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PubMed2001

A dynamical systems model to simulate the perturbation kinetics of gene expression by antisense oligonucleotides.

Antisense oligonucleotides owe their efficacy to an ability to induce RNase H-dependent suppression of RNA translation, for sufficient time to allow physiological proteolysis. The magnitude and time delay preceding the protein nadir concentration determine the extent and timing of maximum antisense oligonucleotide activity. Antisense oligonucleotide degradation underlies reversal of RNA downregulation. The kinetics of protein downregulation is therefore determined by the complex interaction of both ligand chemistry (nuclease stability, affinity and RNase H activation), and gene expression kinetics. Optimization of antisense oligonucleotide efficacy and experimental design requires understanding of these interactions. The kinetics of protein and RNA downregulation have therefore been simulated by analysing a two-compartment kinetic model incorporating RNase H-dependent transcript degradation. The system of nonlinear differential equations describing this model was solved numerically using Runge-Kutte integration. The timecourse solutions corresponding to the four state variables (RNA, protein, antisense/RNA heteroduplex and antisense oligonucleotide), were determined simultaneously. This allowed systematic in silico examination of the consequences of altering variables such as oligonucleotide concentration, affinity, and stability, or the scheduling of multiple transfections on RNA and protein perturbations. By providing a tool for examining antisense oligonucleotide action theoretically, this heuristic model should facilitate both the rational design and interpretation of antisense experiments.

Animals↗

PubMed2002

Control of neural information transmission by synaptic dynamics.

The computational processing of a neural system is strongly influenced by the dynamical characteristics of the information transmission between neurons. In this work, the control of neural information transmission by synaptic dynamics is investigated by means of a master-equation-based stochastic model of pre-synaptic release of neurotransmitter-containing vesicles. The model incorporates facilitation of vesicle fusion with the pre-synaptic membrane due to intracellular calcium ions and depletion of readily releasable vesicles. The message to be transmitted is coded by the pre-synaptic firing sequence, and the received signal corresponds to the post-synaptic membrane potential response. At the sending end, the stochastic character of the vesicle release contributes to the entropy of the probability distribution of the number of vesicles released and represents noise with respect to information transmission. At the receiving end, the generation of post-synaptic membrane potentials is influenced by the temporal behaviour of ionic currents and membrane charging and is determined by means of a low-dimensional model. The rate and temporal types of neural coding are compatible with limiting cases of the synaptic information transmission as a function of initial vesicle release probability and pre-synaptic firing rate. The effects of the nonlinear dependencies of the vesicle release probability on intracellular calcium concentration and number of available vesicles are analysed. The model is compared with phenomenological and reduced models, a principal advantage being the capability of also determining fluctuations of dynamic variables

Calcium↗

PubMed2003

Modeling of pain using artificial neural networks.

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.

Acute Disease↗

PubMed1984

Mechanical loading histories and cortical bone remodeling.

A conceptual framework is presented for understanding and investigating structural adaptation of cortical bone. The magnitudes, orientations, and sense (tension or compression) of the physiologically incurred cyclic principal strains vary markedly throughout the skeleton. It is probable, therefore, that the strain/remodeling response of bone is site specific. Furthermore, there is some indication that immature bone is more responsive to alterations of cyclic strains than mature bone. Animal experimental studies and complementary stress and strain analyses suggest that the structural adaptation due to changes in cyclic strain fields may be a very nonlinear response. Bone loss in mature animals due to immobilization is sensitive to even small changes in the cyclic bone strains. Under normal conditions, however, there appears to be a broad range of physical activity in which bone is relatively unresponsive to changes in loading history. With severe repeated loading, bone hypertrophy can be pronounced. These observations open the possibility that bone atrophy and hypertrophy are controlled by different mechanisms. Therefore, two (or more) complementary control systems may be involved in the regulation of bone mass by bone cyclic strain histories. It is probable that bone mechanical microdamage is one control stimulus for affecting an increase in bone mass.

Adaptation, Physiological↗

PubMed1985

A program for logistic prediction modelling.

A computer program has been developed that can be used for analysing a binary outcome variable and a set of regressors of type interval with a logistic (i.e. nonlinear) model.

Biometry↗

PubMed1999

Resistance of a food chain to invasion by a top predator.

We study the invasion of a top predator into a food chain in a chemostat. For each trophic level, a bioenergetic model is used in which maintenance and energy reserves are taken into account. Bifurcation analysis is performed on the set of nonlinear ordinary differential equations which describe the dynamic behaviour of the food chain. In this paper, we analyse how the ability of a top predator to invade the food chain depends on the values of two control parameters: the dilution rate and the concentration of the substrate in the input. We investigate invasion by studying the long-term behaviour after introduction of a small amount of top predator. To that end we look at the stability of the boundary attractors; equilibria, limit cycles as well as chaotic attractors using bifurcation analysis. It will be shown that the invasibility criterion is the positiveness of the Lyapunov exponent associated with the change of the biomass of the top predator. It appears that the region in the control parameter space where a predator can invade increases with its growth rate. The resulting system becomes more resistant to further invasion when the top predator grows faster. This implies that short food chains with moderate growth rate of the top predator are liable to be invaded by fast growing invaders which consume the top predator. There may be, however, biological constraints on the top predator's growth rate. Predators are generally larger than prey while larger organisms commonly grow slower. As a result, the growth rate generally decreases with the trophic level. This may enable short food chains to be resistant to invaders. We will relate these results to ecological community assembly and the debate on the length of food chains in nature.

Animals↗

PubMed2001

Synthesis and structure-activity relationships of a new model of arylpiperazines. Study of the 5-HT(1a)/alpha(1)-adrenergic receptor affinity by classical hansch analysis, artificial neural networks, and computational simulation of ligand recognition.

A classical quantitative structure-activity relationship (Hansch) study and artificial neural networks (ANNs) have been applied to a training set of 32 substituted phenylpiperazines with affinity for 5-HT(1A) and alpha(1)-adrenergic receptors, to evaluate the structural requirements that are responsible for 5-HT(1A)/alpha(1) selectivity. The resulting models provide a significant correlation of electronic, steric, and hydrophobic parameters with the biological affinities. Although the derived linear Hansch correlations give good statistics and acceptable predictions, the introduction of nonlinear relationships in the analysis gives more solid models and more accurate predictions. In the ANN models on the basis of the obtained 3D plots, the 5-HT(1A) affinity has a nonlinear dependence on F, V(o), V(m), and pi(o), although the nonlinear relationship is not far from a planar one. The alpha(1)-adrenergic receptor affinity has a clear nonlinear dependence on F, V(o), V(m), pi(o), and pi(m). A comparison of both analyses gives an additional understanding for 5-HT(1A)/alpha(1) selectivity: (a) high F values increase the binding affinity for 5-HT(1A) receptors and decrease the affinity for alpha(1) sites; (b) the hydrophobicity at the meta-position has only influence for the alpha(1)-adrenergic receptor; (c) the meta-position seems to be implicated in the 5-HT(1A)/alpha(1) selectivity. While the 5-HT(1A) receptor is able to accommodate bulky substituents in the region of its active site, the steric requirements of the alpha(1)-adrenergic receptor at this position are more restricted. This information was used for the design of the new ligand EF-7412 (33) (5-HT(1A): K(i exptl) = 27 nM, alpha(1): K(i exptl) > 1000 nM; 5-HT(1A): K(i pred) (ANN) = 36 nM, alpha(1): K(i pred ANN) = 2745 nM) which was characterized as an antagonist in vivo in pre- and postsynaptic 5-HT(1A)R sites. Computational simulations of the complex between EF-7412 (33) and a 3D model of the transmembrane domain of the 5-HT(1A) receptor allowed us to define the molecular details of the ligand-receptor interaction that includes: (i) the ionic interaction between the protonated amine of the ligand and Asp 3.32; (ii) the hydrogen bonds between the m-NHSO(2)Et group of the ligand and Asn 7.39; and the hydrogen bonds between the hydantoin moiety of the ligand and (iii) Thr 3.37, (iv) Ser 5.42, and (v) Thr 5.43. These QSAR and ANN results in combination with computational simulations of ligand recognition will be useful for the design of potent selective 5-HT(1A) ligands.

Animals↗

PubMed1997

Correlation between bactericidal activity and postantibiotic effect for five antibiotics with different mechanisms of action.

Theoretically, if the postantibiotic effect (PAE) reflects the duration of cellular recovery, then the extent of cellular damage inflicted on bacteria by an antibiotic, as suggested by the degree of bactericidal activity, should reflect the length of PAE; this is especially true if binding of the antibiotic to bacterial receptors is irreversible. To test this hypothesis, correlation between PAEs and bactericidal rate constants measured simultaneously at various antibiotic concentrations was examined for five antibiotic-bacterium combinations. Each of the five antibiotics used, i.e. tobramycin, ciprofloxacin, dicloxacillin, trimethoprim and tetracycline, has a different mechanism of action: the first three bind irreversibly to bacterial receptors, while trimethoprim and tetracycline bind reversibly. Both PAE and bactericidal activity increased nonlinearly with concentrations in a saturable manner for all the combinations studied. Linear least-square regression analyses showed strong correlations (P < 0.01) between the two responses for individual combinations. Such a linear relationship also extended, with good correlation (P < 0.05), across the five combinations when individual maximal bactericidal rate constants and PAEs were considered separately. These observations suggest that cellular recovery from nonlethal damage following antibiotic exposure may be a major determinant of PAE.

Anti-Bacterial Agents↗

PubMed2001

New data and tools for integrating discrete and continuous population modeling strategies.

Realistic population models have interactions between individuals. Such interactions cause populations to behave as systems with nonlinear dynamics. Much population data analysis is done using linear models assuming no interactions between individuals. Such analyses miss strong influences on population behavior and can lead to serious errors--especially for infectious diseases. To promote more effective population system analyses, we present a flexible and intuitive modeling framework for infection transmission systems. This framework will help population scientists gain insight into population dynamics, develop theory about population processes, better analyze and interpret population data, design more powerful and informative studies, and better inform policy decisions. Our framework uses a hierarchy of infection transmission system models. Four levels are presented here: deterministic compartmental models using ordinary differential equations (DE); stochastic compartmental (SC) models that relax assumptions about population size and include stochastic effects; individual event history models (IEH) that relax the SC compartmental structure assumptions by allowing each individual to be unique. IEH models also track each individual's history, and thus, allow the simulation of field studies. Finally, dynamic network (DNW) models relax the assumption of the previous models that contacts between individuals are instantaneous events that do not affect subsequent contacts. Eventually it should be possible to transit between these model forms at the click of a mouse. An example is presented dealing with Cryptosporidium. It illustrates how transiting model forms helps assess water contamination effects, evaluate control options, and design studies of infection transmission systems using nucleotide sequences of infectious agents.

Data Interpretation, Statistical↗

PubMed1984

Androgen cytosol binding in exercise-induced sparing of muscle atrophy.

This study was undertaken to determine whether the exercise-induced sparing of glucocorticoid-induced muscle atrophy is related to increased androgen cytosol binding. Female rats were divided into a sedentary or an exercise group that was trained by treadmill running 100 min/day for 13-15 wk. During the last 12 days of training, each of these groups was further subdivided into groups that received daily subcutaneous injections of cortisone acetate (CA) (100 mg/kg body wt) or the vehicle 1% carboxymethyl cellulose. Exercise prevented 30-40% of the weight loss due to CA treatment in gastrocnemius and plantaris muscles. Scatchard analyses of specific binding of [3H]methyltrienolone (R1881), a synthetic androgen that binds to androgen receptors, were nonlinear in muscles from vehicle-treated sedentary and trained rats and were resolved by a two-component binding model. The lower affinity component, which was attributed to a glucocorticoid receptor, disappeared in muscles of glucocorticoid-treated animals as evidenced by linear Scatchard plots. Receptor concentrations of the androgenic component of [3H]methyltrienolone binding were similar in gastrocnemius and plantaris muscles in all treatment groups. In binding specificity studies of gastrocnemius muscles, the relatively high competition by various glucocorticoids and progesterone for [3H]methyltrienolone binding in the vehicle-treated groups was reduced by CA treatment. The lack of change in androgen cytosol receptor levels suggests that this is not a mechanism by which exercise protects against glucocorticoid-induced muscle atrophy.

Androgens↗

PubMed1976

Relationships between skeletal maturity and submaximal working capacity in boys 8 to 18 years.

Skeletal age (SA) height, weight, PWC130, Vo2 at 130 heart rate (Vo130) and cardiorespiratory adaptation to work at 3 kgm-sec-1 were measured in 237 boys ranging in age from 8 to 18 years. Correlations for the total population for the variables of the study are generally higher with SA than with chronological age (CA), although there is no significant difference between the two sets of correlations. Analysis of linear trend indicate that significant nonlinear components are more frequent in the relationships between the variables and SA than with CA.Multiple regression analyses for each variate as dependent variable indicate that height and body weight generally contribute more to the explained variance, while SA is the next best predictor. CA is the least significant of the four independent variables. The total variance explained between any of the submaximal working capacity related measures acting as dependent variable and CA, height and weight is not significantly affected by the presence or absence of SA. The present study thus indicates that SA is not a significant factor in explaining submaximal working capacity, beyond CA, height and weight, when considered over the 8 to 18 year age span. However, when considering shorter age spans, or when considering the adolescent period (12-16 years), SA correlates better with PWC130 (.320 less than r less than .675) and with Vo130 (.299 less than r less than .772).

Adolescent↗

PubMed2004

Survival of Listeria monocytogenes strain H7762 and resistance to simulated gastric fluid following exposure to frankfurter exudate.

Listeria monocytogenes strain H7762, a frankfurter isolate, was tested to determine whether it was able to survive at 4 degrees C in frankfurter pack fluid (exudate) and to determine whether food exposure affects its acid sensitivity. Cultures were sampled and tested for acid sensitivity by challenge with simulated gastric fluid (SGF). SGF challenges performed immediately after inoculation revealed that between 20 and 26% of the cells survived the full 30 min of SGF challenge regardless of whether the cells were inoculated into brain heart infusion broth (BHI) or exudate. After 2 days of incubation, cells exposed to both exudate and BHI had significantly decreased SGF resistance; however, the cells exposed to exudate were significantly more SGF resistant than cells exposed to BHI (after 15 min of SGF treatment, 33% of the exudate-exposed cells survived and 12% of the BHI-exposed cells survived). L. monocytogenes exposed to exudate had greater SGF resistance at all challenge times compared with BHI-exposed cells from day 2 through day 4. From days 8 to 15, exudate-exposed cells continued to have greater SGF resistance than BHI-exposed cells up to 10 min of SGF challenge but were as sensitive as the BHI-exposed cells at 20 to 30 min of challenge. By day 25, cells exposed to exudate were significantly more sensitive to SGF challenge than BHI-exposed cells. The survivor data generated from SGF challenges were modeled by a nonlinear regression analysis to calculate the underlying distribution of SGF resistance found in the challenged populations. These analyses indicated that L. monocytogenes exposed to exudate at 4 degrees C had a broader distribution of resistance to SGF compared with cells exposed to BHI at 4 degrees C. In addition, the mean time of death during SGF treatment was greater after exposure to exudate, indicating that cells exposed to exudate were more resistant to killing by SGF These data suggest that exposure to frankfurter exudate might render L. monocytogenes more able to survive the stomach environment during the initial stages of infection.

Adaptation, Physiological↗

PubMed2000

Nonlinear EEG analysis and its potential role in epileptology.

Deterministic chaos offers a striking explanation for apparently irregular behavior of the brain that is evidenced in the EEG. Recent developments in the physical-mathematical framework of the theory of nonlinear dynamics (colloquially often termed chaos theory) provide new concepts and powerful algorithms to analyze such time series. Because of its high versatility, nonlinear time series analysis has already gone beyond the physical sciences and, at present, is being successfully applied in a variety of disciplines, including cardiology, neurology, psychiatry, and epileptology. However, it is well known that different influencing factors limit the use of nonlinear measures to characterize EEG dynamics in a strict sense. Nevertheless, when interpreted with care, relative estimates of, e.g., the correlation dimension or the Lyapunov exponents, can reliably characterize different states of normal and pathologic brain function. In epileptology, extraction of nonlinear measures from the intracranially recorded EEG promises to be important for clinical practice. In addition to an immense reduction of information content of long-lasting EEG recordings, previous studies have shown that these measures enable (a) localization of the primary epileptogenic area in different cerebral regions during the interictal state, (b) investigations of antiepileptic drug effects, (c) analyses of spatio-temporal interactions between the epileptogenic zone and other brain areas, and (d) detection of features predictive of imminent seizure activity. Nonlinear time series analysis provides new and supplementary information about the epileptogenic process and thus contributes to an improvement in presurgical evaluation.

Brain Mapping↗

PubMed2026

Assessing the comorbidity between asthma and depression through polygenic risk scoring and time-to-event models.

BACKGROUND: Patients with asthma have an increased risk of developing depression, affecting their quality of life. To date, the processes contributing to this comorbidity remain unclear. METHODS: We integrated two large genome-wide association studies (88,486 patients with asthma and 447,859 controls; 412,024 patients with depression and 1,587,577 controls) with cross-sectional and longitudinal information available from the All of Us Research Program (N = 87,167) through polygenic risk scoring (PRS), Cox proportional-hazards models, one-sample Mendelian randomization (MR), and gene-set and drug-repurposing analyses. RESULTS: We observed that depression PRS was associated with increased asthma risk (hazard ratio, HR = 1.13, 95% CI = 1.09-1.17), also when accounting for comorbidity status (HR = 1.08, 95% CI = 1.04-1.12). Conversely, the effect of asthma PRS was null after accounting for comorbidity status. One-sample MR analysis showed an effect of depression genetic liability on asthma, ranging from beta = 0.36 ± 0.03 when considering a linear relationship to beta = 3.21 ± 0.31 when considering possible nonlinear relationships. Conversely, the effect of asthma genetic risk on depression was null after accounting for potential confounders. The gene-set analyses showed that asthma and depression polygenic risks share biological processes, molecular functions, and cellular components related to the immune system and the lung-brain axis. CONCLUSIONS: Genetic predisposition contributes to asthma-depression comorbidity through direct effects and shared pathogenic processes. These findings highlight the potential to develop targeted interventions to prevent and treat the co-occurrence of respiratory and neuropsychiatric disorders.

Comorbidity↗

PubMed1982

Genetic regulation of plasma and red blood cell magnesium concentrations in man. I. Univariate and bivariate path analyses.

This paper concerns an analysis of family resemblance for magnesium concentrations, based on data from nuclear families and twins. Neither red blood cell magnesium nor plasma magnesium varies with age in children (under 20 years of age). Whereas adult plasma magnesium varies linearly with age, the red cell magnesium clearly showed a nonlinear trend: quadratic for males and a fifth-degree polynomial for females. Transformed magnesium concentrations generated six correlations in nuclear families and twins for each of the two traits. Separate univariate analyses, using a simple linear model with four parameters, strongly suggested that genetic factors are primarily responsible for the observed family resemblance. Both traits were then analyzed simultaneously using a simple bivariate model. We found that one common genetic factor alone could not explain all the 24 correlations generated for the bivariate analysis. The most parsimonious model involved only three parameters: genetic heritability for red blood cell magnesium (.922 +/- .014), genetic heritability for plasma magnesium (.721 +/- .040), and the genetic correlation between the two traits (.233 +/- .040).

Age Factors↗

PubMed2004

[Synthesis, spectra, nonlinear optical properties and theoretical calculations of metal complexes containing phenothiazine ligand].

A new ligand, S-benzyl-(-N-[10-ethylphenothiazinyl-3yl]-methylene] dithiocarbazate (abbreviated as HL), and its complexes ML2 (M=Co and Hg) have been synthesized and fully characterized by elemental analyses, EI-MS, 1H NMR, IR, UV-Vis spectra, and photoluminescence measurement. All of the compounds emit an olivine color in DMF solution. The emission maximum (lambdamax(em)), and fluorescence quantum (phif) of each compound were measured. Nonlinear Optical properties of the HL and ML2 were studied by z-scan technique. Nonlinear absorption coefficient beta were measured to be 0.71, 0.19 and 0.395 cm x GW(-1). Theoretical calculations (RHF/PM3) were consistent with the experimental results.

Coordination Complexes↗

PubMed2006

Nonlinear regression approach to evaluate nutrient delivery coefficient.

Implementation of the Korean Total Maximum Daily Load Act calls for new tools to quantify nutrient losses from diffuse sources at a river basin district scale. In this study, it was elucidated that the nonlinear regression model (NRM) reduces the uncertainty of the boundary conditions of the water quality model. The NRM was proposed to analyse the delivery coefficients of surface waters and retention coefficients of pollutants. Delivery coefficient of pollution load was considered as a function of two variables: the watershed form ratio, S(f), which is a measurable geomorphologic variable and the retention coefficient, phi, which is an empirical constant representing the basin-wide retarding capacity of pollutant wash-off. This model was applied on the Geum River, one of the major basins in South Korea. The QUAL2E was used to simulate stream water quality using NRM. In this paper, we elucidate the possibility to use a nonlinear regression model for delivery and retention of nutrients in a drainage basin characterized as both data-rich and data-poor, and the magnitude of the nutrient loads and sources has been uncertain for a long time.

Environmental Monitoring↗

PubMed2005

Predicting clinical variable from MRI features: application to MMSE in MCI.

The ability to predict a clinical variable from automated analysis of single, cross-sectional T1-weighted (T1w) MR scans stands to improve the management of patients with neurological diseases. We present a methodology for predicting yearly Mini-Mental Score Examination (MMSE) changes in Mild Cognitive Impairment (MCI) patients. We begin by generating a non-pathological, multidimensional reference space from a group of 152 healthy volunteers by Principal Component Analyses of (i) T1w MR intensity of linearly registered Volumes of Interest (VOI); and (ii) trace of the deformation fields of nonlinearly registered VOIs. We use multiple regression to build linear models from eigenvectors where the projection eigencoordinates of patient data in the reference space are highly correlated with the clinical variable of interest. In our cohort of 47 MCI patients, composed of 16 decliners, 26 stable and 5 improvers (based on MMSE at 1 yr follow-up), there was a significant difference (P = 0.0003) for baseline MMSE scores between decliners and improvers, but no other differences based on age or sex. First, we classified our three groups using leave-one-out, forward stepwise linear discriminant analyses of the projection eigencoordinates with 100% accuracy. Next, we compared various linear models by computing F-statistics on the residuals of predicted vs actual values. The best model was based on 10 eigenvectors + baseline MMSE, with predicted yearly changes highly correlated (r = 0.6955) with actual data. Prospective study of an independent cohort of patients is the next logical step towards establishing this promising technique for clinical use.

Artificial Intelligence↗