From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building.

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

Mathematical modeling of the circadian dynamics of the neuroendocrine-immune network in experimentally induced arthritis.

The circadian dynamics of important neuroendocrine-immune mediators have been implicated in progression of rheumatoid arthritis pathophysiology, both clinically as well as in animal models. We present a mathematical model that describes the circadian interactions between mediators of the hypothalamic-pituitary-adrenal (HPA) axis and the proinflammatory cytokines. Model predictions demonstrate that chronically elevated cytokine expression results in the development of adrenal insufficiency and circadian variability in paw edema. Notably, our model also predicts that an increase in mean secretion of corticosterone (CST) after the induction of the disease is accompanied by a decrease in the amplitude of the CST oscillation. Furthermore, alterations in the phase of circadian oscillation of both cytokines and HPA axis mediators are observed. Therefore, by incorporating the circadian interactions between the neuroendocrine-immune mediators, our model is able to simulate important features of rheumatoid arthritis pathophysiology.

Systems biology of circadian-immune interactions.

There is increasing evidence that the immune system is regulated by circadian rhythms. A wide range of immune parameters, such as the number of red blood cells and peripheral blood mononuclear cells as well as the level of critical immune mediators, such as cytokines, undergo daily fluctuations. Current experimental data indicate that circadian information reaches immune tissues mainly through diurnal patterns of autonomic and endocrine rhythms. In addition, immune factors such as cytokines can also influence the phase of the circadian clock, providing bidirectional flow of circadian information between the neuroendocrine and immune systems. This network of neuroendocrine-immune interactions consists of complexly integrated molecular feedback and feedforward loops that function in synchrony in order to optimize immune response. Chronic stress can disrupt this intrinsic orchestration, as several endocrine signals of chronically stressed patients present blunted rhythmic characteristics. Reprogramming of biological rhythms has recently gained much attention as a potent method to leverage homeostatic circadian controls to ultimately improve clinical outcomes. Elucidation of the intrinsic properties of such complex systems and optimization of intervention strategies require not only an accurate identification of the signaling pathways that mediate host responses, but also a system-level description and evaluation.

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.

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.

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.

Extracting global system dynamics of corticosteroid genomic effects in rat liver.

One of the challenges in constructing biological models involves resolving meaningful data patterns from which the mathematical models will be generated. For models that describe the change of mRNA in response to drug administration, questions exist whether the correct genes have been selected given the myriad transcriptional effects that may occur. Oftentimes, different algorithms will select or cluster different groups of genes from the same data set. A new approach was developed that focuses on identifying the underlying global dynamics of the system instead of selecting individual genes. The procedure was applied to microarray genomic data obtained from rat liver after a large single dose of methylprednisolone in 52 adrenalectomized rats. Twelve clusters of at least 30 genes each were selected, reflecting the major changes over time. This method along with isolating the underlying dynamics of the system also extracts and clusters the genes that make up this global dynamic for further analysis as to the contributions of specific mechanisms affected by the drug.

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.

Analysis of time-series gene expression data: methods, challenges, and opportunities.

Monitoring the change in expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of high-throughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression data. In this review, we summarize the qualitative characteristics of these approaches, discuss the main challenges that this type of complex data present, and, finally, explore the opportunities in the context of developing mechanistic models of cellular response.

A predictive method for the evaluation of peptide binding in pocket 1 of HLA-DRB1 via global minimization of energy interactions.

Human leukocyte antigens (HLA) or histocompatibility molecules are glycoproteins that play a pivotal role in the development of an effective immune response. An important function of the HLA molecules is the ability to bind and present antigen peptides to T lymphocytes. Presently there is no comprehensive way of predicting and energetically evaluating peptide binding on HLA molecules. To quantitatively determine the binding specificity of a class II HLA molecule interacting with peptides, a novel decomposition approach based on deterministic global optimization is proposed that takes advantage of the topography of HLA binding grove, and examined the interactions of the bound peptide with the five different pockets. In particular, the main focus of this paper is the study of pocket 1 of HLADR1 (DRB1*0101 allele). The determination of the minimum energy conformation is based on the ECEPP/3 potential energy model that describes the energetics of the atomic interactions. The minimization of the total potential energy is formulated on the set of peptide dihedral angles, Euler angles, and translation variables to describe the relative position. The deterministic global optimization algorithm, alpha BB, which has been shown to be epsilon-convergent to the global minimum potential energy through the solution of a series of nonlinear convex optimization problems, is utilized. The PACK conformational energy model that utilizes the ECEPP/3 model but also allows the consideration of protein chain interactions is interfaced with alpha BB. MSEED, a program used to calculate the solvation contribution via the area accessible to the solvent, is also interfaced with alpha BB. Results are presented for the entire array of naturally occurring amino acids binding to pocket 1 of the HLA DR1 molecule and very good agreement with experimental binding assays is obtained.