Computer-supported analysis of scientific measurements

In the past decade, large-scale databases and knowledge bases have become available to researchers working in a range of scientific disciplines. In many cases these databases and knowledge bases contain measurements of properties of physical objects which have been obtained in experiments or at observation sites. As examples, one can think of crystallographic databases with molecular structures and property databases in materials science. These large collections of measurements, which will be called measurement bases, form interesting resources for scientific research. By analyzing the contents of a measurement base, one may be able to find patterns that are of practical and theoretical importance. With the use of measurement bases as a resource for scientific inquiry questions arise about the quality of the data being analyzed. In particular, the occurrence of conflicts and systematic errors raises doubts about the reliability of a measurement base and compromises any patterns found in it. On the other hand, conflicts and systematic errors may be interesting patterns in themselves and warrant further investigation. These considerations motivate the topic that will be addressed in this thesis: the development of systematic methods for detecting and resolving con icts and identifying\ud systematic errors in measurement bases. These measurement analysis (MA) methods are implemented in a computer system supporting the user of the measurement base

Semi-Quantitative Comparative Analysis And Its Application

SQCA is an implemented technique for the semi-quantitative comparative analysis of dynamical systems. It is both able to deal with incompletely specified models and make precise predictions by exploiting semi-quantitative information in the form of numerical bounds on the variables and functions occuring in the models. The technique has a solid mathematical foundation which facilitates proofs of correctness and convergence properties. SQCA represents the core of a method for the automated prediction of experimental results

The computer revolution in science: steps towards the realization of computer-supported discovery environments

The tools that scientists use in their search processes together form so-called discovery environments. The promise of artificial intelligence and other branches of computer science is to radically transform conventional discovery environments by equipping scientists with a range of powerful computer tools including large-scale, shared knowledge bases and discovery programs. We will describe the future computer-supported discovery environments that may result, and illustrate by means of a realistic scenario how scientists come to new discoveries in these environments. In order to make the step from the current generation of discovery tools to computer-supported discovery environments like the one presented in the scenario, developers should realize that such environments are large-scale sociotechnical systems. They should not just focus on isolated computer programs, but also pay attention to the question how these programs will be used and maintained by scientists in research practices. In order to help developers of discovery programs in achieving the integration of their tools in discovery environments, we will formulate a set of guidelines that developers could follow

Semi-quantitative comparative analysis

Comparative analysis (CA) of dynamical systems is an important problem in qualitative reasoning. CA techniques predict differences in the behavior of two systems as a consequence of differences in the initial conditions or structural differences. A disadvantage of these techniques is the imprecision of the possible answers due to their qualitative nature. This report presents SQCA, an implemented technique for the semi-quantitative comparative analysis of dynamical systems. SQCA is both able to deal with incompletely specified models and make precise predictions by exploiting numerical information in the formof interval bounds on variable values and envelope functions around monotonic relations. The technique has a solid mathematical foundation which facilitates proofs of correctness and convergence properties

Modeling and Simulation of Genetic Regulatory Systems : A Literature Review

The spatiotemporal expression of genes in an organism is determined by regulatory systems that involve a large number of genes connected through a complex network of interactions. As an intuitive understanding of the behavior of these systems is hard to obtain, computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This report reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, ordinary and partial differential equations, stochastic equations, Boolean networks and their generalizations, qualitative differential equations, and rule-based formalisms. In addition, the report discusses how these formalisms have been used in the modeling and simulation of regulatory systems

Qualitative Simulation and Related Approaches for the Analysis of Dynamical Systems

Methods for qualitative simulation allow predictions to be made on the behavior of a system for which no quantitative information is available. In addition, they help obtain a comprehension of the range of possible qualitative behaviors compatible with the structure of a system. This report reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamical systems have emerged: qualitative phase space analysis and semi-quantitative simulation. The report argues for a synthesis of these approaches to obtain a computational framework for the qualitative analysis of dynamical systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope

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

Piecewise-linear Models of Genetic Regulatory Networks: Equilibria and their Stability

A formalism based on piecewise-linear (PL) differential equations, originally due to Glass and Kauffman, has been shown to be well-suited to modelling genetic regulatory networks. However, the discontinuous vector field inherent in the PL models raises some mathematical problems in defining solutions on the surfaces of discontinuity. To overcome these difficulties we use the approach of Filippov, which extends the vector field to a differential inclusion. We study the stability of equilibria (called singular equilibrium sets) that lie on the surfaces of discontinuity. We prove several theorems that characterize the stability of these singular equilibria directly from the state transition graph, which is a qualitative representation of the dynamics of the system. We also formulate a stronger conjecture on the stability of these singular equilibrium sets

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

The logic layout of the TOL network of Pseudomonas putida pWW0 plasmid stems from a metabolic amplifier motif (MAM) that optimizes biodegradation of m-xylene

<p>Abstract</p> <p>Background</p> <p>The genetic network of the TOL plasmid pWW0 of the soil bacterium <it>Pseudomonas putida </it>mt-2 for catabolism of <it>m-</it>xylene is an archetypal model for environmental biodegradation of aromatic pollutants. Although nearly every metabolic and transcriptional component of this regulatory system is known to an extraordinary molecular detail, the complexity of its architecture is still perplexing. To gain an insight into the inner layout of this network a logic model of the TOL system was implemented, simulated and experimentally validated. This analysis made sense of the specific regulatory topology out on the basis of an unprecedented network motif around which the entire genetic circuit for <it>m-</it>xylene catabolism gravitates.</p> <p>Results</p> <p>The most salient feature of the whole TOL regulatory network is the control exerted by two distinct but still intertwined regulators (XylR and XylS) on expression of two separated catabolic operons (<it>upper </it>and <it>lower</it>) for catabolism of <it>m</it>-xylene. Following model reduction, a minimal modular circuit composed by five basic variables appeared to suffice for fully describing the operation of the entire system. <it>In silico </it>simulation of the effect of various perturbations were compared with experimental data in which specific portions of the network were activated with selected inducers: <it>m-</it>xylene, <it>o-</it>xylene, 3-methylbenzylalcohol and 3-methylbenzoate. The results accredited the ability of the model to faithfully describe network dynamics. This analysis revealed that the entire regulatory structure of the TOL system enables the action an unprecedented metabolic amplifier motif (MAM). This motif synchronizes expression of the <it>upper </it>and <it>lower </it>portions of a very long metabolic system when cells face the head pathway substrate, <it>m-</it>xylene.</p> <p>Conclusion</p> <p>Logic modeling of the TOL circuit accounted for the intricate regulatory topology of this otherwise simple metabolic device. The found MAM appears to ensure a simultaneous expression of the <it>upper </it>and <it>lower </it>segments of the <it>m-</it>xylene catabolic route that would be difficult to bring about with a standard substrate-responsive single promoter. Furthermore, it is plausible that the MAM helps to avoid biochemical conflicts between competing plasmid-encoded and chromosomally-encoded pathways in this bacterium.</p