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1.  Predicting critical transitions in a model of systemic inflammation 
Journal of theoretical biology  2013;338:10.1016/j.jtbi.2013.08.011.
The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.
PMCID: PMC3870185  PMID: 23973206
endotoxemia; systems biology; generalized modeling; bifurcation; stability analysis
2.  Translational applications of evaluating physiologic variability in human endotoxemia 
Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability (HRV) to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.
PMCID: PMC3664105  PMID: 23203205
systemic inflammation; heart rate variability; cortisol; circadian rhythms; decomplexification
3.  Modeling Physiologic Variability in Human Endotoxemia 
The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.
PMCID: PMC3604977  PMID: 23140122
systems biology; inflammation; mathematical modeling
4.  Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling 
Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.
PMCID: PMC3387781  PMID: 22783197
mathematical model; inflammation; physiologic variability; heart rate variability; neural control
5.  Assessment of Pharmacologic Area Under the Curve When Baselines are Variable 
Pharmaceutical research  2011;28(5):1081-1089.
The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements.
An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses.
This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance.
The variable nature of the baseline is an important aspect to consider when calculating the AUC.
PMCID: PMC3152796  PMID: 21234658
AUC; baseline; bioinformatics; microarrays; pharmacogenomics
6.  A New Symbolic Representation for the Identification of Informative Genes in Replicated Microarray Experiments 
Microarray experiments generate massive amounts of data, necessitating innovative algorithms to distinguish biologically relevant information from noise. Because the variability of gene expression data is an important factor in determining which genes are differentially expressed, analysis techniques that take into account repeated measurements are critically important. Additionally, the selection of informative genes is typically done by searching for the individual genes that vary the most across conditions. Yet because genes tend to act in groups rather than individually, it may be possible to glean more information from the data by searching specifically for concerted behavior in a set of genes. Applying a symbolic transformation to the gene expression data allows the detection overrepresented patterns in the data, in contrast to looking only for genes that exhibit maximal differential expression. These challenges are approached by introducing an algorithm based on a new symbolic representation that searches for concerted gene expression patterns; furthermore, the symbolic representation takes into account the variance in multiple replicates and can be applied to long time series data. The proposed algorithm's ability to discover biologically relevant signals in gene expression data is exhibited by applying it to three datasets that measure gene expression in the rat liver.
PMCID: PMC3133780  PMID: 20455749

Results 1-6 (6)