Hormone-induced calcium oscillations in liver cells can be explained by a simple one pool model

Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model.     

Temperature dependence of the epidermal growth factor receptor signaling network can be accounted for by a kinetic model.

Stimulation of isolated hepatocytes with epidermal growth factor (EGF) causes rapid tyrosine phosphorylation of the EGF receptor (EGFR) and adapter/target proteins, which was monitored with 1 and 2 s resolution at 37, 20, and 4 degrees C. The temporal responses detected for multiple signaling proteins involve both transient and sustained phosphorylation patterns, which change dramatically at low temperatures. To account quantitatively for complex responses, we employed a mechanistic kinetic model of the EGFR pathway, formulated in molecular terms as cascades of protein interactions and phosphorylation and dephosphorylation reactions. Assuming differential temperature dependencies for different reaction groups, such as SH2 and PTB domain-mediated interactions, the EGFR kinase, and the phosphatases, good quantitative agreement was obtained between computer-simulated and measured responses. The kinetic model demonstrates that, for each protein-protein interaction, the dissociation rate constant, k(off), strongly decreases at low temperatures, whereas this decline may or may not be accompanied by a large decrease in the k(on) value. Temperature-induced changes in the maximal activities of the reactions catalyzed by the EGFR kinase were moderate, compared to such changes in the V(max) of the phosphatases. However, strong changes in both the V(max) and K(m) for phosphatases resulted in moderate changes in the V(max)/K(m) ratio, comparable to the corresponding changes in EGFR kinase activity, with a single exception for the receptor phosphatase at 4 degrees C. The model suggests a significant decrease in the rates of the EGF receptor dimerization and its dephosphorylation at 4 degrees C, which can be related to the phase transition in the membrane lipids. A combination of high-resolution experimental monitoring and molecular level kinetic modeling made it possible to quantitatively account for the temperature dependence of the integrative signaling responses.     

Cell orientation by a microgrooved substrate can be predicted by automatic control theory

Cells have the ability to measure and respond to extracellular signals like chemical molecules and topographical surface features by changing their orientation. Here, we examined the orientation of cultured human melanocytes exposed to grooved topographies. To predict the cells' orientation response, we describe the cell behavior with an automatic controller model. The predicted dependence of the cell response to height and spatial frequency of the grooves is obtained by considering the symmetry of the system (cell + substrate). One basic result is that the automatic controller responds to the square of the product of groove height and spatial frequency or to the aspect ratio for symmetric grooves. This theoretical prediction was verified by the experiments, in which melanocytes were exposed to microfabricated poly(dimethylsiloxane) substrates having parallel rectangular grooves of heights (h) between 25 and 200 nm and spatial frequencies (L) between 100 and 500 mm(-1). In addition, the model of the cellular automatic controller is extended to include the case of different guiding signals acting simultaneously.     

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

Background  

Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.     

Cell cycle of Chlorella vulgaris can deviate from the synchronous binary division model

Conditioned medium from high density Chlorella vugaris cultures was freeze dried extracted into ethanol. The ethanol was volatilized and the resulting powder then introduced with C. vulgaris photoautotrophic cultures which underwent asynchronous DNA replication forming cells with two, three, four, and six autospores instead of 2 n characteristic of synchronous DNA replication. These cells were blocked at the division stage of the cell cycle.     

T-cell activation by soluble MHC oligomers can be described by a two-parameter binding model

T-cell activation is essential for initiation and control of immune system function. T cells are activated by interaction of cell-surface antigen receptors with major histocompatibility complex (MHC) proteins on the surface of other cells. Studies using soluble oligomers of MHC-peptide complexes and other types of receptor cross-linking agents have supported an activation mechanism that involves T cell receptor clustering. Receptor clustering induced by incubation of T cells with MHC-peptide oligomers leads to the induction of T-cell activation processes, including downregulation of engaged receptors and upregulation of the cell-surface proteins CD69 and CD25. Dose-response curves for these T-cell activation markers are bell-shaped, with different maxima and midpoints, depending on the valency of the soluble oligomer used. In this study, we have analyzed the activation behavior using a mathematical model that describes the binding of multivalent ligands to cell-surface receptors. We show that a simple equilibrium binding model accurately describes the activation data for CD4(+) T cells treated with MHC-peptide oligomers of varying valency. The model can be used to predict activation and binding behavior for T cells and MHC oligomers with different properties.     

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km²) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes'' predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10⁻⁶) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO₂ were significantly correlated with observed surface CO₂ fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ∼3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology.     

Trout coelomic fluid suitability as Goldfish oocyte extender can be determined by a simple turbidity test

Regeneration technologies such as androgenesis, intracytoplasmic sperm injection, and nuclear transfer require that handling conditions do not alter oocyte ability to sustain embryo development. One important parameter in the maintenance of oocyte quality in fish is the possibility to prevent oocytes activation during manipulation. In Cyprinid, such activation is known to be delayed when Salmonid coelomic fluid is used as incubation medium. Coelomic fluid however is a biological fluid whose ability to sustain oocyte quality during in vitro incubation may be variable. The purpose of the present work was to explore this variability using Rainbow Trout (Oncorhynchus mykiss) coelomic fluid (TCF) and Goldfish (Carassius auratus) oocytes, and to set up a test which would reflect TCF suitability for Goldfish oocyte incubation. We showed that different TCF induced very different development rates after oocyte incubation for 30 min at 20 °C: at 24h post fertilization (pf) and at hatching, rates ranged between 35% and 110% of the non-incubated controls. When TCF (1 volume) was mixed with tap water (9 volumes), a precipitate developed whose extent was measured by spectrophotometry. This turbidity test proved to be highly correlated to development rates after Goldfish oocyte incubation in TCF (r² = 0.83 at hatching, n = 150): TCF with the highest turbidity (> 1.5 absorbance unit at 400 nm) were the ones which altered the most the development rates after incubation (less than 50 % at hatching). This easy and rapid turbidity test can therefore be used as a reliable estimator of TCF suitability for Goldfish oocyte incubation and manipulation.     

Glycosyltransferase activity can be selectively modulated by chemical modifications of acceptor substrates

A range of N-acetyllactosamine derivatives, which are modified by a wide range of functionalities at C-2(') and C-6, have been synthesised and the kinetic parameters of transfer catalysed by recombinant alpha-2,6-sialyltransferase and alpha-1,3-fucoyltransferase VI determined. Several of the chemical modifications led to selective modulate the activity the enzymes and offer promising lead compounds for the development of oligosaccharide primers for selective metabolic inhibition of oligosaccharide biosynthesis.     

Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.     

Infant zygosity can be assigned by parental report questionnaire data.

A parental report questionnaire posted to a population sample of 18-month-old twins correctly assigned zygosity in 95%of cases when validated against zygosity determined by identity of polymorphic DNA markers. The questionnaire was as accurate when readministered at 3 years of age, with 96% of children being assigned the same zygosity on both occasions. The results validate the use of parental report questionnaire data to determine zygosity in infancy.     

The probability that complex enzyme kinetic curves can be caused by activators or inhibitors

Numerous chemical compounds are known that alter the rate of conversion of substrates into products in enzyme-catalysed reactions by interacting with the enzyme rather than substrates. Where this takes place in such a way that the effect is reversible on removing the compound, say by dialysis, and where the compound is unchanged chemically by the enzyme system, we refer to such a compound as a modifier. So protons, inorganic salts, activators, inhibitors or even specific allosteric effectors would all be modifiers, and any chemically reasonable kinetic scheme that is proposed to account for such effects is referred to as modifier mechanism. Three versions of a modifier mechanism of enzyme action are studied. The implicit representation is 2:2 in [S] (with α₀=0) and 2:2 in [M] (with α₀≠0), and this is a short-hand scheme for the minimum chemical formulation, the explicit one, involving discrete ES and EP species, which is 2:2 in [S] (with α₀=0) and 3:3 in [M] (with α₀≠0). If m extra steps are allowed between interconversion of ES and EP species, the degree of the rate equation remains 2:2 in [S] (with α₀=0), but increases to degree (m+3):(m+3) in modifier (with α₀≠0). It is proved that this increase in degree is genuine and that highly complex v([M]) (i.e. v-versus-[M]) curves can occur. Computation of the probabilities of the five possible double-reciprocal plots in 1/v versus 1/[S] show that all of these formulations of the modifier mechanism give similar probabilities, and these are characteristic for the mechanism and quite distinct from the intrinsic curve-shape probabilities. It is also established that the probabilities of alternative complex v([M]) plots are similar for the various formulations, and again the probabilities of the allowed complex curves for the mechanism are quite distinct from the instrinsic probabilities of the ten possible v([M]) curves for a 2:2 function (with α₀≠0). The computer studies reported lead to several conclusions about the probability of modifiers leading to inhibition or activation or causing changes in v([S]) curve shapes, and suggest that differentiation between model mechanisms may be facilitated by knowledge of the intrinsic curve-shape probabilities for the appropriate degree rational function and the characteristic way that this is altered by specific mechanisms. It is shown that, although in some instances new curve-shape complexities are possible when schemes are considered that allow for interconversion of ES and EP species, these are highly improbable and, for theoretical purposes, schemes formulated with node compression provide good approximations to the more complicated explicit schemes. By node compression we refer to the procedure whereby enzyme kinetic schemes are simplified by replacing sequences of steps such as ESX₁X₂...EP... by a single step... ES/EP... that does not formally recognize the existence of the intermediate species. We show that the modifier mechanism studied is one where this process alters the form of the rate equation.     

High dopamine transporter selectivity can be displayed by remarkably simple non-nitrogen containing inhibitors

A series of 2-(3,4-dichlorophenyl)-cyclopent-1-enyl carboxylic acid esters and amides were prepared and tested for binding to the DAT, SERT, and NET. The achiral compounds were easily attained and found to inhibit DAT binding with K(i)-values ranging from 0.095 to 0.00003 mM. Among the compounds tested 2-(3,4-dichlorophenyl)-cyclopent-1-enyl carboxylic acid 2-methylphenyl ester was found to be highly selective with SERT/DAT>7000; NET/DAT>1700, K(i)=60 nM.     

On testing structural identifiability by a simple scaling method: Relying on scaling symmetries can be misleading

A recent paper published in PLOS Computational Biology [1] introduces the Scaling Invariance Method (SIM) for analysing structural local identifiability and observability. These two properties define mathematically the possibility of determining the values of the parameters (identifiability) and states (observability) of a dynamic model by observing its output. In this note we warn that SIM considers scaling symmetries as the only possible cause of non-identifiability and non-observability. We show that other types of symmetries can cause the same problems without being detected by SIM, and that in those cases the method may lead one to conclude that the model is identifiable and observable when it is actually not.     

A simple kinetic model for dynein oscillatory activity

A kinetic model was proposed for dynein, a motor protein, complexed with microtubule fragments. The model explains the experimental observations of oscillatory movements in surprisingly simple axoneme fragments perfused with an ATP solution. This is the first model explaining the oscillatory activity of dynein as determined by a cooperative interaction of two dynein heads in the axoneme. The oscillation shape, frequency, and amplitude obtained for the model are close to the corresponding parameters determined experimentally.     

A simple kinetic model for dynein oscillatory activity

A kinetic model for dynein, a molecular motor, complexed with microtubule fragments, is considered. The model explains the experimental observations of oscillatory movements in surprisingly simple axonemal fragments perfused by the ATP solution. The model explains at first time the oscillatory dynein activity as a phenomenon induced by two dynein heads cooperative interaction in the axoneme. The oscillation form, frequency, and amplitude, observed for the model, are close to these experimental characteristics. Kinetic parameters, used in the model, are close to the known experimental parameters.