Abstract
Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of the Trypanosoma cruzi’s life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite’s life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.
Highlights
One of the main aims in the post-genome era is to elucidate the complex webs of interacting genes and proteins underlying the establishment and maintenance of cell states
Key decisions in modeling a gene network system include the choice of variables and the mathematical framework for representing the system dynamics
When choosing a linear model, in which the expression levels of N genes at time t determine the changes of such expression levels at time t + Δt, the transition matrix must be computed from N pairs of input-output data
Summary
One of the main aims in the post-genome era is to elucidate the complex webs of interacting genes and proteins underlying the establishment and maintenance of cell states. The gene regulatory network (GRN) approach is one of the most popular frameworks used today [4, 5]. This approach has been used to study key reprogramming genes and cell differentiation processes in stem cells from different points of view [6,7,8]. GRN models are dynamical systems whose states determine the gene-expression levels [4]. The task of uncovering the GRN architecture from the cell states (gene-expression profiles) represents a very complex inverse problem that has become central in functional genomics [9]. Emerging technologies offer new prospects for monitoring mRNA concentrations, researchers have focused on determining the architecture of simplified theoretical models [11]
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