Quantitative Biology from the arXiv

curated by isomorphismes

[quantitative-biology]

Authors: Margaret Wu, Grace S. Chiu, Lin Lu

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.

[quantitative-biology]

Authors: Satoru Morita, Jin Yoshimura

We study a stochastic linear discrete metapolulation model to understand the effect of risk spreading by dispersion. We calculate analytically the stable distribution of populations that live in different habitats. The result shows that the simultaneous distribution of the populations has a complicated self-similar structure, but a population at each habitat follows a log-normal distribution.

[quantitative-biology]

Authors: Tim Rogers, William Clifford-Brown, Catherine Mills, Tobias Galla

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.

[quantitative-biology]

Authors: Antoine Allard, Laurent Hébert-Dufresne, Pierre-André Noël, Vincent Marceau, Louis J. Dubé

We introduce a formalism for computing bond percolation properties of a class of correlated and clustered random graphs. This class of graphs is a generalization of the Configuration Model where nodes of different types are connected via different types of hyperedges, edges that can link more than 2 nodes. We argue that the multitype approach coupled with the use of clustered hyperedges can reproduce a wide spectrum of complex patterns, and thus enhances our capability to model real complex networks. As an illustration of this claim, we use our formalism to highlight unusual behaviors of the size and composition of the components (small and giant) in a synthetic, albeit realistic, social network.

[quantitative-biology]

Authors: Ehtibar N. Dzhafarov, Janne V. Kujala

We construct a class of real-valued nonnegative binary functions on a set of jointly distributed random variables, which satisfy the triangle inequality and vanish at identical arguments (pseudo-quasi-metrics). These functions are useful in dealing with the problem of selective probabilistic causality encountered in behavioral sciences and in quantum physics. The problem reduces to that of ascertaining the existence of a joint distribution for a set of variables with known distributions of certain subsets of this set. Any violation of the triangle inequality or its consequences by one of our functions when applied to such a set rules out the existence of this joint distribution. We focus on an especially versatile and widely applicable pseudo-quasi-metric called an order-distance and its special case called a classification distance.

[quantitative-biology]

Authors: Peter beim Graben, Serafim Rodrigues

We present a microscopic approach for the coupling of cortical activity, as resulting from proper dipole currents of pyramidal neurons, to the electromagnetic field in extracellular fluid in presence of diffusion and Ohmic conduction. As a result, neural activity becomes represented by a continuous neural field equation, while an observation model for electric field potentials is obtained from the interaction of cortical dipole currents with charge density in non-resistive extracellular space as described by the Nernst-Planck equation.

[quantitative-biology]

Authors: Peter beim Graben, Serafim Rodrigues

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential that contributes to the local field potential of a neural population. This work aligns and satisfies the widespread dipole assumption discussed in the neuroscientific literature, however to our knowledge, no proper observational model has ever been presented. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with a similar model by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel (2008). Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Computational Biology 4(12), e1000239], and conclude that our biophysically motivated approach yields substantial improvement.

[quantitative-biology]

Authors: Waipot Ngamsaad, Kannika Khompurngson

We investigate the pattern formation of colony generated by chemotactic bacteria through a continuum model. In a simplified case, the dynamics of system is governed by a density-dependent convection-reaction-diffusion equation, . This equation admits the analytical solutions that show the self-similarity of the bacterial colony's morphogenesis. In addition, we found that the colony evolves long time as the sharp traveling wave. The roles of chemotaxis on the regulation of pattern formation in these results are also discussed.

[quantitative-biology]

Authors: Brian G. Williams, Eleanor Gouws, John Hargrove, Cari van Schalkwyk, Hilmarie Brand

Anti-retroviral drugs can reduce the infectiousness of people living with HIV by about 96%--treatment as prevention or TasP--and can reduce the risk of being infected by an HIV positive person by about 70%--pre-exposure prophylaxis or PrEP--raising the prospect of using anti-retroviral drugs to stop the epidemic of HIV. The question as to which is more effective, more affordable and more cost effective, and under what conditions, continues to be debated in the scientific literature. Here we compare TasP and PreP in order to determine the conditions under which each strategy is favourable. This analysis suggests that where the incidence of HIV is less than 5% or the risk-reduction under PrEP is less than 50%, TasP is favoured over PrEP; otherwise PrEP is favoured over TasP. The potential for using PreP should therefore be restricted to those among whom the annual incidence of HIV is greater than 5% and TasP reduces transmission by more than 50%. PreP should be considered for commercial sex workers, young women aged about 20 to 25 years, men-who-have-sex with men, or intravenous drug users, but only where the incidence of HIV is high.

[quantitative-biology]

Authors: Caroline Montaudouin, Marie Anson, Yi Hao, Susanne V. Duncker, Tahia Fernandez, Emmanuelle Gaudin, Michael Ehrenstein, William G. Kerr, Jean-Herve Colle, Pierre Bruhns, Marc Daeron, Antonio A. Freitas

Maintenance of plasma IgM levels is critical for immune system function and homeostasis in humans and mice. However, the mechanisms that control homeostasis of the activated IgM-secreting B cells are unknown. After adoptive transfer into immune-deficient hosts, B-lymphocytes expand poorly but fully reconstitute the pool of natural IgM-secreting cells and circulating IgM levels. By using sequential cell transfers and B cell populations from several mutant mice, we were able to identify novel mechanisms regulating the size of the IgM-secreting B cell pool. Contrary to previous mechanisms described regulating homeostasis, which involve competition for the same niche by cells having overlapping survival requirements, homeostasis of the innate IgM-secreting B cell pool is also achieved when B cells populations are able to monitor the number of activated B cells by detecting their secreted products. Notably, B cell populations are able to assess the density of activated B cells by sensing their secreted IgG. This process involves the Fc{\gamma}RIIB, a low-affinity IgG receptor that is expressed on B cells and acts as a negative regulator of B cell activation, and its intracellular effector the inositol phosphatase SHIP. As a result of the engagement of this inhibitory pathway the number of activated IgM-secreting B cells is kept under control. We hypothesize that malfunction of this quorum-sensing mechanism may lead to uncontrolled B cell activation and autoimmunity.

[quantitative-biology]

Authors: Mathankumar Raja, T. R. Krishna Mohan

Lesions in central nervous system (CNS) and their growth leads to debilitating diseases like Multiple Sclerosis (MS), Alzheimer's etc. We developed a model earlier which shows how the lesion growth can be arrested through a beneficial auto-immune mechanism. The success of the approach depends on a set of control parameters and their phase space was shown to have a smooth manifold separating the uncontrolled lesion growth region from the controlled. Here we show that an optimal set of parameter values exist which minimizes system damage while achieving control of lesion growth.

[quantitative-biology]

Authors: Spyros Papageorgiou

The Hox gene collinearity enigma has often been approached using models based on biomolecular mechanisms. The biophysical model, is an alternative approach, speculating that collinearity is caused by physical forces pulling the Hox clusters from a territory where they are inactive to a distinct spatial domain where they are activated in a step by step manner.

Hox gene translocations have recently been observed in support of the biophysical model. Furthermore, genetic engineering experiments, performed in embryonic mice, gave rise to some unexpected mutant expressions that biomolecular models could not predict. In several cases when anterior Hoxd genes are deleted, the expression of the genes whose expression is probed in the mutants are impossible to anticipate. On the contrary, the biophysical model offers convincing explanation.

All these experimental results support the idea of physical forces being responsible for Hox gene collinearity. In order to test the validity of the various models further, certain experiment involving gene deletions are proposed. The biophysical and biomolecular models predict different results for these experiments, hence the expected outcome will confirm or question the validity of these models.

[quantitative-biology]

Authors: Martin Lundgren, Antti J. Niemi, Fan Sha

Folded proteins have a modular assembly. They are constructed from regular secondary structures like alpha-helices and beta-strands that are joined together by loops. Here we develop a visualization technique that is adapted to describe this modular structure. In complement to the widely employed Ramachandran plot that is based on toroidal geometry, our approach utilizes the geometry of a two-sphere. Unlike the more conventional approaches that only describea given peptide unit, ours is capable of describing the entire backbone environment including the neighboring peptide units. It maps the positions of each atom to the surface of the two-sphere exactly how these atoms are seen by an observer who is located at the position of the central C-alpha atom. At each level of side-chain atoms we observe a strong correlation between the positioning of the atom and the underlying local secondary structure with very little if any variation between the different amino acids. As a concrete example we analyze the left-handed helix region of non-glycyl amino acids. This region corresponds to an isolated and highly localized residue independent sector in the direction of the C-beta carbons on the two-sphere. We show that the residue independent localization extends to C-gamma and C-delta carbons, and to side-chain oxygen and nitrogen atoms in the case of asparagine and aspartic acid. When we extend the analysis to the side-chain atoms of the neighboring residues, we observe that left-handed beta-turns display a regular and largely amino acid independent structure that can extend to seven consecutive residues. This collective pattern is duu to the presence of a backbone soliton. We show how one can use our visualization techniques to analyze and classify the different solitons in terms of selection rules that we describe in detail.

[quantitative-biology]

Authors: Martin Lundgren, Antti J. Niemi

We investigate the fine structure of the sp3 hybridized covalent bond geometry that governs the tetrahedral architecture around the central C carbon of a protein backbone, and for this we develop new visualization techniques to analyze high resolution X-ray structures in Protein Data Bank. We observe that there is a correlation between the deformations of the ideal tetrahedral symmetry and the local secondary structure of the protein. We propose a universal coarse grained energy function to describe the ensuing side-chain geometry in terms of the C carbon orientations. The energy function can model the side-chain geometry with a sub-atomic precision. As an example we construct the C-C structure of HP35 chicken villin headpiece. We obtain a configuration that deviates less than 0.4 \.A in root-mean-square distance from the experimental X-ray structure.

[quantitative-biology]

Authors: Anna-Sophie Fiston-Lavier, Charles E. Vejnar, Hadi Quesneville

Transposable elements (TEs) in eukaryote genomes are quantitatively the main components affecting genome size, structure and expression. The dynamics of their insertion and deletion depend on diverse factors varying in strength and nature along the genome. We address here how TE sequence evolution is affected by neighboring genes and the chromatin status (euchromatin or heterochromatin) at their insertion site. We estimated the rates of evolution of TE sequences in Arabidopsis thaliana, and found that they depend on the distance to the nearest genes: TEs located close to genes evolve faster than those that are more distant. Consequently, TE sequences in heterochromatic regions, which are gene-poor regions, are surprisingly younger and longer than those elsewhere. We present a model of TE sequence dynamics in TE-rich genomes, such as maize and wheat, and in TE-poor genomes such as fly and A. thaliana.

[quantitative-biology]

Authors: Martin Carr, Douda Bensasson, Casey M. Bergman

Saccharomyces cerevisiae is one of the premier model systems for studying the genomics and evolution of transposable elements. The availability of the S. cerevisiae genome led to many insights into its five known transposable element families (Ty1-Ty5) in the years shortly after its completion. However, subsequent advances in bioinformatics tools for analysing transposable elements and the recent availability of genome sequences for multiple strains and species of yeast motivates new investigations into Ty evolution in S. cerevisiae. Here we provide a comprehensive phylogenetic and population genetic analysis of Ty families in S. cerevisiae based on a reannotation of Ty elements in the S288c reference genome. We show that previous annotation efforts have underestimated the total copy number of Ty elements for all known families. In addition, we identify a new family of Ty3-like elements related to the S. paradoxus Ty3p which is composed entirely of degenerate solo LTRs. Phylogenetic analyses of LTR sequences identified three families with short-branch, recently active clades nested among long branch, inactive insertions (Ty1, Ty3, Ty4), one family with essentially all recently active elements (Ty2) and two families with only inactive elements (Ty3p and Ty5). Population genomic data from 38 additional strains of S. cerevisiae show that elements present in active clades are predominantly polymorphic, whereas most of the inactive elements are fixed. Finally, we use comparative genomic data to provide evidence that the Ty2 and Ty3p families have arisen in the S. cerevisiae genome by horizontal transfer. Our results demonstrate that the genome of a single individual contains important information about the state of TE population dynamics within a species and suggest that horizontal transfer may play an important role in shaping the diversity of transposable elements in unicellular eukaryotes.

[quantitative-biology]

Authors: Kanaka Rajan, Olivier Marre, Gašper Tkačik

Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are only selective for a small number of linear projections of a potentially high-dimensional input. Here we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g. naturalistic) stimulus distribution, we review several inference methods, focussing in particular on two information-theory-based approaches (maximization of stimulus energy or of noise entropy) and a likelihood-based approach (Bayesian spike-triggered covariance, extensions of generalized linear models). We analyze the formal connection between the likelihood-based and information-based approaches to show how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.