Modeling endotoxin-induced systemic inflammation using an indirect response approach.

A receptor mediated model of endotoxin-induced human inflammation is proposed. The activation of the innate immune system in response to the endotoxin stimulus involves the interaction between the extracellular signal and critical receptors driving downstream signal transduction cascades leading to transcriptional changes. We explore the development of an in silico model that aims at coupling extracellular signals with essential transcriptional responses through a receptor mediated indirect response model. The model consists of eight (8) variables and is evaluated in a series of biologically relevant scenarios indicative of the non-linear behavior of inflammation. Such scenarios involve a self-limited response where the inflammatory stimulus is cleared successfully; a persistent infectious response where the inflammatory instigator is not eliminated, leading to an aberrant inflammatory response, and finally, a persistent non-infectious inflammatory response that can be elicited under an overload of the pathogen-derived product; as such high dose of the inflammatory insult can disturb the dynamics of the host response leading to an unconstrained inflammatory response. Finally, the potential of the model is demonstrated by analyzing scenarios associated with endotoxin tolerance and potentiation effects.

NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION.

Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance.

Translational potential of systems-based models of inflammation.

A critical goal of translational research is to convert basic science to clinically relevant actions related to disease prevention, diagnosis, and eventually enable physicians to identify and evaluate treatment strategies. Integrated initiatives are identified as valuable in uncovering the mechanism underpinning the progression of human diseases. Tremendous opportunities have emerged in the context of systems biology that aims at the deconvolution of complex phenomena to their constituent elements and the quantification of the dynamic interactions between these components through the development of appropriate computational and mathematical models. In this review, we discuss the potential role systems-based translation research can have in the quest to better understand and modulate the inflammatory response.

A novel non-overlapping bi-clustering algorithm for network generation using living cell array data.

MOTIVATION: The living cell array quantifies the contribution of activated transcription factors upon the expression levels of their target genes. The direct manipulation of the regulatory mechanisms offers enormous possibilities for deciphering the machinery that activates and controls gene expression. We propose a novel bi-clustering algorithm for generating non-overlapping clusters of reporter genes and conditions and demonstrate how this information can be interpreted in order to assist in the construction of transcription factor interaction networks.