InterJournal Genetics, 416
Status: Accepted
Manuscript Number: [416]
Submission Date: 609
Cell state dynamics and tumorigenesis in boolean regulatory networks
Author(s): Sui Huang

Subject(s): BG.1, BG.14, BG.2, CX.31, CX.04.02.4

Category: Brief Article

Abstract:

The unprecedented success of genomic technologies has led to a flood of data whose analysis currently aims at assigning 'functions' to each gene. As an alternative to this reductionist approach we propose here that many biological 'functions' are emergent properties that arise from the interaction between the genes. We present here the idea of boolean genetic networks as a simple modeling language for studying the dynamics of gene expression profiles in conjunction with cellular regulation and tumorigenesis. Normal tissue homeostasis requires a dynamic balance between the cellular states of proliferation, differentiation and apoptosis. Hence, the disruption of this balance towards growth can lead to neoplasia. These cellular states are here modeled as attractor states of a regulatory network. We demonstrate how this conceptual framework can provide new insights for old question such as the pleiotropy of signal transduction cascades and the switch between cellular states. We revisit Kauffman's idea of cancer cells being represented by attractors and propose a refined model leading to a general hypothesis for tumorigenesis, in which epigenetic (environment) and genetic (mutagens) are integrated. Confronting simulation results with recent real world observations we argue that despite the idealization and simplification, the boolean network model captures a whole array of features of the dynamics of functional states in real cells. In particular, it provides explanations for specific phenomena concerning the relationship between molecular process and the macroscopic cellular behavior that appeared inexplicable and paradoxical in the traditional view centered around individual, linear pathways. We discuss how the integrative concept of regulatory networks can be useful in the interpretation of data generated by the burgeoning massively-parallel approaches, such as gene expression profiling and proteomics.

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