Thresholds in development

The interpretation of gradients in positional information is considered in terms of thresholds in cell responses, giving rise to cell states which are discrete and persistent. Equilibrium models based on co-operative binding of control molecules do not show true thresholds of discontinuity, though with a very high degree of co-operativity they could mimic them; in any case they do not provide the cells with any memory of a transient signal. A simple kinetic model based upon positive feedback can account both for memory and for discontinuities in the pattern of cell states. The model is an example of a bistable control circuit, and transitions from one state to another may be brought about not only by morphogenetic signals, but also by disturbances in the parameters determining the kinetics of the system. This might explain some aspects of transdetermination in insects.An attempt is made to analyse the precision with which a spatial gradient of a diffusible morphogen could be interpreted by a kinetic threshold mechanism, in terms of the length of the field, the steepness of the concentration gradient, and the intrinsic random variability of cells. It is concluded that it would be possible to specify as many as 30 distinct cell states in a positional field 1 mm long with a concentration span of 10³. Mechanisms for reducing the positional error are considered.     

Normalization strategies for metabonomic analysis of urine samples

Unlike plasma and most biological fluids which have solute concentrations that are tightly controlled, urine volume can vary widely based upon water consumption and other physiological factors. As a result, the concentrations of endogenous metabolites in urine vary widely and normalizing for these effects is necessary. Normalization approaches that utilized urine volume, osmolality, creatinine concentration, and components that are common to all samples (“total useful MS signal”) were compared in order to determine which strategies could be successfully used to differentiate between dose groups based upon the complete endogenous metabolite profile. Variability observed in LC/MS results obtained from targeted and non-targeted metabonomic analyses was highly dependent on the strategy used for normalization. We therefore recommend the use of two different normalization techniques in order to facilitate detection of statistically significant changes in the endogenous metabolite profile when working with urine samples.     

Towards the Disease Biomarker in an Individual Patient Using Statistical Health Monitoring

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current–population based –clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.     

Mathematical modelling of dynamics and control in metabolic networks. V. Static bifurcations in single biochemical control loops

Here we expand an earlier study of feedback activation in simple linear reaction sequences by searching the parameter space of biologically realistic rate laws for multiple stable steady states. The impetus for this work is to seek the origin of decision making strategies at the metabolic level, with particular emphasis on the switching between the operating conditions needed to meet changing substrate availability and organism requirements. The control loop considered herein is a linear reaction chain in which the end product of the reaction sequence feedback activates the first reaction in the sequence to produce feedback control. It has been found that the criteria for the existence of multiple steady state solutions in such loops involve only the kinetics of the regulatory enzyme controlling the first reaction and that of end product removal. The effects of these kinetics are examined here using two representative models for the regulatory enzyme: the lumped controller, based on Hill-type kinetics, and the symmetry model. The behavior of these two models is qualitatively similar, and both show the characteristics needed for switching between low and high substrate utilization. The removal rate is assumed to be of the Michaelis-Menten type. Judicious scaling of the governing equations permits separation of genetically determined kinetic parameters from concentration dependent ones. This allows us to conclude that, for a fixed set of kinetic parameters, the steady state flux through the loop can be switched between stable steady states by merely varying metabolite or enzyme concentrations. In particular, when the initial substrate exceeds a certain critical level, the loop can be "switched on" (by a discontinuous increase in the flux through the chain), and similarly, when it falls below a critical level, the pathway is shut down. Similar effects can be realized by varying the ratios of enzyme concentrations. It is proposed that by identifying these critical points one can gain significant insight into the objectives of decision making at the metabolic level.     

Assessment of rate constants for isolating a metabolite by a model-independent method]

A model-independent method for estimating an elimination rate constant of a metabolite of exogenous substance is suggested as an alternative to known methods. The new method (named the initial slope method) uses blood (plasma) concentration-time data of both the substance and the metabolite obtained after an extravascular impulse input of the substance. The metabolite input is not needed substantially facilitating the experiment. The method is based upon the assessment of areas under the substance and metabolite concentration-time curves, the initial substance concentration, and the initial slope of the metabolite concentration-time curve. The method was tested using artificial data generated on the basis of a compartment model for the substance and metabolite kinetics. It was shown providing nonbiased estimates of a true metabolite elimination rate constant irrespective of the structure of the model used to generate data. Other methods failed to provide such estimates.     

High-throughput quantification of microbial birth and death dynamics using fluorescence microscopy

Background: Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness in terms of birth rate and death rate in a wide range of environments is critical for understanding microbial evolution and ecology. Methods: Here, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite (auxotrophs) grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing. Results: For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate compared to no lysine. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates can vary with metabolite concentration and time. Conclusions: Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial birth and death dynamics and to quantify growth rates in many environments.     

FIBS-enabled Noninvasive Metabolic Profiling

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be validated for predictive purposes. Ideally such systems would be low volume, which precludes sampling and destructive analyses when time course data are to be obtained. What is needed is an in situ monitoring tool which can report the necessary information in real-time and noninvasively. An interesting option is the use of fluorescent, protein-based in vivo biological sensors as reporters of intracellular concentrations. One particular class of in vivo biosensors that has found applications in metabolite quantification is based on Förster Resonance Energy Transfer (FRET) between two fluorescent proteins connected by a ligand binding domain. FRET integrated biological sensors (FIBS) are constitutively produced within the cell line, they have fast response times and their spectral characteristics change based on the concentration of metabolite within the cell. In this paper, the method for constructing Chinese hamster ovary (CHO) cell lines that constitutively express a FIBS for glucose and glutamine and calibrating the FIBS in vivo in batch cell culture in order to enable future quantification of intracellular metabolite concentration is described. Data from fed-batch CHO cell cultures demonstrates that the FIBS was able in each case to detect the resulting change in the intracellular concentration. Using the fluorescent signal from the FIBS and the previously constructed calibration curve, the intracellular concentration was accurately determined as confirmed by an independent enzymatic assay.     

Generally applicable fed-batch culture concept based on the detection of metabolic state by on-line balancing

In many microorganisms, flux limitations in oxidative metabolism lead to the formation of overflow metabolites even under fully aerobic conditions. This can be avoided if the specific growth rate is controlled at a low enough value. This is usually accomplished by controlling the substrate feeding profile in a fed-batch process. The present work proposes a control concept which is based on the on-line detection of metabolic state by on-line calculation of mass and elemental balances. The advantages of this method are: 1) the check of measurement consistency based on all of the available measurements, 2) the minimum requirement of a priori knowledge of metabolism, and 3) the exclusive use of simple and established on-line techniques which do not require direct measurement of the metabolite in question. The control concept has been linked to a simple adaptive controller and applied to fed-batch cultures of S. cerevisiae and E. coli, organisms which express different overflow metabolites, ethanol and acetic acid, respectively. Oxidative and oxidoreductive states of S. cerevisiae and E. coli cultures were detected with high precision. As demonstrated by the formation of acetic acid in E. coli cultures, metabolic states could be correctly distinguished for systems for which traditional methods, such as respiratory quotient (RQ), are insensitive. Hence, it could be shown that the control concept allowed avoidance of overflow metabolite formation and operation at maximum oxidative biomass productivity and oxidative conversion of substrate into biomass. Based on mass and elemental balances, the proposed method additionally provides a richness of additional information, such as yield coefficients and estimation of concentrations and specific conversion rates. These data certainly help the operator to additionally evaluate the state of the process on-line.     

What's wrong with novel ecosystems,really?

The novel ecosystems concept has gained much traction in the restoration community. It has also drawn the ire of several prominent ecologists and is the focus of an ongoing debate. We consider three key aspects of this debate: irreversible thresholds, non‐native species, and the hybrid state. Irreversible thresholds have been acknowledged in restoration for years, but predicting when a threshold will be crossed and the degree of reversibility is problematic. Oftentimes reversibility is a function of multiple factors, such as cost and public support. In this sense, a novel ecosystem is not an alternate state but a decision. The need for pragmatism regarding control of non‐natives has also long been recognized in restoration circles. Proponents of the novel ecosystem idea adopt this pragmatism by recommending that management decisions be based on impacts conferred by species in altered ecosystems, regardless of their origin. The concept of a hybrid state has proven difficult to operationalize. We suggest that rather than trying to identify the boundary between hybrid and novel states, ecosystems exist on a gradient of alteration. We offer a decision tree for restoration action that integrates aspects of novel ecosystems with other perspectives in modern restoration ecology. We conclude that the idea of novel ecosystems, though not perfect, deserves a place under the “big tent” of restoration that includes efforts to return fully to a reference state, as well as strategies for reinstating lost ecological processes and enhancing ecosystem services in transformed landscapes where such a return is deemed infeasible.     

Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis

ABSTRACT: BACKGROUND: An approach to molecular classification based on the comparative expression of protein pairs is presented.The method overcomes some of the present limitations in using peptide intensity data for class prediction forproblems such as the detection of a disease, disease prognosis, or for predicting treatment response. Dataanalysis is particularly challenging in these situations due to sample size (typically tens) being much smallerthan the large number of peptides (typically thousands). Methods based upon high dimensional statisticalmodels, machine learning or other complex classifiers generate decisions which may be very accurate butcan be complex and difficult to interpret in simple or biologically meaningful terms. A classificationscheme, called ProtPair, is presented that generates simple decision rules leading to accurate classificationwhich is based on measurement of very few proteins and requires only relative expression values, providingspecific targeted hypotheses suitable for straightforward validation. RESULTS: ProtPair has been tested against clinical data from 21 patients following a bone marrow transplant, 13 ofwhich progress to idiopathic pneumonia syndrome (IPS). The approach combines multiple peptide pairsoriginating from the same set of proteins, with each unique peptide pair providing an independent measureof discriminatory power. The prediction rate of the ProtPair for IPS study as measured by leave-one-out CVis 69.1%, which can be very beneficial for clinical diagnosis as it may flag patients in need of closer monitoring. The "top ranked" proteins provided by ProtPair are known to be associated with the biologicalprocesses and pathways intimately associated with known IPS biology based on mouse models. CONCLUSIONS: An approach to biomarker discovery, called ProtPair, is presented. ProtPair is based on the differentialexpression of pairs of peptides and the associated proteins. Using mass spectrometry data from "bottom up"proteomics methods, functionally related proteins/peptide pairs exhibiting co-ordinated changes expressionprofile are discovered, which represent a signature for patients progressing to various disease conditions.The method has been tested against clinical data from patients progressing to idiopthatic pneumoniasyndrome (IPS) following a bone marrow transplant. The data indicates that patients with improperregulation in the concentration of specific acute phase response proteins at the time of bone marrowtransplant are highly likely to develop IPS within few weeks. The results lead to a specific set of proteinpairs that can be efficiently verified by investigating the pairwise abundance change in independent cohortsusing ELISA or targeted mass spectrometry techniques. This generalized classifier can be extended to otherclinical problems in a variety of contexts.     

The continuously fed batch reactor for measuring microbial growth rates

The contiuously fed batch reactor (CFBR) is proposed as an alternative technique to the traditional chemostat and batch cultures, for measuring microbial growth rates. After reviewing the pitfalls which plague the conventional growth measurement techniques, the methodology for operating the CFBR to generate specific growth-rate-versus-substrate-concentration data is detailed. This information is extracted from the transient state of the CFBR where both the biomass and substrate concentration show extrema in time. It is suggested that the CFBR can be used for measuring microbial growth rates at low rates at low substrate concentrations where the chemostat method normally encounters difficulties.     

Numerical modelling of label-structured cell population growth using CFSE distribution data

Background

Drug-drug interactions resulting from the inhibition of an enzymatic process can have serious implications for clinical drug therapy. Quantification of the drugs internal exposure increase upon administration with an inhibitor requires understanding to avoid the drug reaching toxic thresholds. In this study, we aim to predict the effect of the CYP3A4 inhibitors, itraconazole (ITZ) and its primary metabolite, hydroxyitraconazole (OH-ITZ) on the pharmacokinetics of the anesthetic, midazolam (MDZ) and its metabolites, 1' hydroxymidazolam (1OH-MDZ) and 1' hydroxymidazolam glucuronide (1OH-MDZ-Glu) using mechanistic whole body physiologically-based pharmacokinetic simulation models. The model is build on MDZ, 1OH-MDZ and 1OH-MDZ-Glu plasma concentration time data experimentally determined in 19 CYP3A5 genotyped adult male individuals, who received MDZ intravenously in a basal state. The model is then used to predict MDZ, 1OH-MDZ and 1OH-MDZ-Glu concentrations in an CYP3A-inhibited state following ITZ administration.

Results

For the basal state model, three linked WB-PBPK models (MDZ, 1OH-MDZ, 1OH-MDZ-Glu) for each individual were elimination optimized that resulted in MDZ and metabolite plasma concentration time curves that matched individual observed clinical data. In vivo K_m and V_max optimized values for MDZ hydroxylation were similar to literature based in vitro measures. With the addition of the ITZ/OH-ITZ model to each individual coupled MDZ + metabolite model, the plasma concentration time curves were predicted to greatly increase the exposure of MDZ as well as to both increase exposure and significantly alter the plasma concentration time curves of the MDZ metabolites in comparison to the basal state curves. As compared to the observed clinical data, the inhibited state curves were generally well described although the simulated concentrations tended to exceed the experimental data between approximately 6 to 12 hours following MDZ administration. This deviations appeared to be greater in the CYP3A5 *1/*1 and CYP3A5 *1/*3 group than in the CYP3A5 *3/*3 group and was potentially the result of assuming that ITZ/OH-ITZ inhibits both CYP3A4 and CYP3A5, whereas in vitro inhibition is due to CYP3A4.

Conclusion

This study represents the first attempt to dynamically simulate metabolic enzymatic drug-drug interactions via coupled WB-PBPK models. The workflow described herein, basal state optimization followed by inhibition prediction, is novel and will provide a basis for the development of other inhibitor models that can be used to guide, interpret, and potentially replace clinical drug-drug interaction trials.     

Probability and Persistence of High Pollutant Concentrations in Soils: A Modeling Study and Implications for Exposure and Risk Assessment

Risk assessments for environmental pollutants have relied upon steady-state models that do not represent the variability of pollutant transport and fate processes, thus predictions are unlikely to reflect the true variability in pollutant concentrations. Such models cannot be used to estimate the probability, magnitude and duration of short- to intermediate-term and high-concentration events that might lead to adverse acute impacts. In this study, a numerical model is used to simulate pollutant accumulation in surface soils at six U.S. locations that result from atmospheric deposition and leaching. Historical (50 year) precipitation data drive the model. Model predictions are filtered and analyzed to identify high pollution events (exceeding specific concentration thresholds) and their occurrence probability and duration. Predicted concentrations at each site varied by a factor of 100 over time and by a factor of five among the six locations. The frequency and duration of high pollution events also differed by locations and concentration threshold. In general, larger thresholds lead to less frequent events and shorter durations. The proposed method allows estimates of the probability of occurrence and duration of high pollution events, providing information that complements the steady-state methods.     

CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.     

Two subgroups of lung vagal C-fibers with different vulnerabilities to blockades by perivagal capsaicin and vagal cooling in dogs

Perivagal capsaicin treatment and vagal cooling are two techniques that have been widely used to study the respiratory reflexes mediated by lung vagal C-fibers because they can block the neural conduction of unmyelinated fibers. We hypothesized that there are two subgroups of lung vagal C-fibers which have different vulnerabilities to blockades by these two techniques. To test this hypothesis, afferent activity arising from lung vagal C-fibers was recorded in 29 anesthetized, paralyzed, and artificially ventilated dogs. Afferent C-fiber activity was recorded before and after various concentrations of perivagal capsaicin treatment or before and during various temperatures of vagal cooling. Of the 89 lung vagal C-fibers studied, 73 fibers were classified as the group of "low resistance" to capsaicin, while the other 16 were classified as the group of "high resistance". The former group differed from the latter due to their afferent activity being blocked at relatively low concentrations of perivagal capsaicin and at relatively low temperatures of vagal cooling. Our results suggest that lung vagal C-fibers can be categorized into two subgroups, based upon their different blocking thresholds for perivagal capsaicin and vagal cooling. Our data may provide information for researchers to further differentiate the respiratory reflexes originating from these two subgroups of lung vagal C-fibers.     

Mining gene expression data based on template theory

MOTIVATION: It is understood that clustering genes are useful for exploring scientific knowledge from DNA microarray gene expression data. The explored knowledge can be finally used for annotating biological function for novel genes. Representing the explored knowledge in an efficient manner is then closely related to the classification accuracy. However, this issue has not yet been paid the attention it deserves. RESULT: A novel method based on template theory in cognitive psychology and pattern recognition is developed in this study for representing knowledge extracted from cluster analysis effectively. The basic principle is to represent knowledge according to the relationship between genes and a found cluster structure. Based on this novel knowledge representation method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct a classifier for annotating biological functions of novel genes. The experiments on five published datasets show that this method has improved the classification performance compared with the conventional method. The statistical tests indicate that this improvement is significant. AVAILABILITY: The software package can be obtained upon request from the author.     

An Environmentally Conscious Decision Support System for Life-Cycle Management

An Internet-based environmentally conscious decision support tool (EcoDS) has been developed for life-cycle management EcoDS involves an initial vertical streamlining step, where the significant life-cycle stages, stressors, and impact categories are selected and cross-correlated. Because the streamlining is performed prior to the inventory, the approach expedites data collection. Comparisons among alternative product designs or manufacturing processes are based on two metrics: financial risk (or cost) and "residual" risk. For purposes of evaluation these two indicators are individually aggregated using a user or organization-specified value system. A salient feature of EcoDS is that this output can be condensed into a single summary matrix akin to a hybrid pro forma income statement and environmental balance sheet. The clear delineation between the tradeoffs involved in each alternative facilitates decision making by upper management. A case study on painting attematives is presented to illustrate the methodology