Noise from spatial heterogeneity changes signal amplification magnitude and increases the variability in dose responses

In most molecular level simulations, spatial heterogeneity is neglected by the well-mixed condition assumption. However, the signals of biomolecular networks are affected from both time and space, which are responsible for diverse physiological responses. To account the spatial heterogeneity in the kinetic model, we consider multiple subvolumes of a reaction, introduce parameters representing transfer of ligands between the volumes, and reduce this to an error-term representing the difference between the well-mixed condition and the actual spatial factors. The error-term approach allows modelling of varying spatial heterogeneity without increasing computational burden exponentially. The effect of varying this term, d, between 0 (well-mixed) and 1 (no mixing) and of adding noise to the kinetic constants was then investigated and correlated with knowledge of the behaviour of real systems and situations where network models are inadequate. The spatial distribution effects on the epidermal growth factor receptor (EGFR) in human mammary epithelial tissue, which is involved in proliferation and tumorigenesis, are studied by introducing noisy kinetic constants. The steady-state of the dose response in the EGFR is strongly affected by spatial fluctuations. The ligand-bound receptor is reduced up to 50% from the response without spatial fluctuations and the variance of the steady-state is increased at least 2-fold from the one for no spatial fluctuations. On the other hand, dynamic properties such as the rising time and overshoot are less sensitive to spatial noise

Validation of a model of regulation in the tryptophan operon against multiple experiment data using global optimisation

This paper is concerned with validating a mathematical model of regulation in the tryptophan operon using global optimization. Although a number of models for this biochemical network are proposed, in many cases only qualitative agreement between the model output and experimental data was demonstrated, since very little information is currently available to guide the selection of parameter values for the models. This paper presents a model validating method using both multiple experimental data and global optimization

Domestication, Genomics and the Future for Banana

Background Cultivated bananas and plantains are giant herbaceous plants within the genus Musa. They are both sterile and parthenocarpic so the fruit develops without seed. The cultivated hybrids and species are mostly triploid (2n = 3x = 33; a few are diploid or tetraploid), and most have been propagated from mutants found in the wild. With a production of 100 million tons annually, banana is a staple food across the Asian, African and American tropics, with the 15 % that is exported being important to many economies. Scope There are well over a thousand domesticated Musa cultivars and their genetic diversity is high, indicating multiple origins from different wild hybrids between two principle ancestral species. However, the difficulty of genetics and sterility of the crop has meant that the development of new varieties through hybridization, mutation or transformation was not very successful in the 20th century. Knowledge of structural and functional genomics and genes, reproductive physiology, cytogenetics, and comparative genomics with rice, Arabidopsis and other model species has increased our understanding of Musa and its diversity enormously. Conclusions There are major challenges to banana production from virulent diseases, abiotic stresses and new demands for sustainability, quality, transport and yield. Within the genepool of cultivars and wild species there are genetic resistances to many stresses. Genomic approaches are now rapidly advancing in Musa and have the prospect of helping enable banana to maintain and increase its importance as a staple food and cash crop through integration of genetical, evolutionary and structural data, allowing targeted breeding, transformation and efficient use of Musa biodiversity in the future

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

<b>Background</b>: We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. <b>Results</b>: The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and <i>mdm2</i> messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL)-6 and (IL)-12b messenger RNA expression as a function of ATF3 and NF-κB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL)-6 and (IL)-12b by ATF3. <b>Conclusion</b>: The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable more accurate and reliable identification and modelling of biochemical networks

From Crop Domestication to Super-domestication

Research related to crop domestication has been transformed by technologies and discoveries in the genome sciences as well as information-related sciences that are providing new tools for bioinformatics and systems' biology. Rapid progress in archaeobotany and ethnobotany are also contributing new knowledge to understanding crop domestication. This sense of rapid progress is encapsulated in this Special Issue, which contains 18 papers by scientists in botanical, crop sciences and related disciplines on the topic of crop domestication. One paper focuses on current themes in the genetics of crop domestication across crops, whereas other papers have a crop or geographic focus. One feature of progress in the sciences related to crop domestication is the availability of well-characterized germplasm resources in the global network of genetic resources centres (genebanks). Germplasm in genebanks is providing research materials for understanding domestication as well as for plant breeding. In this review, we highlight current genetic themes related to crop domestication. Impressive progress in this field in recent years is transforming plant breeding into crop engineering to meet the human need for increased crop yield with the minimum environmental impact – we consider this to be ‘super-domestication’. While the time scale of domestication of 10 000 years or less is a very short evolutionary time span, the details emerging of what has happened and what is happening provide a window to see where domestication might – and can – advance in the future

What Brown saw and you can too

A discussion is given of Robert Brown's original observations of particles ejected by pollen of the plant \textit{Clarkia pulchella} undergoing what is now called Brownian motion. We consider the nature of those particles, and how he misinterpreted the Airy disc of the smallest particles to be universal organic building blocks. Relevant qualitative and quantitative investigations with a modern microscope and with a "homemade" single lens microscope similar to Brown's, are presented.Comment: 14.1 pages, 11 figures, to be published in the American Journal of Physics. This differs from the previous version only in the web site referred to in reference 3. Today, this Brownian motion web site was launched, and http://physerver.hamilton.edu/Research/Brownian/index.html, is now correc

Magnetic unmixing of first-order reversal curve diagrams using principal component analysis

We describe a quantitative magnetic unmixing method based on principal component analysis (PCA) of first-order reversal curve (FORC) diagrams. For PCA we resample FORC distributions on grids that capture diagnostic signatures of single-domain (SD), pseudo-single-domain (PSD), and multidomain (MD) magnetite, as well as of minerals such as hematite. Individual FORC diagrams are recast as linear combinations of end-member (EM) FORC diagrams, located at user-defined positions in PCA space. The EM selection is guided by constraints derived from physical modeling and imposed by data scatter. We investigate temporal variations of two EMs in bulk North Atlantic sediment cores collected from the Rockall Trough and the Iberian Continental Margin. Sediments from each site contain a mixture of magnetosomes and granulometrically distinct detrital magnetite. We also quantify the spatial variation of three EM components (a coarse silt-sized MD component, a fine silt-sized PSD component, and a mixed clay-sized component containing both SD magnetite and hematite) in surficial sediments along the flow path of the North Atlantic Deep Water (NADW). These samples were separated into granulometric fractions, which helped constrain EM definition. PCA-based unmixing reveals systematic variations in EM relative abundance as a function of distance along NADW flow. Finally, we apply PCA to the combined dataset of Rockall Trough and NADW sediments, which can be recast as a four-EM mixture, providing enhanced discrimination between components. Our method forms the foundation of a general solution to the problem of unmixing multi-component magnetic mixtures, a fundamental task of rock magnetic studies.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC grant agreement 320750.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/2015GC00590

The progamic phase of an early-divergent angiosperm, Annona cherimola (Annonaceae)

33 Pag. The definitive version, with the figures, is available at: http://aob.oxfordjournals.org/Background and Aims: Recent studies of reproductive biology in ancient angiosperm lineages are beginning to shed light on the early evolution of flowering plants, but comparative studies are restricted by fragmented and meagre species representation in these angiosperm clades. In the present study, the progamic phase, from pollination to fertilization, is characterized in Annona cherimola, which is a member of the Annonaceae, the largest extant family among early-divergent angiosperms. Beside interest due to its phylogenetic position, this species is also an ancient crop with a clear niche for expansion in subtropical climates. Methods: The kinetics of the reproductive process was established following controlled pollinations and sequential fixation. Gynoecium anatomy, pollen tube pathway, embryo sac and early post-fertilization events were characterized histochemically. Key Results: A plesiomorphic gynoecium with a semi-open carpel shows a continuous secretory papillar surface along the carpel margins, which run from the stigma down to the obturator in the ovary. The pollen grains germinate in the stigma and compete in the stigma-style interface to reach the narrow secretory area that lines the margins of the semi-open stylar canal and is able to host just one to three pollen tubes. The embryo sac has eight nuclei and is well provisioned with large starch grains that are used during early cellular endosperm development. Conclusions: A plesiomorphic simple gynoecium hosts a simple pollen–pistil interaction, based on a support–control system of pollen tube growth. Support is provided through basipetal secretory activity in the cells that line the pollen tube pathway. Spatial constraints, favouring pollen tube competition, are mediated by a dramatic reduction in the secretory surface available for pollen tube growth at the stigma–style interface. This extramural pollen tube competition contrasts with the intrastylar competition predominant in more recently derived lineages of angiosperms.This work was supported by the Spanish Ministry of Education (Project Grants AGL2004-02290/AGR, AGL2006-13529 and AGL2007-60130/AGR), GIC-Aragón 43, Junta de Andalucía (AGR2742), and the European Union under the INCO-DEV program (Contract 015100). J.L. was supported by a grant from Junta de Andalucía.Peer reviewe

Characterisation of Perennial Ryegrass Parental Inbred Lines for Generating Recombinant Inbred Lines for Fine Mapping and Gene Cloning

Intermated recombinant inbred lines (IRIs) are a powerful tool for fine mapping and cloning of genes. Such population structures have been particularly helpful for cloning of genes in the model genetic plant Arabidopsis thaliana. IRIs or recombinant inbred lines (RILs) would be valuable for perennial ryegrass (Lolium perenne), but its allogamous character means that the construction of RILs is a difficult task. The international Lolium community would benefit from the development of such lines. The aims of the projects are to characterise the parental lines and initial generations at the (1) phenotypic, (2) molecular and (3) molecular cytogenetic level