The effectiveness of a comprehensive diabetes program for glycemic control and adherence, and the selection of candidates for sensor-augmented insulin pump therapy.

OBJECTIVE: This study aimed to estimate the effectiveness of a comprehensive diabetes program (CDP) in terms of glycemic control, adherence, and the selection of candidates for sensor-augmented insulin pump therapy (SAP). METHODS: We compared diabetes control before and 6 months after CDP. The program was based on disease management using a logical model dealing with the following: case management, education and coaching, nutritional assessment, and mental health. RESULTS: The CDP improved glycemic control, HbA1c decreased by 0.56% (p-value=0.004; 95% CI: 0.14-0.98) and 19.1% of the patients reached the HbA1c goal without hypoglycemia. The CDP reduced by 52.4% the indication for SAP due to better glycemic control (36.4%) or non-adherence issues (63.6%); the remaining 47.6% persisted with poor glycemic control despite good adherence and were scaled to SAP. Among the 30 suitable candidates for SAP therapy, 60% did not reach the HbA1c goal and 40% had either hypoglycemic episodes (severe or persistent) or dawn phenomenon. The overall non-adherence rate was 33.3%. CONCLUSIONS: CDP optimized the selection of suitable candidates for SAP by improving glycemic control and identifying adherence issues early. These results provide evidence of the impact of the implementation of patient selection and educational protocols in the real-life setting of a highly experienced clinic.

The effectiveness of a comprehensive diabetes program for glycemic control and adherence, and the selection of candidates for sensor-augmented insulin pump therapy.

OBJECTIVE: This study aimed to estimate the effectiveness of a comprehensive diabetes program (CDP) in terms of glycemic control, adherence, and the selection of candidates for sensor-augmented insulin pump therapy (SAP). METHODS: We compared diabetes control before and 6 months after CDP. The program was based on disease management using a logical model dealing with the following: case management, education and coaching, nutritional assessment, and mental health. RESULTS: The CDP improved glycemic control, HbA1c decreased by 0.56% (p-value=0.004; 95% CI: 0.14-0.98) and 19.1% of the patients reached the HbA1c goal without hypoglycemia. The CDP reduced by 52.4% the indication for SAP due to better glycemic control (36.4%) or non-adherence issues (63.6%); the remaining 47.6% persisted with poor glycemic control despite good adherence and were scaled to SAP. Among the 30 suitable candidates for SAP therapy, 60% did not reach the HbA1c goal and 40% had either hypoglycemic episodes (severe or persistent) or dawn phenomenon. The overall non-adherence rate was 33.3%. CONCLUSIONS: CDP optimized the selection of suitable candidates for SAP by improving glycemic control and identifying adherence issues early. These results provide evidence of the impact of the implementation of patient selection and educational protocols in the real-life setting of a highly experienced clinic.

Modeling the dynamic behavior of biochemical regulatory networks.

Strategies for modeling the complex dynamical behavior of gene/protein regulatory networks have evolved over the last 50 years as both the knowledge of these molecular control systems and the power of computing resources have increased. Here, we review a number of common modeling approaches, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches). We discuss the pro's and con's of each approach, to help novice modelers choose a modeling strategy suitable to their problem, based on the type and bounty of available experimental information. We illustrate different modeling strategies in terms of some abstract network motifs, and in the specific context of cell cycle regulation.

Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients.

Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.

Boolean modeling techniques for protein co-expression networks in systems medicine.

INTRODUCTION: Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type. AREAS COVERED: This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It's also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed. Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).

Programmatic access to logical models in the Cell Collective modeling environment via a REST API.

UNLABELLED: Cell Collective (www.cellcollective.org) is a web-based interactive environment for constructing, simulating and analyzing logical models of biological systems. Herein, we present a Web service to access models, annotations, and simulation data in the Cell Collective platform through the Representational State Transfer (REST) Application Programming Interface (API). The REST API provides a convenient method for obtaining Cell Collective data through almost any programming language. To ensure easy processing of the retrieved data, the request output from the API is available in a standard JSON format. AVAILABILITY AND IMPLEMENTATION: The Cell Collective REST API is freely available at http://thecellcollective.org/tccapi. All public models in Cell Collective are available through the REST API. For users interested in creating and accessing their own models through the REST API first need to create an account in Cell Collective (http://thecellcollective.org). CONTACT: thelikar2@unl.edu. SUPPLEMENTARY INFORMATION: Technical user documentation: https://goo.gl/U52GWo.

Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4(+) T-lymphocytes.

Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4(+) T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4(+) immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4(+) T-cell model was constructed and simulated under CAV1 (+/+), CAV1 (+/-), and CAV1 (-/-) conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4(+) T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4(+) T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia.

Modelos lógicos em avaliação de sistemas instrucionais: dois estudos de caso / Logical models for Instructional systems evaluation: two case studies

O objetivo deste artigo é discutir os principais desafios e benefícios associados à adoção de modelos lógicos em avaliação de sistemas instrucionais. Análises da produção de conhecimentos nesta área mostraram lacunas na investigação de relacionamentos entre efeitos de programas no nível dos egressos e impactos indiretos desses programas instrucionais sobre a organização. Essas lacunas nas pesquisas estimularam a adoção de estratégias metodológicas que facilitassem a compreensão da realidade dos programas. Os modelos lógicos são ferramentas eficazes na avaliação de programas sociais e governamentais. Este artigo apresenta, a título de demonstração empírica, dois casos de aplicação bem-sucedida da abordagem de modelos lógicos em avaliação de sistemas instrucionais. O primeiro apresenta a avaliação de um treinamento corporativo e o segundo, a avaliação de um mestrado profissional. Nesses estudos foram realizadas diversas etapas de pesquisa qualitativa, a partir das quais foram confeccionados modelos lógicos e figuras, que resumem a teoria do programa, tal como concebida pelos stakeholders, participantes da pesquisa. As pesquisas são descritas brevemente, de modo a ilustrar a aplicação de modelos lógicos nos dois contextos estudados. Ao final, são discutidos benefícios e limitações dessa abordagem na avaliação de sistemas instrucionais. (AU)

The aim of this article is to discuss the main challenges and benefits associated with the adoption of logical models to evaluate instructional systems. Analyses of the production of knowledge in this area have shown gaps in the investigation of relationships between program effects for egresses as well as the indirect impacts of these instructional programs on the organization. These gaps in the research have stimulated the adoption of methodological strategies to improve the understanding of the reality of such programs. Logical models are effective tools for the evaluation of social and governmental programs. This article presents, by way of empirical demonstration, two cases in which logical models were successfully used for instructional systems evaluation. The first presents the evaluation of a corporate training course and the second the evaluation of a professional master's. Several qualitative research steps were conducted in these studies and from them logical models were created. These models summarize the theory of the program, as conceived by the stakeholders, who were also participants in the study. The studies are described briefly, to illustrate the application of the logical models in these two contexts. Finally, the benefits and limitations of this approach for the evaluation of instructional systems are discussed. (AU)

Modelos lógicos em avaliação de sistemas instrucionais: dois estudos de caso / Logical models for Instructional systems evaluation: two case studies

O objetivo deste artigo é discutir os principais desafios e benefícios associados à adoção de modelos lógicos em avaliação de sistemas instrucionais. Análises da produção de conhecimentos nesta área mostraram lacunas na investigação de relacionamentos entre efeitos de programas no nível dos egressos e impactos indiretos desses programas instrucionais sobre a organização. Essas lacunas nas pesquisas estimularam a adoção de estratégias metodológicas que facilitassem a compreensão da realidade dos programas. Os modelos lógicos são ferramentas eficazes na avaliação de programas sociais e governamentais. Este artigo apresenta, a título de demonstração empírica, dois casos de aplicação bem-sucedida da abordagem de modelos lógicos em avaliação de sistemas instrucionais. O primeiro apresenta a avaliação de um treinamento corporativo e o segundo, a avaliação de um mestrado profissional. Nesses estudos foram realizadas diversas etapas de pesquisa qualitativa, a partir das quais foram confeccionados modelos lógicos e figuras, que resumem a teoria do programa, tal como concebida pelos stakeholders, participantes da pesquisa. As pesquisas são descritas brevemente, de modo a ilustrar a aplicação de modelos lógicos nos dois contextos estudados. Ao final, são discutidos benefícios e limitações dessa abordagem na avaliação de sistemas instrucionais.

The aim of this article is to discuss the main challenges and benefits associated with the adoption of logical models to evaluate instructional systems. Analyses of the production of knowledge in this area have shown gaps in the investigation of relationships between program effects for egresses as well as the indirect impacts of these instructional programs on the organization. These gaps in the research have stimulated the adoption of methodological strategies to improve the understanding of the reality of such programs. Logical models are effective tools for the evaluation of social and governmental programs. This article presents, by way of empirical demonstration, two cases in which logical models were successfully used for instructional systems evaluation. The first presents the evaluation of a corporate training course and the second the evaluation of a professional master's. Several qualitative research steps were conducted in these studies and from them logical models were created. These models summarize the theory of the program, as conceived by the stakeholders, who were also participants in the study. The studies are described briefly, to illustrate the application of the logical models in these two contexts. Finally, the benefits and limitations of this approach for the evaluation of instructional systems are discussed.