Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand.</p> <p>Results</p> <p>In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort.</p> <p>Conclusions</p> <p>The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.</p

Graph problems arising from parameter identification of discrete dynamical systems

This paper focuses on combinatorial feasibility and optimization problems that arise in the context of parameter identification of discrete dynamical systems. Given a candidate parametric model for a physical system and a set of experimental observations, the objective of parameter identification is to provide estimates of the parameter values for which the model can reproduce the experiments. To this end, we define a finite graph corresponding to the model, to each arc of which a set of parameters is associated. Paths in this graph are regarded as feasible only if the sets of parameters corresponding to the arcs of the path have nonempty intersection. We study feasibility and optimization problems on such feasible paths, focusing on computational complexity. We show that, under certain restrictions on the sets of parameters, some of the problems become tractable, whereas others are NP-hard. In a similar vein, we define and study some graph problems for experimental design, whose goal is to support the scientist in optimally designing new experiment

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (ly-. ing bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine -learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sen-sitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential

Passive States for Essential Observers

The aim of this note is to present a unified approach to the results given in \cite{bb99} and \cite{bs04} which also covers examples of models not presented in these two papers (e.g. $d$-dimensional Minkowski space-time for $d\geq 3$). Assuming that a state is passive for an observer travelling along certain (essential) worldlines, we show that this state is invariant under the isometry group, is a KMS-state for the observer at a temperature uniquely determined by the structure constants of the Lie algebra involved and fulfills (a variant of) the Reeh-Schlieder property. Also the modular objects associated to such a state and the observable algebra of an observer are computed and a version of weak locality is examined.Comment: 27 page

A protocol for identifying the binding sites of small molecules on the cystic fibrosis transmembrane conductance regulator (CFTR) protein

We describe a protocol to identify the binding site(s) for a drug called ivacaftor that potentiates the CFTR chloride channel. We use photoaffinity probes-based on the structure of ivacaftor-to covalently modify the CFTR protein at the region that constitutes the drug binding site(s). We define the methods for photo-labeling CFTR, its membrane extraction, and enzymatic digestion using trypsin. We then describe the experimental methods to identify the modified peptides by using mass spectrometry. For complete details on the use and execution of this protocol, please refer to Laselva et&nbsp;al. (2021)

Cosmogenic Nuclide Systematics and the CRONUScalc Program

As cosmogenic nuclide applications continue to expand, the need for a common basis for calculation becomes increasingly important. In order to accurately compare between results from different nuclides, a single method of calculation is necessary. Calculators exist in numerous forms with none matching the needs of the CRONUS-Earth project to provide a simple and consistent method to interpret data from most commonly used cosmogenic nuclides. A new program written for this purpose, CRONUScalc, is presented here. This unified code presents a method applicable to 10Be, 26Al, 36Cl, 3He, and 14C, with 21Ne in testing. The base code predicts the concentration of a sample at a particular depth for a particular time in the past, which can be used for many applications. The multi-purpose code already includes functions for performing production rate calibrations as well as calculating erosion rates and surface exposure ages for single samples and depth profiles. The code is available under the GNU General Public License agreement and can be downloaded and modified to deal with specific atypical scenarios

Biofunctionalization of zinc oxide nanowires for DNA sensory applications

We report on the biofunctionalization of zinc oxide nanowires for the attachment of DNA target molecules on the nanowire surface. With the organosilane glycidyloxypropyltrimethoxysilane acting as a bifunctional linker, amino-modified capture molecule oligonucleotides have been immobilized on the nanowire surface. The dye-marked DNA molecules were detected via fluorescence microscopy, and our results reveal a successful attachment of DNA capture molecules onto the nanowire surface. The electrical field effect induced by the negatively charged attached DNA molecules should be able to control the electrical properties of the nanowires and gives way to a ZnO nanowire-based biosensing device