The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon

Article Mathematical Modeling of the Heat-Shock Response in HeLa Cells

ABSTRACT The heat-shock response is a key factor in diverse stress scenarios, ranging from hyperthermia to protein folding diseases. However, the complex dynamics of this physiological response have eluded mathematical modeling efforts. Although several computational models have attempted to characterize the heat-shock response, they were unable to model its dynamics across diverse experimental datasets. To address this limitation, we mined the literature to obtain a compendium of in vitro hyperthermia experiments investigating the heat-shock response in HeLa cells. We identified mechanisms previously discussed in the experimental literature, such as temperature-dependent transcription, translation, and heat-shock factor (HSF) oligomerization, as well as the role of heat-shock protein mRNA, and constructed an expanded mathematical model to explain the temperature-varying DNA-binding dynamics, the presence of free HSF during homeostasis and the initial phase of the heat-shock response, and heat-shock protein dynamics in the long-term heat-shock response. In addition, our model was able to consistently predict the extent of damage produced by different combinations of exposure temperatures and durations, which were validated against known cellular-response patterns. Our model was also in agreement with experiments showing that the number of HSF molecules in a HeLa cell is roughly 100 times greater than the number of stress-activated heat-shock element sites, further confirming the model's ability to reproduce experimental results not used in model calibration. Finally, a sensitivity analysis revealed that altering the homeostatic concentration of HSF can lead to large changes in the stress response without significantly impacting the homeostatic levels of other model components, making it an attractive target for intervention. Overall, this model represents a step forward in the quantitative understanding of the dynamics of the heat-shock response

Mathematical Modeling of the Heat-Shock Response in HeLa Cells

AbstractThe heat-shock response is a key factor in diverse stress scenarios, ranging from hyperthermia to protein folding diseases. However, the complex dynamics of this physiological response have eluded mathematical modeling efforts. Although several computational models have attempted to characterize the heat-shock response, they were unable to model its dynamics across diverse experimental datasets. To address this limitation, we mined the literature to obtain a compendium of in vitro hyperthermia experiments investigating the heat-shock response in HeLa cells. We identified mechanisms previously discussed in the experimental literature, such as temperature-dependent transcription, translation, and heat-shock factor (HSF) oligomerization, as well as the role of heat-shock protein mRNA, and constructed an expanded mathematical model to explain the temperature-varying DNA-binding dynamics, the presence of free HSF during homeostasis and the initial phase of the heat-shock response, and heat-shock protein dynamics in the long-term heat-shock response. In addition, our model was able to consistently predict the extent of damage produced by different combinations of exposure temperatures and durations, which were validated against known cellular-response patterns. Our model was also in agreement with experiments showing that the number of HSF molecules in a HeLa cell is roughly 100 times greater than the number of stress-activated heat-shock element sites, further confirming the model’s ability to reproduce experimental results not used in model calibration. Finally, a sensitivity analysis revealed that altering the homeostatic concentration of HSF can lead to large changes in the stress response without significantly impacting the homeostatic levels of other model components, making it an attractive target for intervention. Overall, this model represents a step forward in the quantitative understanding of the dynamics of the heat-shock response

Assessment of pharmacologic area under the curve when baselines are variable. Pharmaceutical Research, 28, 10811089. Abbreviations used: aCSF, artificial cerebrospinal fluid; AUC, area under the curve; BBB, blood-brain-barrier; CE, cholesteryl ester; Cmax

Abstract Purpose: 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. Methods: 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. Results: 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. Conclusions: The variable nature of the baseline is an important aspect to consider when calculating the AUC

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

A multiscale modeling approach to inflammation: A case study in human endotoxemia

Inflammation is a critical component in the body’s response to injury. A dysregulated inflammatory response, in which either the injury is not repaired or the inflammatory response does not appropriately self-regulate and end, is associated with a wide range of inflammatory diseases such as sepsis. Clinical management of sepsis is a significant problem, but progress in this area has been slow. This may be due to the inherent nonlinearities and complexities in the interacting multiscale pathways that are activated in response to systemic inflammation, motivating the application of systems biology techniques to better understand the inflammatory response. Here, we review our past work on a multiscale modeling approach applied to human endotoxemia, a model of systemic inflammation, consisting of a system of compartmentalized differential equations operating at different time scales and through a discrete model linking inflammatory mediators with changing patterns in the beating of the heart, which has been correlated with outcome and severity of inflammatory disease despite unclear mechanistic underpinnings. Working towards unraveling the relationship between inflammation and heart rate variability (HRV) may enable greater understanding of clinical observations as well as novel therapeutic targets