Kishore Hari

The paper shows how regulatory noise affects cell-fate decisions by altering phenotype occupancy in gene regulatory networks.

The paper identifies IRF6 as an inhibitor of epithelial-mesenchymal transition (EMT) through integrated computational and experimental analysis.

The paper identifies a feedback loop between AMPK, ERK, and TFEB in matrix-detached cancer cells that generates non-genetic heterogeneity, affecting autophagy and cell survival.

The paper introduces a multilevel Boolean model to capture hybrid epithelial-mesenchymal phenotypes, revealing more stable and biologically relevant hybrid states than traditional binary models.

This paper develops a deterministic model to study how competition and phenotypic plasticity affect adaptive therapy outcomes in cancer, showing that phenotypic transitions can complicate therapy and that asymmetric competition benefits treatment.

The paper shows that multi-team competition in gene regulatory networks leads to less stable outcomes compared to binary competition, explaining why binary decisions are common in biology.

The paper introduces a framework to analyze gene regulatory networks by identifying mutually inhibiting teams of nodes and shows how network topology predicts dynamic behavior.

This study evaluates two threshold formalisms for Boolean gene regulatory networks, finding that while Ising rules match fewer functions exactly, they provide better average agreement with biological dynamics.

This study investigates toggle-n networks to understand the dynamics of differentiation into multiple cell states, showing co-expression of transcription factors and the role of cytokine signaling and regulatory asymmetry.

The paper shows that coordinated teams in biological networks lead to low-dimensional phenotypic space, with PC1 capturing most variance.

The paper investigates lineage-specific coordination of X-chromosome inactivation and active-X upregulation in female cells across different developmental contexts and identifies UTF1 as a mediator of X-upregulation erasure in some lineages.

The paper studies a four-node mutually repressive network, showing it allows for six stable double-positive states and applies this to explain Th1/Th2/Th17/Treg differentiation.

The paper compares two methods for analyzing gene regulatory network dynamics, finding strong agreement between parameter sampling and combinatorial analysis despite differing assumptions about model parameters.

The paper analyzes the bistability characteristics of mutual inhibition, mutual activation, and self-activation motifs using ODE simulations and explores their utility in coordinated gene expression.

This study investigates how couplings between bistable switches affect multistability and phenotypic space using simulation methods.

The paper investigates how mutually repressing network motifs maintain their dynamics when embedded in larger networks, finding that increased connectivity and self-inhibition reduce their canonical behavior.

The paper identifies network topology metrics that influence the enrichment of hybrid epithelial-mesenchymal phenotypes in metastasis using gene regulatory network models.

The paper identifies 'teams' of nodes in regulatory networks that shape phenotypic landscapes, showing how network topology determines phenotypic distributions without simulation.

The paper investigates X-chromosome upregulation (XCU) in mouse embryos and finds it is not global, differs between ancestral and acquired X-linked genes, and may not drive the evolution of X-chromosome inactivation (XCI).

The paper investigates robustness in epithelial-mesenchymal plasticity (EMP) regulatory networks by analyzing the role of positive and negative feedback loops in structural and dynamic stability.

The paper investigates dynamic random monoallelic expression (aRME) in pre-gastrulation embryos, finding that allelic bursting is semi-coordinated rather than independent, leading to expression heterogeneity.

The paper investigates a regulatory network in small cell lung cancer that exhibits multistability, explaining phenotypic heterogeneity through toggle switch dynamics.

The paper uses network topology analysis to identify key links in epithelial-mesenchymal plasticity networks that could inhibit metastasis by reducing positive feedback loops.

This paper develops a mechanistic model of SARS-CoV-2 and immune system interactions to explain diverse clinical outcomes and guide drug combination strategies.

The paper analyzes single-cell expression profiles of circulating tumor cells (CTCs) across cancers, identifies EMT-related patterns, and develops a classifier to recognize CTCs using PBMC transcriptomes.