Multi-component biological networks perform diverse functions, ranging from cell division to environmental adaptation. Their structures are understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. We believe that the integration of engineering and biology can lead to paradigm shifting theoretical and experimental advances that will revolutionize our ability to understanding complexity in biological systems, via deriving fundamental insights on their networks. Based on preliminary experiments, we form the hypothesis that a range of small-scale synthetic networks, that emulate interconnections and topologies frequently encountered in cells, can be utilized to develop and validate novel reverse engineering tools and theory. Our long-term objective is to develop a framework for the design of multi-target therapeutics. The proposed aim will bring us considerably closer to this objective, providing a first generation of robust and reliable reverse engineering algorithms that will allow us to shed light in direct versus indirect regulation in cells. In particular, we aim to construct a set of small scale networks that will be stably integrated in mammalian cells. Subsequently, the individual nodes of these networks will be weakly perturbed from their steady state. The pre- and post-perturbation steady states will be measured and fed into reverse engineering algorithms to predict the network structure. The results of the algorithm will be compared against the known connectivities, and will be used to adjust the parameters of the algorithm and more generally the experiment. These parameters include the magnitude of the perturbations, the data collection and processing techniques, as well as the details of computational processing. Developing automated and rigorously validated methodologies for unraveling the complexity of bimolecular networks in human cells is one of the central challenges to life scientists and engineers. Our research agenda proposes an innovative experimental platform to transform the way in which this challenge is addressed by the scientific community, and we believe it has the potential to greatly influence basic biological research. We will generate a collection of bimolecular networks integrated in human cells freely available to the broad scientific community, thus available for a wide spectrum of studies. Using these cells we will create novel methods for reverse engineering and characterization of biological networks incorporating newly- developed experimental techniques and developing theoretical tools for interpreting the data. The results will be used towards identifying general principles and laws of biological systems, in particular focusing on delineating the properties of networks and distinguishing direct versus indirect effects.

Public Health Relevance

We propose to implement a novel experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. The results will shed light in new ways to identify direct versus indirect regulation in cells and spark a wide range of applications relevant to public health, related to understanding cellular networks and developing novel multi-target therapeutics.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Exploratory/Developmental Grants (R21)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Lyster, Peter
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University of Texas-Dallas
Engineering (All Types)
Schools of Engineering
United States
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