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1.  Integrating Interactive Computational Modeling in Biology Curricula 
PLoS Computational Biology  2015;11(3):e1004131.
While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by “building and breaking it” via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the “Vision and Change” call to action in undergraduate biology education by providing a hands-on approach to biology.
PMCID: PMC4366374  PMID: 25790483
2.  Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions 
BMC Systems Biology  2014;8:92.
An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.
We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes.
The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure.
PMCID: PMC4363947  PMID: 25189194
Information fusion; Node attributes; Signal transduction; Nonadditive set functions; Choquet integral; Sensitivity
3.  A Comprehensive, Multi-Scale Dynamical Model of ErbB Receptor Signal Transduction in Human Mammary Epithelial Cells 
PLoS ONE  2013;8(4):e61757.
The non-receptor tyrosine kinase Src and receptor tyrosine kinase epidermal growth factor receptor (EGFR/ErbB1) have been established as collaborators in cellular signaling and their combined dysregulation plays key roles in human cancers, including breast cancer. In part due to the complexity of the biochemical network associated with the regulation of these proteins as well as their cellular functions, the role of Src in EGFR regulation remains unclear. Herein we present a new comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells. This model, constructed manually from published biochemical literature, consists of 245 nodes representing proteins and their post-translational modifications sites, and over 1,000 biochemical interactions. Using computer simulations of the model, we find it is able to reproduce a number of cellular phenomena. Furthermore, the model predicts that overexpression of Src results in increased endocytosis of EGFR in the absence/low amount of the epidermal growth factor (EGF). Our subsequent laboratory experiments also suggest increased internalization of EGFR upon Src overexpression under EGF-deprived conditions, further supporting this model-generated hypothesis.
PMCID: PMC3630219  PMID: 23637902
4.  Bio-Logic Builder: A Non-Technical Tool for Building Dynamical, Qualitative Models 
PLoS ONE  2012;7(10):e46417.
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.
PMCID: PMC3474764  PMID: 23082121
5.  The Cell Collective: Toward an open and collaborative approach to systems biology 
BMC Systems Biology  2012;6:96.
Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety.
The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time.
The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction.
PMCID: PMC3443426  PMID: 22871178
6.  ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists 
BMC Systems Biology  2009;3:58.
New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.
We have developed a Boolean network-based simulation and analysis software tool, ChemChains, which combines the advantages of the parameter-free nature of logical models while providing the ability for users to interact with their models in a continuous manner, similar to the way laboratory biologists interact with laboratory data. ChemChains allows users to simulate models in an automatic fashion under tens of thousands of different external environments, as well as perform various mutational studies.
ChemChains combines the advantages of logical and continuous modeling and provides a way for laboratory biologists to perform in silico experiments on mathematical models easily, a necessary component of laboratory research in the systems biology era.
PMCID: PMC2705353  PMID: 19500393
7.  Regulation of c-Fes Tyrosine Kinase and Biological Activities by N-Terminal Coiled-Coil Oligomerization Domains 
Molecular and Cellular Biology  1999;19(12):8335-8343.
The cytoplasmic protein-tyrosine kinase Fes has been implicated in cytokine signal transduction, hematopoiesis, and embryonic development. Previous work from our laboratory has shown that active Fes exists as a large oligomeric complex in vitro. However, when Fes is expressed in mammalian cells, its kinase activity is tightly repressed. The Fes unique N-terminal sequence has two regions with strong homology to coiled-coil-forming domains often found in oligomeric proteins. Here we show that disruption or deletion of the first coiled-coil domain upregulates Fes tyrosine kinase and transforming activities in Rat-2 fibroblasts and enhances Fes differentiation-inducing activity in myeloid leukemia cells. Conversely, expression of a Fes truncation mutant consisting only of the unique N-terminal domain interfered with Rat-2 fibroblast transformation by an activated Fes mutant, suggesting that oligomerization is essential for Fes activation in vivo. Coexpression with the Fes N-terminal region did not affect the transforming activity of v-Src in Rat-2 cells, arguing against a nonspecific suppressive effect. Taken together, these findings suggest a model in which Fes activation may involve coiled-coil-mediated interconversion of monomeric and oligomeric forms of the kinase. Mutation of the first coiled-coil domain may activate Fes by disturbing intramolecular coiled-coil interaction, allowing for oligomerization via the second coiled-coil domain. Deletion of the second coiled-coil domain blocks fibroblast transformation by an activated form of c-Fes, consistent with this model. These results provide the first evidence for regulation of a nonreceptor protein-tyrosine kinase by coiled-coil domains.
PMCID: PMC84918  PMID: 10567558

Results 1-7 (7)