Active processes in one dimension

We consider the thermal and athermal overdamped motion of particles in 1D geometries where discrete internal degrees of freedom (spin) are coupled with the translational motion. Adding a driving velocity that depends on the time-dependent spin constitutes the simplest model of active particles (run-and-tumble processes) where the violation of the equipartition principle and of the Sutherland-Einstein relation can be studied in detail even when there is generalized reversibility. We give an example (with four spin values) where the irreversibility of the translational motion manifests itself only in higher-order (than two) time correlations. We derive a generalized telegraph equation as the Smoluchowski equation for the spatial density for an arbitrary number of spin values. We also investigate the Arrhenius exponential law for run-and-tumble particles, due to their activity the slope of the potential becomes important in contrast to the passive diffusion case and activity enhances the escape from a potential well (if that slope is high enough). Finally, in the absence of a driving velocity, the presence of internal currents such as in the chemistry of molecular motors may be transmitted to the translational motion and the internal activity is crucial for the direction of the emerging spatial current.Comment: 26 pages, 3 figure

Dynamics of learning motives and barriers in the context of changing human life roles

This paper promotes a theoretical discussion that focuses on the motives and barriers that make impact on adults learning as well as on their dynamics related to the change of social roles. The adult learning motives and barriers change and vary according to the prevailing social roles at different periods of one’s life. This dynamics of adult learning motives and barriers is mostly influenced by the importance and compatibility of acquired social roles, responsibility areas and spaces of a person and other factors. The qualitative data was gathered in March – April 2016 in Kaunas, Lithuania. The sample consisted of 30 narratives, written by informants, aged 35 to 65 years that were participating in professional training courses. There has been prepared 30 self-reflections that were analysed using content analysis. The analysis of empirical data shows that external learning motives and barriers prevail in the period when an individual is active in the labour market while the personal motives remain overshadowed. However, personal barriers prevail in the expression of learning barriers. This is influenced by the society’s attitude towards the performance of pupil and student roles and the value attitudes of surrounding people that partially control it

COMODI: COmputational MOdels DIffer - a hands-on-poster

Models are regularly updated, even after publication. The BiVeS library [3, 4, 5] offers comparison of versions of SBML [6] and CellML [7] encoded models. The result of such a comparison is a list of changes, based on the differences in both XML files. While it is now easy to see the differences in a network, it is still not possible to provide - or retrieve - information about the causes and effects of these changes. For example, together with a model update it should be possible to provide information on the characteristics of a change, e.g. stating that the changes remove an error in the kinetics.<br>We manually analysed hundreds of model versions and their differences from the BioModels Database [9] and the CellML Model Repository [8] to study the evolution of computational<br>models. We then derived a vocabulary to describe the differences and implemented it in the COMODI ontology. The OWL encoding and a documentation are available from our website [1]. A Java library [2] allows for easy integration in software projects.<br>We envision to use COMODI for automatic annotation of differences generated in BiVeS, and we like to provide it to modellers who wish to document<br>the evolution of their models.<br><br>[1] http://purl.org/net/comodi<br>[2] https://github.com/binfalse/jCOMODI<br>[3] https://sems.uni-rostock.de/bives/<br>[4] Scharm et al.: An algorithm to detect and communicate the differences in computational models describing biological systems Bioinformatics, 2015<br>[5] Waltemath et al.: Improving the reuse of computational models through version control Bioinformatics, 2013<br>[6] Hucka et al.: The Systems Biology Markup Language(SBML): A medium for representation and exchange of biochemical network models Bioinformatics, 2003<br>[7] Lloyd et al.: CellML: its future, presentand past Progress in Biophysicsand Molecular Biology, 2004<br>[8] Lloyd et al.: The CellML ModelRepository Bioinformatics, 2008<br>[9] Liet al.: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models BMC Systems Biology, 2010<br><br

Scheme of the primary carbon metabolism, encoded as a kinetic model of <i>Synechococcus elongatus</i> PCC 7942.

<p>The model includes the Calvin-Benson cycle, sucrose and glycogen synthesis, photorespiratory pathways, glycolysis and sink reactions, representing the adjacent pathways. Green color represents the reaction catalyzed by phosphoglycerate mutase (PGM) and indicates its cardinal position in the crossroads of metabolic pathways (need for complex model). Note: The reactions are described in the model by reversible and irreversible Michaelis-Menten kinetics; reversibility of particular reaction is indicated by two little arrows.</p

Phosphoglycerate Mutases Function as Reverse Regulated Isoenzymes in <em>Synechococcus elongatus</em> PCC 7942

<div><p>Phosphoglycerate-mutase (PGM) is an ubiquitous glycolytic enzyme, which in eukaryotic cells can be found in different compartments. In prokaryotic cells, several PGMs are annotated/localized in one compartment. The identification and functional characterization of PGMs in prokaryotes is therefore important for better understanding of metabolic regulation. Here we introduce a method, based on a multi-level kinetic model of the primary carbon metabolism in cyanobacterium <i>Synechococcus elongatus</i> PCC 7942, that allows the identification of a specific function for a particular PGM. The strategy employs multiple parameter estimation runs in high CO₂, combined with simulations testing a broad range of kinetic parameters against the changes in transcript levels of annotated PGMs. Simulations are evaluated for a match in metabolic level in low CO₂, to reveal trends that can be linked to the function of a particular PGM. A one-isoenzyme scenario shows that PGM2 is a major regulator of glycolysis, while PGM1 and PGM4 make the system robust against environmental changes. Strikingly, combining two PGMs with reverse transcriptional regulation allows both features. A conclusion arising from our analysis is that a two-enzyme PGM system is required to regulate the flux between glycolysis and the Calvin-Benson cycle, while an additional PGM increases the robustness of the system.</p> </div

Parameter space of the equilibrium constant and ratio of k_M constants for single PGM and dual PGMs scenarios.

<p>The values k_ms and k_mp indicate the k_M values for the preferred substrate and product (reversibility), respectively. Keq indicates the equilibrium constant. <b>Solid squares</b> denote independent runs of parameter estimation, presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058281#pone-0058281-g003" target="_blank">Fig. 3</a>, for single PGM scenario. <b>The open square</b> denotes the best fit for single PGM scenario. <b>Solid triangles</b> denote independent runs of parameter estimation, presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058281#pone-0058281-g005" target="_blank">Fig. 5</a>, for dual reverse regulated PGMs scenario for PGM beta. <b>The open triangle</b> denotes the best fit for dual reverse regulated PGMs scenario for PGM beta. <b>The open circle</b> denotes the best fit for triple PGMs scenario for PGM gamma. This analysis shows how multiple sets of kinetic parameters match experimental data in one steady state for unconstrainted parameter estimation.</p

Representation of energy storage by the ATP synthase at different levels of detail from the more abstract layer (left) to the more detailed layer (right).

<p>In the first diagram, the nature of the entities is unspecified (oval shaped glyphs) and the modulation is of unknown direction. The second diagram is more detailed with a macromolecule “ATP synthase” that stimulates the reaction consuming the simple chemicals “ADP” and “Pi” to produce the simple chemical “ATP.” In the third diagram, the ATP glyph has been substituted by a complex, making the diagram even more precise. Finally, the forth diagram highlights an identified complex catalysing the synthesis of a simple chemical. ADP, adenosine diphosphate; Pi, inorganic phosphate.</p

Quick tips for creating effective and impactful biological pathways using the Systems Biology Graphical Notation

<p>Quick tips for creating effective and impactful biological pathways using the Systems Biology Graphical Notation</p

Grouping of PGMs and PSPs by using cluster analysis.

<p>ClustalW2 2.1 (<a href="http://www.ebi.ac.uk/Tools/" target="_blank">http://www.ebi.ac.uk/Tools/</a>) was employed as a tool for protein alignment analysis, codon table for bacteria was selected. PGMs 1–4 (<i>Synechococcus</i>) and two PSPs (<i>Hydrogenobacter thermophilus</i>) are highlighted. PGM3 is clustered with two PSPs.</p

Quality for the match of simulated and measured data in low CO₂ for single PGM scenario in dependence on (i) varying PGM activities fitted in high CO₂ and (ii) regulation by transcript amounts.

<p>Figures represent the match between simulated and measured data in low CO₂ in dependence of estimated kinetic parameters (V_max and k_M values for preferred substrate and product) in high CO₂ for a single PGM scenario.The V_max, fitted to steady state in high CO₂, was modified by the amount of PGM isoforms taken from the changes in mRNA values (one by one) after shift from high to low CO₂. Results are shown for randomly chosen set of twenty parameters estimation runs for PGM and the best fit (Nr. 12). The <b>left figure</b> shows the match for preferred product of PGM, 2-phosphoglycerate (2PGA). The <u>black solid line</u> shows the impact of 1.7-fold down-regulated enzyme, corresponding to PGM4; <u>gray contours</u> indicate the difference in matching the data if PGM is 15.4-fold up-regulated (corresponding to PGM2). In order to illustrate the impact of transcriptomic changes for the other two annotated PGMs, the results for 1.4-fold down-regulated (<u>circle</u>) and 2.7-fold up-regulated (<u>square</u>) isoenzymes are presented in the case of the best fit. The <b>right figure</b> shows the match for preferred substrate of PGM, 3-phosphoglycerate (3PGA); colors/lines have the same meaning as for the figure on the left. Notes: 1) the top boundary of axis y shows results equal or worse than match ratio equals to 3, 2) The match ratio is calculated as X/Y where X(Y) is a higher(lower) number from a pair of simulated and experimental values for a particular data point, 3) Fit from high CO₂ was included (saved as a result) if the difference between simulated and measured data was smaller than 15%, 4) each point represents an independent simulation run compared to experimental data - the lines improve the perception for the differences in match ratios and have no other meaning.</p