Abstract
Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.
Highlights
The ability to evolve and adapt to changing environments is a fundamental characteristic of every cell (Magyar et al, 2007; Balázsi et al, 2011) that exploits nested feedback loops, multistable dynamics, and gene expression noise to generate complex regulatory networks (Elowitz et al, 2002; Brandman et al, 2005; Ray and Igoshin, 2010; Tiwari et al, 2010)
In this study we describe a computational model of Epithelial to mesenchymal transition (EMT) that integrates a transcriptional regulation network and a discrete time Markov Chain (DTMC)
These maps were selected as the ones that included at least six markers commonly associated with EMT (Epithelial to Mesenchymal Transition RT2 Profiler PCR Array, Qiagen) and were independently converted in boolean network (BN), substituting each interaction type coded in KEGG with a boolean operation
Summary
The ability to evolve and adapt to changing environments is a fundamental characteristic of every cell (Magyar et al, 2007; Balázsi et al, 2011) that exploits nested feedback loops, multistable dynamics, and gene expression noise to generate complex regulatory networks (Elowitz et al, 2002; Brandman et al, 2005; Ray and Igoshin, 2010; Tiwari et al, 2010) These processes are collectively referred to as cell decision making and are fundamental for the survival of every organism, from bacteria to humans (Magyar et al, 2007). Increased survival capabilities, motility, and fibrogenesis are hallmarks of this transition, together with marked modifications in cell shape and expression profile (Zhang et al, 2014), Figure 1
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