2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014
We propose a model-based systems engineering approach for representing and analyzing the percepti... more We propose a model-based systems engineering approach for representing and analyzing the perception and consideration of the presence of external entities by systems. This approach is inspired by the conceptual model-based systems biology paradigm we have previously proposed. The notion of presence and absence of surrounding objects, or occurrence of surrounding processes, is becoming ever more critical in a world of highly-aware automation agents and the increasingly complex and stochastic environmental configurations in which these agents act. This problem is well-known in the biological modeling domain. System models usually focus on nominal, mainstream aspects and scenarios. Presence-related aspects are often assumed to pose no issue, and remain unattended in both the conceptual and actual system design phases. The Pareto rule-ofthumb of system design asserts that about 20% of design and development efforts cover the nominal, "sunny day" process, while the other 80% cover exceptions, alternative scenarios, failure modes, edge conditions, special settings, and emergencies. We propose an approach to extend the naïve, nominal system model by enriching it with notions of presence-awareness and sensitivity. This approach is facilitated by Object-Process Methodology, a holistic system and process modeling framework, currently being adopted as ISO standard 19450.
Biologists are required to integrate large amounts of data to construct a working model of the sy... more Biologists are required to integrate large amounts of data to construct a working model of the system under investigation. This model is often informal and stored mentally or textually, making it prone to contain undetected inconsistencies, inaccuracies, or even contradictions, not much less than a representation in free natural language. Using Object-Process Methodology (OPM), a formal yet visual and humanly accessible conceptual modeling language, we have created an executable working model of the mRNA decay process in Saccharomyces cerevisiae, as well as the import of its components to the nucleus following mRNA decay. We show how our model, which incorporates knowledge from 43 articles, can reproduce outcomes that match the experimental findings, evaluate hypotheses, and predict new possible outcomes. Moreover, we were able to analyze the effects of the mRNA decay model perturbations related to gene and interaction deletions, and predict the nuclear import of certain decay factors, which we then verified experimentally. In particular, we verified experimentally the hypothesis that Rpb4p, Lsm1p, and Pan2p remain bound to the RNA 39-untralslated region during the entire process of the 59 to 39 degradation of the RNA open reading frame. The model has also highlighted erroneous hypotheses that indeed were not in line with the experimental outcomes. Beyond the scientific value of these specific findings, this work demonstrates the value of the conceptual model as an in silico vehicle for hypotheses generation and testing, which can reinforce, and often even replace, risky, costlier wet lab experiments.
Modeling plays an increasingly important role in the life-cycle of systems, starting from the des... more Modeling plays an increasingly important role in the life-cycle of systems, starting from the design stage, as well as important in the process of studying an unfamiliar, existing system. Complex systems are difficult to model, making it harder on users to deeply understand their intricate behavior. Moreover, in many cases the system may exhibit complex computational and stochastic behavior, critical to truly representing the system. Existing modeling methodologies contain behavioral diagrams aimed to describe the systems' changes over time, but they are still static and do not reflect the behavior of systems in spatio-temporal space in a manner that is close to conceived reality. Similarly, these methodologies do not offer an ability to fully model the quantitative aspects of the system, or offer such ability at the expense of simplicity or generality of the methodology. We offer two complementing concepts, Vivid OPM and OPM Matlab Layer, which address the dynamic display and quantitative aspects by enhancing Object Process Methodology (OPM) to better reflects these aspects while keeping the holism and simplicity of OPM.
Exceptions in safety-critical systems must be addressed during conceptual design and risk analysi... more Exceptions in safety-critical systems must be addressed during conceptual design and risk analysis. We developed a conceptual model of exceptions, a methodology for eliciting and modeling exceptions, and templates for modeling them in an extension of the Object Process Methodology (OPM)a system analysis and design methodology and language that uses a single graphical model for describing systems, including their timing exceptions, which has been shown to be an effective modeling methodology. Using an antibiotics treatment guideline as a case study, we demonstrate the value of our approach in eliciting and modeling exceptions that occur in clinical care systems.
We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing... more We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system’s structure— the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model.
2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014
We propose a model-based systems engineering approach for representing and analyzing the percepti... more We propose a model-based systems engineering approach for representing and analyzing the perception and consideration of the presence of external entities by systems. This approach is inspired by the conceptual model-based systems biology paradigm we have previously proposed. The notion of presence and absence of surrounding objects, or occurrence of surrounding processes, is becoming ever more critical in a world of highly-aware automation agents and the increasingly complex and stochastic environmental configurations in which these agents act. This problem is well-known in the biological modeling domain. System models usually focus on nominal, mainstream aspects and scenarios. Presence-related aspects are often assumed to pose no issue, and remain unattended in both the conceptual and actual system design phases. The Pareto rule-ofthumb of system design asserts that about 20% of design and development efforts cover the nominal, "sunny day" process, while the other 80% cover exceptions, alternative scenarios, failure modes, edge conditions, special settings, and emergencies. We propose an approach to extend the naïve, nominal system model by enriching it with notions of presence-awareness and sensitivity. This approach is facilitated by Object-Process Methodology, a holistic system and process modeling framework, currently being adopted as ISO standard 19450.
Biologists are required to integrate large amounts of data to construct a working model of the sy... more Biologists are required to integrate large amounts of data to construct a working model of the system under investigation. This model is often informal and stored mentally or textually, making it prone to contain undetected inconsistencies, inaccuracies, or even contradictions, not much less than a representation in free natural language. Using Object-Process Methodology (OPM), a formal yet visual and humanly accessible conceptual modeling language, we have created an executable working model of the mRNA decay process in Saccharomyces cerevisiae, as well as the import of its components to the nucleus following mRNA decay. We show how our model, which incorporates knowledge from 43 articles, can reproduce outcomes that match the experimental findings, evaluate hypotheses, and predict new possible outcomes. Moreover, we were able to analyze the effects of the mRNA decay model perturbations related to gene and interaction deletions, and predict the nuclear import of certain decay factors, which we then verified experimentally. In particular, we verified experimentally the hypothesis that Rpb4p, Lsm1p, and Pan2p remain bound to the RNA 39-untralslated region during the entire process of the 59 to 39 degradation of the RNA open reading frame. The model has also highlighted erroneous hypotheses that indeed were not in line with the experimental outcomes. Beyond the scientific value of these specific findings, this work demonstrates the value of the conceptual model as an in silico vehicle for hypotheses generation and testing, which can reinforce, and often even replace, risky, costlier wet lab experiments.
Modeling plays an increasingly important role in the life-cycle of systems, starting from the des... more Modeling plays an increasingly important role in the life-cycle of systems, starting from the design stage, as well as important in the process of studying an unfamiliar, existing system. Complex systems are difficult to model, making it harder on users to deeply understand their intricate behavior. Moreover, in many cases the system may exhibit complex computational and stochastic behavior, critical to truly representing the system. Existing modeling methodologies contain behavioral diagrams aimed to describe the systems' changes over time, but they are still static and do not reflect the behavior of systems in spatio-temporal space in a manner that is close to conceived reality. Similarly, these methodologies do not offer an ability to fully model the quantitative aspects of the system, or offer such ability at the expense of simplicity or generality of the methodology. We offer two complementing concepts, Vivid OPM and OPM Matlab Layer, which address the dynamic display and quantitative aspects by enhancing Object Process Methodology (OPM) to better reflects these aspects while keeping the holism and simplicity of OPM.
Exceptions in safety-critical systems must be addressed during conceptual design and risk analysi... more Exceptions in safety-critical systems must be addressed during conceptual design and risk analysis. We developed a conceptual model of exceptions, a methodology for eliciting and modeling exceptions, and templates for modeling them in an extension of the Object Process Methodology (OPM)a system analysis and design methodology and language that uses a single graphical model for describing systems, including their timing exceptions, which has been shown to be an effective modeling methodology. Using an antibiotics treatment guideline as a case study, we demonstrate the value of our approach in eliciting and modeling exceptions that occur in clinical care systems.
We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing... more We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system’s structure— the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model.
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Papers by Judith Somekh
knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a
graphical and textual executable modeling language. OPM enables concurrent representation of the system’s structure—
the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down
approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this
high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach
is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding,
enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological
processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling
and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various
types. We also show how model execution enhances a coherent model construction. Identification and pinpointing
knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether
conjectures about uncertain mechanisms fit into the already verified model.
knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a
graphical and textual executable modeling language. OPM enables concurrent representation of the system’s structure—
the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down
approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this
high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach
is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding,
enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological
processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling
and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various
types. We also show how model execution enhances a coherent model construction. Identification and pinpointing
knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether
conjectures about uncertain mechanisms fit into the already verified model.