Teniendo en consideración que la formación integral de un profesor de Física implica que éste tenga un dominio de su campo disciplinar, una concepción crítica sobre la ciencia que enseña y su enseñanza; presentamos en este trabajo el diseño de una propuesta didáctica que tiene como principal propósito considerar los principios de la Teoría del Aprendizaje Significativo Crítico de Moreira en el aula de clase, a partir de la implementación de actividades de modelación computacional. Estas actividades están apoyadas en el uso del diagrama AVM (Adaptación de la V de Gowin a la Modelación Computacional) y tienen como propósito favorecer el aprendizaje significativo de conceptos físicos y la formación de futuros profesores de Física con visiones más críticas y reflexivas en relación con el conocimiento científico, la modelación científica y la enseñanza de las Ciencias.; This paper presents a didactical proposal that aims to help the mastery of Physics contents and the developing of critical thinking on Physics and its learning by pre-service teachers. The proposal was designed to embed the principles of Moreira’s Critical Meaningful Learning Theory into computational modeling activities through the use of a heuristic instrument known as AVM diagram (Adaptation of Gowin’s V for Computational Modeling). A detailed description of the proposal and some of its possible implications for Physics teaching are discussed.
The purpose of this research was to investigate the learning of students who worked with computational modeling and simulation using the software Modellus in the study of simple electric circuits. The theoretical framework was based on Ausubel’s theory of meaningful learning and Halloun’s schematic modeling approach. The quantitative results show that there were statistically significant differences in the performance of students who worked with the computational activities (experimental group), when compared to the one in which students were just exposed to a traditional method of teaching (control group). The qualitative results suggest that many students from the experimental group seemed to have achieved meaningful learning, and the students’ interaction among themselves and with the teacher as well during the computational activities has strongly contributed for that.
El propósito de este trabajo fue realizar un estudio exploratorio sobre el proceso de aprendizaje de algunos conceptos básicos de dinámica newtoniana, a través de la implementación de actividades de modelación computacional haciendo uso del diagrama AVM (Adaptación del diagrama V de Gowin a la Modelación Computacional). La fundamentación teórica está soportada en la Teoría del aprendizaje significativo de Ausubel, en la modelación esquemática de Halloun y en la modelación computacional con diagrama AVM. Los resultados de esta investigación indican un desempeño satisfactorio de los estudiantes en la realización de estas actividades que se traduce no solo en la gran contribución de éstas para el aprendizaje de conceptos de dinámica newtoniana, sino también en su influencia positiva en la predisposición de los estudiantes para aprender conceptos físicos. Tales actividades fueron implementadas en un curso inicial de física con 23 estudiantes de Licenciatura en Ciencias Naturales y Educación Ambiental de la Universidad de Antioquia, Colombia.; The purpose of this work was to conduct an exploratory study on the learning process of some basic concepts of Newtonian dynamics, through computational modeling activities using the AVM diagram (Adaptation of the Gowin’s V diagram to Computational Modeling). The theoretical framework is based on Ausubel’s meaningful learning theory...
Neste trabalho apresentamos a modelagem computacional de uma válvula de expansão eletrônica a partir de dados experimentais de entradas e saídas através de modelos no espaço de estado, usando técnicas de subespaços, com objetivo de ter um sistema de refrigeração e ar condicionado eficiente, combinando eletrônica de potência e computação de modo a fornecer uma melhor solução para conservação de energia. A modelagem e a validação são feitas usando uma implementação computacional dos algoritmos de subespaços do espaço de estado. Os resultados apresentados mostram a validade e vantagens da técnica de modelagem realizada; This research shows the computational modeling of a electronic expansion valve based on input and output experimental data using Models in State Space and subspace methods. The aim of this work was to obtain an efficient Cooling and Air Conditioning system by the combination of power electronics and computation, as a result, a better solution for energy conservation was obtained. Modeling and validation are made using a computational implementation of subspace methods algorithms in state space. Achieved results show the validity and advantages of the modeling technique implemented
The use of computational modeling to predict arrhythmia and arrhythmogensis is a relatively new field, but has nonetheless dramatically enhanced our understanding of the physiological and pathophysiological mechanisms that lead to arrhythmia. This review summarizes recent advances in the field of computational modeling approaches with a brief review of the evolution of cellular action potential models, and the incorporation of genetic mutations to understand fundamental arrhythmia mechanisms, including how simulations have revealed situation specific mechanisms leading to multiple phenotypes for the same genotype. The review then focuses on modeling drug blockade to understand how the less-than-intuitive effects some drugs have to either ameliorate or paradoxically exacerbate arrhythmia. Quantification of specific arrhythmia indicies are discussed at each spatial scale, from channel to tissue. The utility of hERG modeling to assess altered repolarization in response to drug blockade is also briefly discussed. Finally, insights gained from Ca2+ dynamical modeling and EC coupling, neurohumoral regulation of cardiac dynamics, and cell signaling pathways are also reviewed.
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamoring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensorimotor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialog between two fictional characters: Ernest, the “experimenter,” and Mary, the “computational modeler.” The dialog consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and...
Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and conduct simulations of immune function, We provide descriptions of the key data gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.
One of the major obstacles in computational modeling of a biological system is to determine a large number of parameters in the mathematical equations representing biological properties of the system. To tackle this problem, we have developed a global optimization method, called Discrete Selection Levenberg-Marquardt (DSLM), for parameter estimation. For fast computational convergence, DSLM suggests a new approach for the selection of optimal parameters in the discrete spaces, while other global optimization methods such as genetic algorithm and simulated annealing use heuristic approaches that do not guarantee the convergence. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. The goal of computational modeling is to construct an analyzer to understand the nature of the system. Also, the simulation by computational modeling for phagocyte transmigration provides critical clues to recognize current knowledge of the system and to predict yet-to-be observed biological phenomenon.
Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation...
This chapter presents an overview of computational modeling as a tool for multilevel cancer care and intervention research. Model-based analyses have been conducted at various “beneath the skin” or biological scales as well as at various “above the skin” or socioecological levels of cancer care delivery. We review the basic elements of computational modeling and illustrate its applications in four cancer control intervention areas: tobacco use, colorectal cancer screening, cervical cancer screening, and racial disparities in access to breast cancer care. Most of these models have examined cancer processes and outcomes at only one or two levels. We suggest ways these models can be expanded to consider interactions involving three or more levels. Looking forward, a number of methodological, structural, and communication barriers must be overcome to create useful computational models of multilevel cancer interventions and population health.
Developing an understanding of the mechanism of voltage-gated ion channels in molecular terms requires knowledge of the structure of the active and resting conformations. Although the active-state conformation is known from x-ray structures, an atomic resolution structure of a voltage-dependent ion channel in the resting state is not currently available. This has motivated various efforts at using computational modeling methods and molecular dynamics (MD) simulations to provide the missing information. A comparison of recent computational results reveals an emerging consensus on voltage-dependent gating from computational modeling and MD simulations. This progress is highlighted in the broad context of preexisting work about voltage-gated channels.
Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and ?-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis.
Although computational modeling is common in many areas of science and engineering, only recently have advances in experimental techniques and medical imaging allowed this tool to be applied in cardiac surgery. Despite its infancy in cardiac surgery, computational modeling has been useful in calculating the effects of clinical devices and surgical procedures. In this review, we present several examples that demonstrate the capabilities of computational cardiac modeling in cardiac surgery. Specifically, we demonstrate its ability to simulate surgery, predict myofiber stress and pump function, and quantify changes to regional myocardial material properties. In addition, issues that would need to be resolved in order for computational modeling to play a greater role in cardiac surgery are discussed.
The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.
The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.
A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.
In this paper computational modeling efforts undertaken at NASA Stennis Space Center in support of rocket engine component testing are discussed. Such analyses include structurally complex cryogenic liquid valves and gas valves operating at high pressures and flow rates. Basic modeling and initial successes are documented, and other issues that make valve modeling at SSC somewhat unique are also addressed. These include transient behavior, valve stall, and the determination of flow patterns in LOX valves. Hexahedral structured grids are used for valves that can be simplifies through the use of axisymmetric approximation. Hybrid unstructured methodology is used for structurally complex valves that have disparate length scales and complex flow paths that include strong swirl, local recirculation zones/secondary flow effects. Hexahedral (structured), unstructured, and hybrid meshes are compared for accuracy and computational efficiency. Accuracy is determined using verification and validation techniques.
The development of the technology of ballistics as applied to gun launched Army weapon systems is the main objective of research at the U.S. Army Ballistic Research Laboratory (BRL). The primary research programs at the BRL consist of three major ballistic disciplines: exterior, interior, and terminal. The work done at the BRL in these areas was traditionally highly dependent on experimental testing. A considerable emphasis was placed on the development of computational modeling to augment the experimental testing in the development cycle; however, the impact of the computational modeling to this date is modest. With the availability of supercomputer computational resources recently installed at the BRL, a new emphasis on the application of computational modeling to ballistics technology is taking place. The major application areas are outlined which are receiving considerable attention at the BRL at present and to indicate the modeling approaches involved. An attempt was made to give some information as to the degree of success achieved and indicate the areas of greatest need.
Objective: Dysregulated muscle metabolism is a cardinal feature of human insulin resistance (IR) and associated diseases, including type 2 diabetes (T2D). However, specific reactions contributing to abnormal energetics and metabolic inflexibility in IR are unknown. Methods: We utilize flux balance computational modeling to develop the first systems-level analysis of IR metabolism in fasted and fed states, and varying nutrient conditions. We systematically perturb the metabolic network to identify reactions that reproduce key features of IR-linked metabolism. Results: While reduced glucose uptake is a major hallmark of IR, model-based reductions in either extracellular glucose availability or uptake do not alter metabolic flexibility, and thus are not sufficient to fully recapitulate IR-linked metabolism. Moreover, experimentally-reduced flux through single reactions does not reproduce key features of IR-linked metabolism. However, dual knockdowns of pyruvate dehydrogenase (PDH), in combination with reduced lipid uptake or lipid/amino acid oxidation (ETFDH), does reduce ATP synthesis, TCA cycle flux, and metabolic flexibility. Experimental validation demonstrates robust impact of dual knockdowns in PDH/ETFDH on cellular energetics and TCA cycle flux in cultured myocytes. Parallel analysis of transcriptomic and metabolomics data in humans with IR and T2D demonstrates downregulation of PDH subunits and upregulation of its inhibitory kinase PDK4...
Cell survival, growth, differentiation, migration, and communication all depend on the appropriate combination of specific interactions between proteins and biomolecules. Therefore, understanding the molecular mechanisms influencing protein-biomolecule binding interactions is important both for fundamental knowledge and as a foundation for therapeutic applications and biotechnology. This thesis presents two applications of computational modeling to study protein-biomolecule binding in different contexts. First, we sought to characterize effects of applied mechanical force on protein structural and biochemical properties. Despite growing experimental evidence of force-regulated cell behavior, the molecular mechanisms involved in force sensing and transmission are still largely unknown. We adapted a free energy method to directly compute the change in binding affinity upon force application. Our simulations demonstrated that differential responses in the bound and unbound state of a protein-ligand complex can lead to graded force-modulation of binding affinity. Application to a prototypical protein system - the helical bundle complex of a paxillin fragment bound to the FAT domain of focal adhesion kinase (FAK) revealed several structural mechanisms responsible. Second...