Encouraging Creativity and Intellectual Stimulation: An Exercise that Forces Students to Think Outside the Box

This exercise involves a hands-on approach to generating innovation and creativity in the workplace. It is feasible as a follow-up, special-topic activity in intellectual stimulation in full-range or transformational leadership training. Participants are presented with the seemingly impossible task of integrating diverse products or services into a single business plan, forcing them to think outside the box. This exercise features lateral and innovative thinking in a highly interactive session, producing innovative and creative solutions from participants. After successfully completing this exercise, participants will be more confident in their ability to creatively solve many challenges that at first glance seem impossible. The paper provides theoretical background, objectives, complete instructions, processing information, and some suggestions for advancing the concepts

Modeling the differentiation of A- and C-type baroreceptor firing patterns

The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion channel dynamics within the baroreceptor neurons. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed variation in the model parameters are hypothesized to correspond to physiological differences between A- and C-type neurons. In agreement with published experimental observations, our simulations suggest that a twofold lower potassium conductance in C-type neurons is responsible for the observed sustained basal firing, whereas a tenfold higher mechanosensitive conductance is responsible for the greater firing rate observed in A-type neurons. A better understanding of the difference between the two neuron types can potentially be used to gain more insight into the underlying pathophysiology facilitating development of targeted interventions improving baroreflex function in diseased individuals, e.g. in patients with autonomic failure, a syndrome that is difficult to diagnose in terms of its pathophysiology.Comment: Keywords: Baroreflex model, mechanosensitivity, A- and C-type afferent baroreceptors, biophysical model, computational mode

Modeling the Afferent Dynamics of the Baroreflex Control System

In this study we develop a modeling framework for predicting baroreceptor firing rate as a function of blood pressure. We test models within this framework both quantitatively and qualitatively using data from rats. The models describe three components: arterial wall deformation, stimulation of mechanoreceptors located in the BR nerve-endings, and modulation of the action potential frequency. The three sub-systems are modeled individually following well-established biological principles. The first submodel, predicting arterial wall deformation, uses blood pressure as an input and outputs circumferential strain. The mechanoreceptor stimulation model, uses circumferential strain as an input, predicting receptor deformation as an output. Finally, the neural model takes receptor deformation as an input predicting the BR firing rate as an output. Our results show that nonlinear dependence of firing rate on pressure can be accounted for by taking into account the nonlinear elastic properties of the artery wall. This was observed when testing the models using multiple experiments with a single set of parameters. We find that to model the response to a square pressure stimulus, giving rise to post-excitatory depression, it is necessary to include an integrate-and-fire model, which allows the firing rate to cease when the stimulus falls below a given threshold. We show that our modeling framework in combination with sensitivity analysis and parameter estimation can be used to test and compare models. Finally, we demonstrate that our preferred model can exhibit all known dynamics and that it is advantageous to combine qualitative and quantitative analysis methods

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems

Molecular networks in Dahl salt-sensitive hypertension based on transcriptome analysis of a panel of consomic rats

The Dahl salt-sensitive (SS) rat is a widely used model of human salt-sensitive hypertension and renal injury. We studied the molecular networks that underlie the complex disease phenotypes in the SS model, using a design that involved two consomic rat strains that were protected from salt-induced hypertension and one that was not protected. Substitution of Brown Norway (BN) chromosome 13 or 18, but not 20, into the SS genome was found to significantly attenuate salt-induced hypertension and albuminuria. Gene expression profiles were examined in the kidneys of SS and consomic SS-13BN, SS-18 BN, and SS-20BN rats with a total of 240 cDNA microarrays. The substituted chromosome was overrepresented in genes differentially expressed between a consomic strain and SS rats on a 0.4% salt diet. F5, Serpinc1, Slc19a2, and genes represented by three other expressed sequence tags (ESTs), which are located on chromosome 13, were found to be differentially expressed between SS-13BN and all other strains examined. Likewise, Acaa2, B4galt6, Colec12, Hsd17b4, and five other ESTs located on chromosome 18 exhibited expression patterns unique to SS-18BN. On exposure to a 4% salt diet, there were 184 ESTs in the renal cortex and 346 in the renal medulla for which SS-13BN and SS-18BN shared one expression pattern, while SS and SS-20BN shared another, mirroring the phenotypic segregation among the four strains. Molecular networks that might contribute to the development of Dahl salt-sensitive hypertension and albuminuria were constructed with an approach that merged biological knowledge-driven analysis and data-driven Bayesian probabilistic analysis. Copyright © 2008 the American Physiological Society

Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation

We consider the inverse and patient specific problem of short term (seconds to minutes) heart rate regulation specified by a system of nonlinear ODEs and corresponding data. We show how a recent method termed the structural correlation method (SCM) can be used for model reduction and for obtaining a set of practically identifiable parameters. The structural correlation method includes two steps: sensitivity and correlation analysis. When combined with an optimization step, it is possible to estimate model parameters, enabling the model to fit dynamics observed in data. This method is illustrated in detail on a model predicting baroreflex regulation of heart rate and applied to analysis of data from a rat and healthy humans. Numerous mathematical models have been proposed for prediction of baroreflex regulation of heart rate, yet most of these have been designed to provide qualitative predictions of the phenomena though some recent models have been developed to fit observed data. In this study we show that the model put forward by Bugenhagen et al. (2010) can be simplified without loss of its ability to predict measured data and to be interpreted physiologically. Moreover, we show that with minimal changes in nominal parameter values the simplified model can be adapted to predict observations from both rats and humans. The use of these methods make the model suitable for estimation of parameters from individuals, allowing it to be adopted for diagnostic procedures