Abstract
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.
Highlights
A myriad of detailed pieces of knowledge regarding the structure and function of the living cell have been accumulating at an ever increasing rate while emphasis in biological research has shifted from probing into a single molecular function to studying complete cellular pathways, cycles and the entire cell as a system
We propose a classification of knowledge gaps that might arise as a result of qualitative conceptual modeling of molecular biology system mechanisms
We have proposed a Conceptual Model-based Systems Biology framework
Summary
A myriad of detailed pieces of knowledge regarding the structure and function of the living cell have been accumulating at an ever increasing rate while emphasis in biological research has shifted from probing into a single molecular function to studying complete cellular pathways, cycles and the entire cell as a system. In order to better understand the expression of protein-encoding genes, we need to consider the entire multi-stage process, as each stage can be regarded as a subdivision of a continuous cyclical gene expression process. This realization calls for adopting a holistic, integrative, Conceptual Model-based Systems Biology that would enable making mechanistic system-level sense of the countless pieces of information that have been gathered thanks to decades of meticulous laboratory research by many thousands of scientists. In this paper we propose a framework for concurrently modeling structural and behavioral aspects of molecular biology systems and address the challenges of a coherent mechanistic model construction, its execution, and related knowledge gaps discovery and elicitation
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