Huang, Z.-G. | bioRxiv Science

bioRxiv2024

Canalization and competition: the cornerstone of genetic network's dynamic stability and evolution

Grasping the fundamental dynamic property is a crucial approach for understanding living systems. Here we conduct a comprehensive study into the relationship between regulatory modes and dynamic features of gene networks. Our findings indicate that conditional constraints and competition, corresponding to canalizing and threshold regulating modes respectively, play pivotal roles in driving gene networks towards criticality. Particularly, they effectively rescue biosystems from disordered area as source of evolutionary driving force. By employing variant Kauffman models, order parameters, and stability analysis, we provide sufficient numerical evidence demonstrating the diverse and distinctive capabilities of regulatory modes in stabilizing systems. Our findings give the most systematic analysis to date on the dynamic atlas of regulatory modes, offering a framework-independent proof of genetic networks operating at the edge of chaos with evolutionary implications. Furthermore, we discus the bridge between criticality and canalizing/threshold regulating modes and propose a reasonable scheme for generating model.

systems biology↗

bioRxiv2023

Convergent Transcriptomic Evidence Reveals the Dysfunctional Quantitative Mechanism of Synaptic Plasticity Control in ASD

A prominent endophenotype in Autism Spectrum Disorder (ASD) is synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigated the postsynaptic signal transduction network in glutamatergic neurons and integrated transcriptomics to unveil the malfunction of translation control. We devised an innovative and highly dependable pipeline to transform our acquired signal transduction network into a mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employed Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD. The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncovered distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons. Importantly, our work has introduced a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.

systems biology↗