Modeling brain dynamics using computational neurogenetic approach

The paper introduces a novel computational approach to brain dynamics modeling that integrates dynamic gene–protein regulatory networks with a neural network model. Interaction of genes and proteins in neurons affects the dynamics of the whole neural network. Through tuning the gene–protein interaction network and the initial gene/protein expression values, different states of the neural network dynamics can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network of the generation of local field potential. Our approach allows for investigation of how deleted or mutated genes can alter the dynamics of a model neural network. We conclude with the proposal how to extend this approach to model cognitive neurodynamics.

Effects of mesophyll conductance on vegetation responses to elevated CO2 concentrations in a land surface model

Mesophyll conductance (g_m) is known to affect plant photosynthesis. However, g_m is rarely explicitly considered in land surface models (LSMs), with the consequence that its role in ecosystem and large‐scale carbon and water fluxes is poorly understood. In particular, the different magnitudes of g_m across plant functional types (PFTs) are expected to cause spatially divergent vegetation responses to elevated CO₂ concentrations. Here, an extensive literature compilation of g_m across major vegetation types is used to parameterize an empirical model of g_m in the LSM JSBACH and to adjust photosynthetic parameters based on simulated A_n ? C_i curves. We demonstrate that an explicit representation of g_m changes the response of photosynthesis to environmental factors, which cannot be entirely compensated by adjusting photosynthetic parameters. These altered responses lead to changes in the photosynthetic sensitivity to atmospheric CO₂ concentrations which depend both on the magnitude of g_m and the climatic conditions, particularly temperature. We then conducted simulations under ambient and elevated (ambient + 200 μmol/mol) CO₂ concentrations for contrasting ecosystems and for historical and anticipated future climate conditions (representative concentration pathways; RCPs) globally. The g_m‐explicit simulations using the RCP8.5 scenario resulted in significantly higher increases in gross primary productivity (GPP) in high latitudes (+10% to + 25%), intermediate increases in temperate regions (+5% to + 15%), and slightly lower to moderately higher responses in tropical regions (?2% to +5%), which summed up to moderate GPP increases globally. Similar patterns were found for transpiration, but with a lower magnitude. Our results suggest that the effect of an explicit representation of g_m is most important for simulated carbon and water fluxes in the boreal zone, where a cold climate coincides with evergreen vegetation.     

Subinhibitory concentrations of antibiotics affect stress and virulence gene expression in Listeria monocytogenes and cause enhanced stress sensitivity but do not affect Caco‐2 cell invasion

Aims

Antibiotics can act as signal molecules and affect bacterial gene expression, physiology and virulence. The purpose of this study was to determine whether subinhibitory antibiotic concentrations alter gene expression and physiology of Listeria monocytogenes.

Methods and Results

Using an agar‐based screening assay with promoter fusions, 14 of 16 antibiotics induced or repressed expression of one or more stress and/or virulence genes. Despite ampicillin‐induced up‐regulation of PinlA‐lacZ expression, Caco‐2 cell invasion was not affected. Subinhibitory concentrations of ampicillin and tetracycline caused up‐ and down‐regulation of stress response genes, respectively, but both antibiotics caused increased sensitivity to acid stress. Six combinations of gene‐antibiotic were quantified in broth cultures and five of the six resulted in the same expression pattern as the agar‐based assay.

Conclusions

Antibiotics affect virulence and/or stress gene expression; however, altered expression could not predict changes in phenotypic behaviour. Subinhibitory concentrations of antibiotics led to increased acid sensitivity, and we speculate that this is attributed to changes in cell envelope or reduced σ^B‐dependent gene expression.

Significance and Impact of the Study

Although subinhibitory concentrations of antibiotics affect gene expression in L. monocytogenes, the changes did not increase virulence but did enhance the acid sensitivity.     

A general-purpose framework to simulate musculoskeletal system of human body: using a motion tracking approach*

Computation of muscle force patterns that produce specified movements of muscle-actuated dynamic models is an important and challenging problem. This problem is an undetermined one, and then a proper optimization is required to calculate muscle forces. The purpose of this paper is to develop a general model for calculating all muscle activation and force patterns in an arbitrary human body movement. For this aim, the equations of a multibody system forward dynamics, which is considered for skeletal system of the human body model, is derived using Lagrange–Euler formulation. Next, muscle contraction dynamics is added to this model and forward dynamics of an arbitrary musculoskeletal system is obtained. For optimization purpose, the obtained model is used in computed muscle control algorithm, and a closed-loop system for tracking desired motions is derived. Finally, a popular sport exercise, biceps curl, is simulated by using this algorithm and the validity of the obtained results is evaluated via EMG signals.     

Creating a human head finite element model using a multi-block approach for predicting skull response and brain pressure

To better understand head injuries, human head finite element (FE) models have been reported in the literature. In scenarios where the head is directly impacted and measurements of head accelerations are not available, a high-quality skull model, as well as a high-quality brain model, is needed to predict the effect of impact on the brain through the skull. Furthermore, predicting cranial bone fractures requires comprehensively validated skull models. Lastly, high-quality meshes for both the skull and brain are needed for accurate strain/stress predictions across the entire head. Hence, we adopted a multi-block approach to develop hexahedral meshes for the brain, skull, and scalp simultaneously, a first approach in its kind. We then validated our model against experimental data of brain pressures (Nahum et al., 1977 Nahum AM, Smith R, Ward CC. 1977. Intracranial pressure dynamics during head impact. Proceedings of the 21st Stapp Car Crash Conference, SAE Paper No. 770922; Warrendale, PA: Society of Automotive Engineers.[Crossref] , [Google Scholar]) and comprehensive skull responses (Yoganandan et al., 1995 Yoganandan N, Pintar FA, Sances A, Jr., Walsh PR, Ewing CL, Thomas DJ, Snyder RG. 1995. Biomechanics of skull fracture. J Neurotrauma. 12(4):659–668.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], Yoganandan et al., 2004 Yoganandan N, Zhang J, Pintar FA. 2004. Force and acceleration corridors from lateral head impact. Traffic Injury Prevention. 5(4):368–373.[Taylor &; Francis Online] , [Google Scholar], and Raymond et al., 2009 Raymond D, Van Ee C, Crawford G, Bir C. 2009. Tolerance of the skull to blunt ballistic temporo-parietal impact. J Biomech. 42(15):2479–2485.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). We concluded that a human head FE model was developed with capabilities to predict blunt- and ballistic-impact-induced skull fractures and pressure-related brain injuries.     

Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints

Modified 3D-SDAR fingerprints combining ¹³C and ¹⁵N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD−). The best performing 3D-QSDAR model utilized a grid with a density of 8 ppm × 8 ppm in the C–C region, 8 ppm × 20 ppm in the C–N region and 20 ppm × 20 ppm in the N–N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy = 0.70, sensitivity = 0.73 and specificity = 0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5–7.5 Å distant from an amino-group. The presence of a second aromatic ring separated by a 4–5 Å spacer from the first ring and at a distance of between 5.5 Å and 7 Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.     

A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.     

A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.     

Improvement of cloned alpha-amylase gene expression in fed-batch culture of recombinant Saccharomyces cerevisiae by regulating both glucose and ethanol concentrations using a fuzzy controller

The effect of ethanol concentration on cloned gene expression in recombinant Saccharomyces cerevisiae strain 20B-12 containing one of two plasmids, pNA3 and pNA7, was investigated in batch cultures. Plasmids pNA3 and pNA7 contain the alpha-amylase gene under the control of the SUC2 or PGK promoter, respectively. When the ethanol concentration was controlled at 2 to 5 g/L, the gene expressions were two times higher than those at 20 g/L ethanol. The increase the gene expression by maintaining both the ethanol and glucose concentrations at low levels, a fuzzy ontroller was developed. The concentrations of glucose and ethanol were controlled simultaneously at 0.15 and 2 g/L, respectively, in the production phase using the fuzzy controller in fed-batch culture. The synthesis of alpha-amylase was induced by the low glucose concentration and maintained at a high level of activity by regulating the ethanol concentration at 2 g/L. The secretory alpha-amylase was induced by the low glucose concentration and maintained at a high level of activity by regulating the ethanol concentration at 2 g/L. The secretory alpha-amylase activities of cells harboring plasmids pNA3 and pNA7 in fed-batch culture were 175 and 395 U/mL, and their maximal specific activities 7.7 and 12.4 U/mg dry cells, respectively. These values are two to three times higher in activity and three to four times higher in specific activity than those obtained when glucose only was controlled. (c) 1994 John Wiley & Sons, Inc.