cells having undergone j divisions. The experimental data set that we specifically analyze specifies the following characteristics of the kinetics of PHA-induced human T lymphocyte proliferation assay in vitroL (1) the total number of live cells, (2) the total number of dead but not disintegrated cells and (3) the number of cells divided j times. Following the maximum likelihood approach for data fitting, we estimate the model parameters which, in particular, present the CTL birth- and death rate “functions”. It is the first study of CFSE labelling data which convincingly shows that the lymphocyte proliferation and death both in vitro and in vivo are division number dependent. For the first time, the confidence in the estimated parameter values is analyzed by comparing three major methods: the technique based on the variance–covariance matrix, the profile-likelihood-based approach and the bootstrap technique. We compare results and performance of these methods with respect to their robustness and computational cost. We show that for evaluating mathematical models of differing complexity the information-theoretic approach, based upon indicators measuring the information loss for a particular model (Kullback–Leibler information), provides a consistent basis. We specifically discuss methodological and computational difficulties in parameter identification with CFSE data, e.g. the loss of confidence in the parameter estimates starting around the sixth division. Overall, our study suggests that the heterogeneity inherent in cell kinetics should be explicitly incorporated into the structure of mathematical models.      

The challenges and scope of theoretical biology

Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. At the same time, model building has proven to be very successful when it comes to explaining and predicting the behavior of particular biological systems. In this respect biology resembles alternative model-rich frameworks, such as economics and engineering. In this paper we explore the prospects for general theories in biology, and suggest that these take inspiration not only from physics, but also from the information sciences. Future theoretical biology is likely to represent a hybrid of parsimonious reasoning and algorithmic or rule-based explanation. An open question is whether these new frameworks will remain transparent to human reason. In this context, we discuss the role of machine learning in the early stages of scientific discovery. We argue that evolutionary history is not only a source of uncertainty, but also provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life.     

Disordered binding of small molecules to Aβ(12-28)

In recent years, an increasing number of small molecules and short peptides have been identified that interfere with aggregation and/or oligomerization of the Alzheimer β-amyloid peptide (Aβ). Many of them possess aromatic moieties, suggesting a dominant role for those in interacting with Aβ along various stages of the aggregation process. In this study, we attempt to elucidate whether interactions of such aromatic inhibitors with monomeric Aβ(12-28) point to a common mechanism of action by performing atomistic molecular dynamics simulations at equilibrium. Our results suggest that, independently of the presence of inhibitors, monomeric Aβ(12-28) populates a partially collapsed ensemble that is largely devoid of canonical secondary structure at 300 K and neutral pH. The small molecules have different affinities for Aβ(12-28) that can be partially rationalized by the balance of aromatic and charged moieties constituting the molecules. There are no predominant binding modes, although aggregation inhibitors preferentially interact with the N-terminal portion of the fragment (residues 13-20). Analysis of the free energy landscape of Aβ(12-28) reveals differences highlighted by altered populations of a looplike conformer in the presence of inhibitors. We conclude that intrinsic disorder of Aβ persists at the level of binding small molecules and that inhibitors can significantly alter properties of monomeric Aβ via multiple routes of differing specificity.     

Advancing science through mining libraries, ontologies, and communities

Life scientists today cannot hope to read everything relevant to their research. Emerging text-mining tools can help by identifying topics and distilling statements from books and articles with increased accuracy. Researchers often organize these statements into ontologies, consistent systems of reality claims. Like scientific thinking and interchange, however, text-mined information (even when accurately captured) is complex, redundant, sometimes incoherent, and often contradictory: it is rooted in a mixture of only partially consistent ontologies. We review work that models scientific reason and suggest how computational reasoning across ontologies and the broader distribution of textual statements can assess the certainty of statements and the process by which statements become certain. With the emergence of digitized data regarding networks of scientific authorship, institutions, and resources, we explore the possibility of accounting for social dependences and cultural biases in reasoning models. Computational reasoning is starting to fill out ontologies and flag internal inconsistencies in several areas of bioscience. In the not too distant future, scientists may be able to use statements and rich models of the processes that produced them to identify underexplored areas, resurrect forgotten findings and ideas, deconvolute the spaghetti of underlying ontologies, and synthesize novel knowledge and hypotheses.     

Bayesian inference of a historical bottleneck in a heavily exploited marine mammal

Emerging Bayesian analytical approaches offer increasingly sophisticated means of reconstructing historical population dynamics from genetic data, but have been little applied to scenarios involving demographic bottlenecks. Consequently, we analysed a large mitochondrial and microsatellite dataset from the Antarctic fur seal Arctocephalus gazella, a species subjected to one of the most extreme examples of uncontrolled exploitation in history when it was reduced to the brink of extinction by the sealing industry during the late eighteenth and nineteenth centuries. Classical bottleneck tests, which exploit the fact that rare alleles are rapidly lost during demographic reduction, yielded ambiguous results. In contrast, a strong signal of recent demographic decline was detected using both Bayesian skyline plots and Approximate Bayesian Computation, the latter also allowing derivation of posterior parameter estimates that were remarkably consistent with historical observations. This was achieved using only contemporary samples, further emphasizing the potential of Bayesian approaches to address important problems in conservation and evolutionary biology.