Computing intrinsic noise of the genetic regulation modeled by Hill functions

Intrinsic noise embedded in stochastic gene regulation due to low copy number of species has been studied using the approach of theoretical modeling and computational simulation, including the standard methods of stochastic simulation algorithm (SSA) and linear noise approximation (LNA). At average cell level, Hill functions are widely used as a compact format to represent gene regulation involving multi-transcription-factor binding and cooperativity. Heuristic SSA and LNA methods (hSSA and hLNA) have been applied to study stochastic models employing Hill functions. It is however unclear which modeling and simulation method is suitable to characterize intrinsic noise of Hill-type gene regulation with sufficient accuracy and computational efficiency. In this work, we perform noise analysis of two gene regulatory models represented by second-order activating and inhibitory Hill functions, seeking to evaluate the performance of five existing noise modeling methods. Specifically, SSA and LNA are applied to the full models that are expanded from the Hill functions containing only elementary reactions, while hSSA and hLNA are applied to reduced models where the Hill function is heuristically used. In addition, we characterize intrinsic noise using the slow-scale LNA (ssLNA) method that is recently proposed to deal with models with both fast and slow time scales. Using SSA as ground truth, we find that hSSA and hLNA underestimate the level of intrinsic noise in the Hill-type models, despite of high computational efficiency. The ssLNA approach calculates noise with comparable accuracy as SSA and LNA, while requesting much less computational resources. In addition, the chemical Langevin equation (CLE) under the same slow-scale framework simulates single-cell stochastic trajectories as accurately as SSA yet with significantly lower computational demands. This study shows that ssLNA complemented by slow-scale CLE offers a computational platform that out-performs the other four methods in characterizing intrinsic stochasticity of the Hill-type genetic models.

Novel Computational Model of the Brain Water Metabolism: Introducing an Interdisciplinary Approach

Brain water metabolism ensures the processes of cellular communication, transit of the signaling molecules, neurotransmitters, cytokines and substrates, participates in the clearance of pathogenic metabolites. Many neurological conditions that present serious clinical problems arise from altered fluid flow (e.g. Alzheimer’s disease, idiopathic normal pressure hydrocephalus, migraine, traumatic brain injury and stroke). At present, the orthodox theory fails to explain the accumulated experimental evidence and clinical data on the brain water metabolism. Modeling becomes an important approach to testing current theories and developing new working mechanisms. A novel computational model of brain water metabolism has been developed and explored. Using an interdisciplinary approach the long-recognized nanodimentionality of the brain interstitial space is now viewed as a nanofluidic domain with the fluid flow there governed by the slip-flow principles of nanofluidics. Aquaporin-4 (AQP4) of the astrocyte endfeet membranes ensures kinetic control over water movement across the blood-brain barrier. The pulsatory intracranial pressure presents the driving force behind the transcapillary water flow. The model demonstrates good predictability in respect to some physiological features of brain water metabolism and relevance in explaining clinical conditions. The model may find its use in neurobiological research, development of the AQP4-targeted drug therapy, optimization of the intrathecal drug delivery to the brain tumours, in a research on a broad spectrum of water-metabolic-disorder-related conditions.

Predicting Multi-Drug Inhibition Interactions based on Signaling Networks and Single Drug Dose-Response Information

Inhibiting multiple pathways/targets with combination drug therapies is widely used to maximize therapeutic benefit in many diseases. Therapeutic success can often be improved by concurrently and/or sequentially inhibiting multiple pathways/targets by combining drugs with different mechanisms of action. It is critical to understand potential drug interactions in developing and optimizing new multidrug regimens. We used data from experiments of single drugs (and few combinations) and existing signaling network knowledge from sources such as KEGG, to develop a statistical rescaling model to describe the effects of drugs on network topology. The model comprises a Hill equation for signals arriving at each receptor, a generic enzymatic rate equation to transmit signals among connecting genes, and a regression model to represent the cumulative effect of genes implicated in activation of the cell death machinery. We performed in silico experiments to derive a global sensitivity index of each term in the functional ANOVA of the dose-response model by generating doses of the drugs with the Quasi Monte-Carlo method. Only terms with large (principal) global sensitivity indices remain in the functional dose-response model. Thus, we reduce a high dimensional problem into one that can be managed experimentally. The model generates an index that indicates potential interactions of the drugs and the principal combinations, those associated with large global sensitivity indices, can be considered as candidates for further experimental validation.