ABI Innovation: Empirical Dynamics: A Next-Generation Approach For Uncovering Hidden Causal Links in Gene Expression

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The full human genome of 3.3 billion base pairs was first sequenced nearly 15 years ago. Nonetheless, genetic science is still a long way off from understanding how this exhaustive list of "parts" fits together. This project heralds a transformative shift in the basic mathematical approach to understanding how genes interact, by exploiting the fact that gene expression is a temporal and context dependent process. For example, every person has the genetic coding to produce melatonin; however, its expression varies through the day (keying on genes in the circadian clock) and it is sensitive to environmental factors like light exposure. Thus the temporal sequence and context of expression are important. However, current approaches to understanding variability in expression have relied on non-temporal statistical frameworks based on correlation. They assume that if genes interact, they will always either be positively correlated (simultaneously expressed among all samples) or negatively correlated (expressed only when the other is not) regardless of context or the changing cellular environment. Though a convenient simplification, such correlative approaches are obviously incomplete, and are likely to overlook essential processes such as thresholding, regime shifts, and gene check-pointing that specifically arise from dynamic, context-dependent behavior. This project will investigate empirical dynamic modeling (EDM) as an emerging framework that explicitly accounts for both the context and temporal sequence of expression events. The work will be a mixture of proof-of-principle and application to a cell differentiation pathway in mice associated with breast cancers. Development of EDM will provide an important complement to current approaches, with the capability to dramatically increase the efficacy of bioinformatics and systems biology research, and to reveal important regulatory hubs of genes that are non-correlated and thus invisible to current approaches.This project will investigate and further develop empirical dynamic modeling (EDM) as a conceptual framework to identify causal interactions among genes and produce mechanistic understanding that can be validated by prediction. As an approach that accommodates the reality of natural (non-engineered) nonlinear (context-dependent) interconnected systems, EDM is well-suited to leverage temporally-explicit genomic datasets that have only recently become feasible. In the first phase, the approach will be applied to construct gene interaction networks during the yeast (S. cerevisiae) cell cycle to explore how an explicitly nonlinear and dynamic approach can reveal information that is hidden to current analytical methods. Building on this, the second phase will seek to develop and implement an EDM approach (static cross-map) to single cell data that necessarily lack an explicit time sequence. The approach will be developed and tested on computational models of gene circuits, then applied to single cell data obtained from a stem-cell differentiation sequence in mice. Work will be validated by comparison to existing ontologies and by tailored experiments to be carried out by a collaborating lab at The Salk Institute. Software will be distributed through BioConductor: https://www.bioconductor.org/, all other materials (video animations, interactive demos, etc.) will be hosted on the Sugihara Lab website: http://deepeco.ucsd.edu.