Journal of Computational Systems Biology最新文献

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Novel Computational Model of the Brain Water Metabolism: Introducing an Interdisciplinary Approach 脑水代谢的新计算模型:引入跨学科方法

Journal of Computational Systems Biology Pub Date : 2018-12-01 DOI: 10.15744/2455-7625.3.102

T. E.

{"title":"Novel Computational Model of the Brain Water Metabolism: Introducing an Interdisciplinary Approach","authors":"T. E.","doi":"10.15744/2455-7625.3.102","DOIUrl":"https://doi.org/10.15744/2455-7625.3.102","url":null,"abstract":"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.","PeriodicalId":412600,"journal":{"name":"Journal of Computational Systems Biology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123508303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Computing intrinsic noise of the genetic regulation modeled by Hill functions 希尔函数遗传调控的本征噪声计算

Journal of Computational Systems Biology Pub Date : 2018-12-01 DOI: 10.15744/2455-7625.3.101

Riccardo Ziraldo, Lan Ma

{"title":"Computing intrinsic noise of the genetic regulation modeled by Hill functions","authors":"Riccardo Ziraldo, Lan Ma","doi":"10.15744/2455-7625.3.101","DOIUrl":"https://doi.org/10.15744/2455-7625.3.101","url":null,"abstract":"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.","PeriodicalId":412600,"journal":{"name":"Journal of Computational Systems Biology","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1