Model Revision of Logical Regulatory Networks Using Logic-Based Tools

Recently, biological data has been increasingly produced calling for the existence of computational models able to organize and computationally reproduce existing observations. In particular, biological regulatory networks have been modeled relying on the Sign Consistency Model or the logical formalism. However, their construction still completely relies on a domain expert to choose the best functions for every network component. Due to the number of possible functions for k arguments, this is typically a process prone to error. Here, we propose to assist the modeler using logic-based tools to verify the model, identifying crucial network components responsible for model inconsistency. We intend to obtain a model building procedure capable of providing the modeler with repaired models satisfying a set of pre-defined criteria, therefore minimizing possible modeling errors

Revision of Boolean Logical Models of Biological Regulatory Networks using Answer-Set Programming

Biological regulatory networks are one of the most prominent tools used to represent complex, regulatory cellular processes. Creating computational models of these networks is key to better comprehend the corresponding cellular processes, as they allow for the reproduction of known behaviors, the testing of hypotheses, and the identification of predictions in silico. However, given that the process of constructing and revising such models is mainly a manual one, it is prone to error, and would therefore benefit from automation. An attempt at solving this problem has already been made using a mixture of Answer Set Programming (ASP) and C++. The previous attempt automated the process of revising these models, by using ASP to verify whether a Boolean logical model of a biological regulatory network was consistent with a given set of experimental observations and, in case of inconsistencies, used C++ to implement an algorithm capable of searching for possible sets of repair operations to render the model consistent. In our work we propose an alternative solution for this problem, a solution that fully leverages ASP which, being a declarative language tailored for this type of difficult search problems, has demonstrated to be a great tool to use both for consistency checking as well as model repair. This is in view of the fact that ASP offers a more intuitive and elaboration-tolerant programming style, which facilitates the processes of understanding, and modifying the code behind the model revision process. This, coupled with the powerful and exhaustively optimized solving capabilities provided by the state of the art ASP system clingo, has shown that there is great potential in adopting a fully ASP-based approach to aid in the automation of the revision of Boolean logical models. In this thesis we present the tool that we have developed to automate the process of revising Boolean logical models of Biological Regulatory Network(s) (BRN), which uses ASP to search for inconsistencies and perform repairs on these models.As redes reguladoras biológicas são das ferramentas mais proeminentes usadas para representar processos celulares regulatórios complexos. A criação de modelos computacionais destas redes é fundamental para entender melhor os processos celulares correspondentes, pois permitem reproduzir comportamentos conhecidos, testar hipóteses e identificar previsões in silico. Porém, dado que o processo de construção e revisão destes modelos é principalmente manual, torna-se propenso a erros e, logo, beneficiaria de automação. Já foi feita uma tentativa de resolução deste problema usando uma mistura de Programação por Conjuntos de Resposta (ASP) com C++. A tentativa anterior automatizou o processo de revisão destes modelos, usando ASP para verificar se um modelo lógico booleano de uma rede regulatória é consistente com um determinado conjunto de observações experimentais e, caso inconsistências se verifiquem, é utilizado um algoritmo desenvolvido em C++ capaz de encontrar possíveis conjuntos de operações de reparo para tornar o modelo consistente. No nosso trabalho, propomos uma solução alternativa para este problema, que tira completo partido da utilização ASP que, sendo uma linguagem declarativa adaptada a este tipo de problemas de busca difíceis, demonstrou ser uma excelente ferramenta a utilizar tanto para a verificação da consistência como para a reparação de modelos. Tal deve-se ao facto de ASP oferecer um estilo de programação mais intuitivo e tolerante à elaboração, o que facilita os processos de compreensão, e a modificação do código por detrás do processo de revisão de modelos. Isto, juntamente com as poderosas e otimizadas capacidades de resolução de problemas de busca oferecidas pelo sistema ASP de última geração clingo, demonstrou que existe um grande potencial na adopção de um sistema totalmente baseado em ASP para ajudar na automatização da revisão destes modelos. Nesta tese apresentamos a ferramenta que desenvolvemos para automatizar o processo de revisão de modelos lógicos booleanos de redes reguladoras biológicas (BRN), que utiliza ASP para procurar inconsistências e efectuar reparações nestes modelos

A practical guide to mechanistic systems modeling in biology using a logic-based approach

Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h

Model Checking to Assess T-Helper Cell Plasticity

Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.LabEx MemoLife, Ecole Normale Supérieure, FCT grants: (PEst-OE/EEI/LA0021/2013, IF/01333/2013), Ph.D.program of the Agence National de Recherche sur Le Sida (ANRS), European Research Council consolidator grant

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Global Talentship: Toward a Decision Science Connecting Talent to Global Strategic Success

It is widely accepted that global competitive advantage frequently requires managing such complex situations that traditional organization and job structures are simply insufficient. Increasingly, in order to create a flexible and integrated set of decisions that balance local flexibility with global efficiency, organizations must rely on more social, informal and matrix-based shared visions among managers and employees. Research on global strategic advantage, global organizational structures, and even shared mindsets has suggested that dimensions of culture, product and function provide a valuable organizing framework. However, typical decisions about organization structure, HRM practices and talent often remain framed at such a high level as to preclude their solution. We maintain that there is often no logical answer to such questions as, “Should the sales force be local or global?” or “Should product authority rest with the countries or the corporate center?” However, we propose that embedding business processes or value chains within a Culture and Product matrix provides the necessary analytic detail to reveal otherwise elusive solutions. Moreover, by linking this global process matrix to a model that bridges strategy and talent, it is possible to identify global “pivotal talent pools,” and to target organizational and human resource investments toward those talent areas that have the greatest impact on strategic advantage. We demonstrate the Value-Chain, Culture and Product (VCCP) matrix using several examples, and discuss future research and practical implications, particularly for leadership and leadership development

The Semantic Web Revisited

The original Scientific American article on the Semantic Web appeared in 2001. It described the evolution of a Web that consisted largely of documents for humans to read to one that included data and information for computers to manipulate. The Semantic Web is a Web of actionable information--information derived from data through a semantic theory for interpreting the symbols.This simple idea, however, remains largely unrealized. Shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community presaging ultimate success for the Semantic Web--just as the use of HTTP within the CERN particle physics community led to the revolutionary success of the original Web. This article is part of a special issue on the Future of AI

ISO 50001: 2018 and Its Application in a Comprehensive Management System with an Energy-Performance Focus

[EN] Business progress and human development are linked to the efficient management of energy resources. The research in this paper contributes to the generalized application of good practices that reduce the vulnerability of companies. The research focuses on energy efficiency through comprehensive management systems (CMS), and "thought based on risks and opportunities", considering the discussion about the revision of ISO 50001:2018, the basic approach of the model and the route to implement CMS for quality, safety and health in the workplace, environmental management, energy efficiency, and other risk components. This implementation route, with the acronym CMS QHSE3+, places special emphasis on the functions of strategic planning, operational and risk management, and controls, as well as on deliverables and references to examples, templates, standards, and documents, to facilitate its application general in small and medium enterprises and in the management of energy efficiency.We express our gratitude for the support received, to CAJACOPI ATLÁNTICO, QUARA Group, ASTEQ Technology, Simón Bolivar University, the Universitat Politècnica de València, SANTO TORIBIO Business Group, and to all the personalities and companies who offered us their contributions and their valuable points of view.Poveda-Orjuela, PP.; García-Díaz, JC.; Pulido-Rojano, A.; Cañón-Zabala, G. (2019). ISO 50001: 2018 and Its Application in a Comprehensive Management System with an Energy-Performance Focus. Energies. 12(24):1-33. https://doi.org/10.3390/en12244700S1331224Strategic Business Plan 2017https://isotc.iso.org/livelink/livelink/fetch/2000/2122/687806/ISO_ TC_301_Energy_management_and_energy_saving.pdf?nodeid=19278553&vernum=-2Directives and Policies Ninth Edition, Part 2. Official Rules to Develop an ISO Standardwww.iso.org/directives-and-policies.htmlStandards by ISO/TC 301https://www.iso.org/committee/6077221.htmlCEM Advancing Clean Energy Together, Ministerial Meetingshttps://www.cleanenergyministerial.org/events-Clean-Energy-Ministerial. CEM 01 to CEM 10Organization for Economic Co-Operation and Development OECDhttps://stats.oecd.org/index.aspx?queryid=70734Strategic Plan 2016–2020. Bruxelles: CEEhttps://trade.ec.europa.eu/doclib/docs/2016/august/tradoc_154919.pdfChaos Reporthttp://www.laboratorioti.com /2016/05/16/informe-del-caos-2015-chaos-report-2015-bien-malfueron-los-proyectos-ano-2015/Oliva, F. L. (2016). A maturity model for enterprise risk management. International Journal of Production Economics, 173, 66-79. doi:10.1016/j.ijpe.2015.12.007Thekdi, S., & Aven, T. (2016). An enhanced data-analytic framework for integrating risk management and performance management. Reliability Engineering & System Safety, 156, 277-287. doi:10.1016/j.ress.2016.07.010Aven, T., & Krohn, B. S. (2014). A new perspective on how to understand, assess and manage risk and the unforeseen. Reliability Engineering & System Safety, 121, 1-10. doi:10.1016/j.ress.2013.07.005Wilson, J. P., & Campbell, L. (2018). ISO 9001:2015: the evolution and convergence of quality management and knowledge management for competitive advantage. Total Quality Management & Business Excellence, 31(7-8), 761-776. doi:10.1080/14783363.2018.1445965Ciravegna Martins da Fonseca, L. M. (2015). ISO 14001:2015: An improved tool for sustainability. Journal of Industrial Engineering and Management, 8(1). doi:10.3926/jiem.1298Cosgrove, J., Littlewood, J., & Wilgeroth, P. (2017). Development of a framework of key performance indicators to identify reductions in energy consumption in a medical devices production facility. International Journal of Ambient Energy, 39(2), 202-210. doi:10.1080/01430750.2017.1278718Castrillón Mendoza, R., Rey Hernández, J., Velasco Gómez, E., San José Alonso, J., & Rey Martínez, F. (2018). Analysis of the Methodology to Obtain Several Key Indicators Performance (KIP), by Energy Retrofitting of the Actual Building to the District Heating Fuelled by Biomass, Focusing on nZEB Goal: Case of Study. Energies, 12(1), 93. doi:10.3390/en12010093Chiu, T.-Y., Lo, S.-L., & Tsai, Y.-Y. (2012). Establishing an Integration-Energy-Practice Model for Improving Energy Performance Indicators in ISO 50001 Energy Management Systems. Energies, 5(12), 5324-5339. doi:10.3390/en5125324Laskurain, I., Ibarloza, A., Larrea, A., & Allur, E. (2017). Contribution to Energy Management of the Main Standards for Environmental Management Systems: The Case of ISO 14001 and EMAS. Energies, 10(11), 1758. doi:10.3390/en10111758Al-Sakkaf, S., Kassas, M., Khalid, M., & Abido, M. A. (2019). An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch. Energies, 12(8), 1457. doi:10.3390/en12081457Zobel, T., & Malmgren, C. (2016). Evaluating the Management System Approach for Industrial Energy Efficiency Improvements. Energies, 9(10), 774. doi:10.3390/en9100774Laskurain, I., Heras-Saizarbitoria, I., & Casadesús, M. (2015). Fostering renewable energy sources by standards for environmental and energy management. Renewable and Sustainable Energy Reviews, 50, 1148-1156. doi:10.1016/j.rser.2015.05.050Stoeglehner, G., Niemetz, N., & Kettl, K.-H. (2011). Spatial dimensions of sustainable energy systems: new visions for integrated spatial and energy planning. Energy, Sustainability and Society, 1(1). doi:10.1186/2192-0567-1-2Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273-287. doi:10.1016/j.rser.2015.10.133Blaauwbroek, N., Nguyen, P. H., Konsman, M. J., Shi, H., Kamphuis, R. I. G., & Kling, W. L. (2015). Decentralized Resource Allocation and Load Scheduling for Multicommodity Smart Energy Systems. IEEE Transactions on Sustainable Energy, 6(4), 1506-1514. doi:10.1109/tste.2015.2441107Mao, M., Jin, P., Hatziargyriou, N. D., & Chang, L. (2014). Multiagent-Based Hybrid Energy Management System for Microgrids. IEEE Transactions on Sustainable Energy, 1-1. doi:10.1109/tste.2014.2313882Carli, R., & Dotoli, M. (2019). Decentralized control for residential energy management of a smart users microgrid with renewable energy exchange. IEEE/CAA Journal of Automatica Sinica, 6(3), 641-656. doi:10.1109/jas.2019.1911462The ISO 27k Forumhttps://www.iso27001 security.com/html/iso27000.htmlIntroduction to the Basic Concepts of General Systems Theory. Cinta de Moebiohttp://www.redalyc.org/articulo.oa?id=10100306Von Bertalanffy, L. (1950). The Theory of Open Systems in Physics and Biology. Science, 111(2872), 23-29. doi:10.1126/science.111.2872.23Hernandis Ortuño, B., & Briede Westermeyer, J. C. (2009). AN EDUCATIONAL APPLICATION FOR A PRODUCT DESIGN AND ENGINEERING SYSTEMS USING INTEGRATED CONCEPTUAL MODELS. Ingeniare. Revista chilena de ingeniería, 17(3). doi:10.4067/s0718-33052009000300017Howard, T. J., Culley, S. J., & Dekoninck, E. (2008). Describing the creative design process by the integration of engineering design and cognitive psychology literature. Design Studies, 29(2), 160-180. doi:10.1016/j.destud.2008.01.001Conceptual Model and Route to Implement a Comprehensive Management System QHSE3+, in New Trends in Operations Research and Administrative Sciences. An Approach from Latin American Studieshttps://bonga.unisimon.edu.co/handle/20.500.12442/2601Golini, R., Kalchschmidt, M., & Landoni, P. (2015). Adoption of project management practices: The impact on international development projects of non-governmental organizations. International Journal of Project Management, 33(3), 650-663. doi:10.1016/j.ijproman.2014.09.006Marcelino-Sádaba, S., González-Jaen, L. F., & Pérez-Ezcurdia, A. (2015). Using project management as a way to sustainability. From a comprehensive review to a framework definition. Journal of Cleaner Production, 99, 1-16. doi:10.1016/j.jclepro.2015.03.020Archer, N. ., & Ghasemzadeh, F. (1999). An integrated framework for project portfolio selection. International Journal of Project Management, 17(4), 207-216. doi:10.1016/s0263-7863(98)00032-5Velásquez-Restrepo, S. M., Londoño-Gallego, J. A., López-Romero, C., & Vahos, J. D. (2018). Desarrollo de una plataforma web multimedial para la elaboración de proyectos bajo la metodología de marco lógico. Lámpsakos, 1(18), 12. doi:10.21501/21454086.2601Crawford, P., & Bryce, P. (2003). Project monitoring and evaluation: a method for enhancing the efficiency and effectiveness of aid project implementation. International Journal of Project Management, 21(5), 363-373. doi:10.1016/s0263-7863(02)00060-1San Cristóbal, J. R., Carral, L., Diaz, E., Fraguela, J. A., & Iglesias, G. (2018). Complexity and Project Management: A General Overview. Complexity, 2018, 1-10. doi:10.1155/2018/4891286Ramasesh, R. V., & Browning, T. R. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management, 32(4), 190-204. doi:10.1016/j.jom.2014.03.003Pollack, J. (2007). The changing paradigms of project management. International Journal of Project Management, 25(3), 266-274. doi:10.1016/j.ijproman.2006.08.002Lamers, M. (2002). Do you manage a project, or what? A reply to «Do you manage work, deliverables or resources», International Journal of Project Management, April 2000. International Journal of Project Management, 20(4), 325-329. doi:10.1016/s0263-7863(00)00053-3Torabi, S. A., Giahi, R., & Sahebjamnia, N. (2016). An enhanced risk assessment framework for business continuity management systems. Safety Science, 89, 201-218. doi:10.1016/j.ssci.2016.06.015Baccarini, D. (1999). The Logical Framework Method for Defining Project Success. Project Management Journal, 30(4), 25-32. doi:10.1177/875697289903000405Casals, M., Gangolells, M., Forcada, N., Macarulla, M., Giretti, A., & Vaccarini, M. (2016). SEAM4US: An intelligent energy management system for underground stations. Applied Energy, 166, 150-164. doi:10.1016/j.apenergy.2016.01.029Matrawy, K. K., Mahrous, A.-F., & Youssef, M. S. (2015). Energy management and parametric optimization of an integrated PV solar house. Energy Conversion and Management, 96, 377-383. doi:10.1016/j.enconman.2015.02.088Kyriakarakos, G., Dounis, A. I., Arvanitis, K. G., & Papadakis, G. (2012). A fuzzy logic energy management system for polygeneration microgrids. Renewable Energy, 41, 315-327. doi:10.1016/j.renene.2011.11.019Johansson, M. T., & Thollander, P. (2018). A review of barriers to and driving forces for improved energy efficiency in Swedish industry– Recommendations for successful in-house energy management. Renewable and Sustainable Energy Reviews, 82, 618-628. doi:10.1016/j.rser.2017.09.052Jovanović, B., & Filipović, J. (2016). ISO 50001 standard-based energy management maturity model – proposal and validation in industry. Journal of Cleaner Production, 112, 2744-2755. doi:10.1016/j.jclepro.2015.10.023Majernik, M., Bosak, M., Stofova, L., & Szaryszova, P. (2015). INNOVATIVE MODEL OF INTEGRATED ENERGY MANAGEMENT IN COMPANIES. Quality Innovation Prosperity, 19(1). doi:10.12776/qip.v19i1.384Implementation of ISO 50001 in Industry in The Netherlands. ECEE Industry Summer Studywww.eceee.orgDe Groot, H. L. F., Verhoef, E. T., & Nijkamp, P. (2001). Energy saving by firms: decision-making, barriers and policies. Energy Economics, 23(6), 717-740. doi:10.1016/s0140-9883(01)00083-4Development of the EMAS Sectoral Reference Documents on Best Environmental Management Practice. Learning from Frontrunners Promoting Best Practice. Publications Office of the European Unionhttps://publications.jrc.ec.europa.eu/repository/bitstream/JRC84966/lfna26291enn.pd