Experiment Selection for the Discrimination of Semi-Quantitative Models of Dynamical Systems

Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for experiment selection that helps in discriminating between differential equation models of experimental systems in a systematic and efficient way. The method generalizes upon previous work on model discrimination in that it deals with semi-quantitative differential equations, which use interval bounds on parameter values and envelopes for functional relations. The model discrimination method is based on an entropy criterion for the selection of the most informative experiment. The applicability of the method to real-world problems is illustrated by means of an example in population biology, the discrimination of competing models of the growth of phytoplankton in a bioreactor

Experiment selection for the discrimination of semi-quantitative models of dynamical systems

AbstractModeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. To discriminate among these models, additional experiments are needed. Existing methods for the selection of discriminatory experiments in statistics and in artificial intelligence are often based on an entropy criterion, the so-called information increment. A limitation of these methods is that they are not well-adapted to discriminating models of dynamical systems under conditions of limited measurability. Moreover, there are no generic procedures for computing the information increment of an experiment when the models are qualitative or semi-quantitative. This has motivated the development of a method for the selection of experiments to discriminate among semi-quantitative models of dynamical systems. The method has been implemented on top of existing implementations of the qualitative and semi-quantitative simulation techniques QSIM, Q2, and Q3. The applicability of the method to real-world problems is illustrated by means of an example in population biology: the discrimination of four competing models of the growth of phytoplankton in a bioreactor. The models have traditionally been considered equivalent for all practical purposes. Using our model discrimination approach and experimental data we show, however, that two of them are superior for describing phytoplankton growth under a wide range of experimental conditions

Experiment Selection for the Discrimination of Semi-Quantitative Models of Dynamical Systems

Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for experiment selection that helps in discriminating between differential equation models of experimental systems in a systematic and efficient way. The method generalizes upon previous work on model discrimination in that it deals with semi-quantitative differential equations, which use interval bounds on parameter values and envelopes for functional relations. The model discrimination method is based on an entropy criterion for the selection of the most informative experiment. The applicability of the method to real-world problems is illustrated by means of an example in population biology, the discrimination of competing models of the growth of phytoplankton in a bioreactor

Discrimination of Semi-Quantitative Models by Experiment Selection: Method and Application in Population Biology

Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semiquantitative differential equations. The method is a generalization of previous work in model discrimination. It is based on an entropy criterion for the selection of the most informative experiment which can handle cases where the models predict multiple qualitative behaviors. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor

Organs on chip approach: A tool to evaluate cancer-immune cells interactions

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a micro uidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative con rmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems

The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe

Experimental effects on dynamics and thermodynamics in nuclear reactions on the symmetry energy as seen by the CHIMERA 4π\pi detector

Heavy ion collisions have been widely used in the last decade to constraint the parameterizations of the symmetry energy term of nuclear equation of state (EOS) for asymmetric nuclear matter as a function of baryonic density. In the Fermi energy domain one is faced with variations of the density within a narrow range of values around the saturation density $\rho_0$=0.16 fm$^{-3}$ down towards sub-saturation densities. The experimental observables which are sensitive to the symmetry energy are constructed starting from the detected light particles, clusters and heavy fragments that, in heavy ion collisions, are generally produced by different emission mechanisms at different stages and time scales of the reaction. In this review the effects of dynamics and thermodynamics on the symmetry energy in nuclear reactions are discussed and characterized using an overview of the data taken so far with the CHIMERA multi-detector array.Comment: 21 pages, 25 figures. Review to appear in EPJA special volume on nuclear symmetry energ

Kinetic Intersections as a Source of Information on Rate Coefficients

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Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe

Recordings from neurons in the insects' olfactory primary processing center, the antennal lobe (AL), reveal that the AL is able to process the input from chemical receptors into distinct neural activity patterns, called olfactory neural codes. These exciting results show the importance of neural codes and their relation to perception. The next challenge is to \emph{model the dynamics} of neural codes. In our study, we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a neural network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons, and is capable of producing unique olfactory neural codes for the tested odorants. Specifically, we (i) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii) characterize scent recognition, i.e., decision-making based on olfactory signals and (iii) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study answers a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns