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Abstract
Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene–gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype–phenotype relationships and provide valuable information for personalized medicine.
Highlights
IntroductionGene regulatory networks (GRNs) have been thought to operate as the genomic mechanisms that guide the organism’s response to changes in their environment.[1,2] One promising subject of research in modern biology and translational medicine is how to infer biologically realistic and statistically robust GRNs from increasingly available transcriptional data and link them to physiological, pathological, and clinical characteristics.[3,4,5] A number of statistical approaches, such as Boolean networks,[6] Bayesian networks,[7] mutual information theory,[8,9] and graphical models,[10] have been developed for network inference, and these approaches visualize GRNs as probabilistic, undirected or unidirectional graphs, where each node represents a gene and edges depict relationships between genes
To investigate the genomic mechanisms underlying graft outcome, transcriptomes of circulating monocytes from patients of success and failure were monitored at pre-operation network reconstruction, we can interpolate or extrapolate gene networks based on their expression index (EI)
The promotes its partner but the latter does not affect the former expression of ADAM9 and LCN2 increases with EI, but the former, while directional antagonism occurs if one gene inhibits displays a greater slope of increase (Fig. 2a) than does the latter the other and the other is neutral
Summary
Gene regulatory networks (GRNs) have been thought to operate as the genomic mechanisms that guide the organism’s response to changes in their environment.[1,2] One promising subject of research in modern biology and translational medicine is how to infer biologically realistic and statistically robust GRNs from increasingly available transcriptional data and link them to physiological, pathological, and clinical characteristics.[3,4,5] A number of statistical approaches, such as Boolean networks,[6] Bayesian networks,[7] mutual information theory,[8,9] and graphical models,[10] have been developed for network inference, and these approaches visualize GRNs as probabilistic, undirected or unidirectional graphs, where each node represents a gene and edges depict relationships between genes Such graphs may not be sufficiently informative for charting the topological structure of a GRN because genes may regulate and be regulated by other genes, with regulations in different signs and strengths and varying across time and space scales.[3,11]. The second condition is very difficult to justify, since gene expression is often stochastically fluctuated.[20,21] To the end, despite its capacity to code bidirectional, signed, and weighted interactions into a fully informative network, the direct use of ODE networking can be very limited in practice
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