Abstract
Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.
Highlights
With the goal of understanding the complexities of various biological processes, computational modeling is an important part of Systems Biology
Case study: The regulatory mechanism of regulatory mechanism of the (Rac) Biological interactions defined using the Bio-Logic builder are described by Boolean expressions that users build by using qualitative descriptives generally used by laboratory scientists to explain the interaction from experimental studies
The lack of simple-to-use tools for creating/editing and simulating computational models plays a significant role in the gap that exists between the computational and experimental sides of biomedical research [6]
Summary
With the goal of understanding the complexities of various biological processes, computational modeling is an important part of Systems Biology. Despite the excitement around computational systems biology and its potential, it has been difficult to fully utilize modeling as part of laboratory research. This is largely due to a significant gap between the computational and experimental sides of the science [1]. In order to couple computational models more closely with experimental studies, software tools to build and simulate models in a non-mathematical fashion will be required to bridge this gap. While some tools (e.g., GINSim [7] or Genetic Network Analyzer [8]) allow users to ‘‘draw’’ logical models, for systems with more complex interactions, users are required to manually define the models’ underlying mathematics
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