Can interaction coefficients be determined from census data? Testing two estimation methods with Negev Desert rodents

Two approaches based on regression models are proposed to estimate competition from census data. The "static" approach is based on censuses of population sizes among species at one point in time over many sites. The "dynamic" approach relies on a time series of species abundance data to examine whether per capita changes in one species are associated with the abundance of other species. We estimated competition interactions in a Negev rodent community consisting of 10 species using both approaches, basing on 8 years (16 half-year periods) of observations. The static approach revealed significant competitive interactions in four of 45 pairs of species, whereas the dynamic approach did so in the same four plus two more pairs. For each species pair, both approaches revealed significant negative interactions in only 1–4 of 16 seasons. The static approach provided nearly symmetric estimations of competition, whereas estimations of dynamic approach were asymmetric. Moreover, estimations of the two approaches did not coincide in time. Cases of negative interactions recorded by the static approach were more frequent at peak and increase phases of population density dynamics, whereas those recorded by the dynamic approach were more frequent at peak and decline phases. Results of field removal experiments with Mus musculus and Gerbillus dasyurus supported predictions of dynamic but not static approaches. We hypothesized that in harsh and fluctuating desert environments that disrupt equilibrium, the dynamic approach indicates true (exploitation) competition, whereas the static approach reflects negative interspecific spatial association (interference).     

Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem

Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally.     

Teaching from classic papers: Hill's model of muscle contraction

A. V. Hill's 1938 paper "The heat of shortening and the dynamic constants of muscle" is an enduring classic, presenting detailed methods, meticulous experiments, and the model of muscle contraction that now bears Hill's name. Pairing a simulation based on Hill's model with a reading of his paper allows students to follow his thought process to discover key principles of muscle physiology and gain insight into how to develop quantitative models of physiological processes. In this article, the experience of the author using this approach in a graduate biomedical engineering course is outlined, along with suggestions for adapting this approach to other audiences.     

maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.     

Curve fitting approach for measurement of cellular osmotic properties by the electrical sensing zone method. I. Osmotically inactive volume

We have investigated the confounding effects of dynamic range limitations on measurement of the osmotically inactive volume using electrical sensing zone instruments (e.g., Coulter counters), and propose an improved approach to parameter estimation. The conventional approach for analysis of cell size distributions measured by such particle sizing instruments requires data truncation: the mean cell volume is computed after exclusion of data below a specified lower bound (typically chosen to remove artifacts due to small-volume noise) and above an upper bound (typically governed by instrument limitations). The osmotically inactive volume is then estimated from a Boyle–van’t Hoff plot of the averaged volume data obtained after exposure to various solution osmolalities. We demonstrate that systematic exclusion of data in the conventional approach introduces bias that results in erroneously high estimates of the osmotically inactive volume fraction. To minimize this source of error, we have devised a new algorithm based on fitting a bimodal distribution model to the non-truncated volume data. In experiments with mouse insulinoma (MIN6) cells, the osmotically inactive volume fraction was estimated to be 0.15 ± 0.01 using the new method, which was significantly smaller than the estimate of 0.37 ± 0.02 obtained using the conventional method (p < 0.05). In silico experiments indicated that the parameter estimate obtained by the new method was accurate within 5%, whereas the error associated with the conventional approach was approximately 150%. Parametric analysis was used to elucidate the sensitivity of errors to variations in instrument dynamic range and cell volume distribution width.     

NMR-based fluxomics: quantitative 2D NMR methods for isotopomers analysis

We have investigated the reliability of 2D-COSY and 2D-TOCSY experiments to provide accurate measurements of (13)C-enrichments in complex mixtures of (13)C-labelled metabolites. This was done from both theoretical considerations and experimental investigations. The results showed that 2D-TOCSY but not 2D-COSY could provide accurate measurements of (13)C-enrichments, provided efficient zero-quantum filters were applied during the mixing period. This approach extends the range of NMR methods applicable in (13)C-labelling experiments and is suitable to investigating the dynamic behaviour of metabolic systems.     

Dynamics of the Ethanolamine Glycerophospholipid Remodeling Network

Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE) remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1) sn1 and sn2 acyl positions are independently remodeled; (2) remodeling reaction rates are constant over time; and (3) acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In constrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.     

Real-Time Monitoring of Photocytotoxicity in Nanoparticles-Based Photodynamic Therapy: A Model-Based Approach

Nanoparticles are widely suggested as targeted drug-delivery systems. In photodynamic therapy (PDT), the use of multifunctional nanoparticles as photoactivatable drug carriers is a promising approach for improving treatment efficiency and selectivity. However, the conventional cytotoxicity assays are not well adapted to characterize nanoparticles cytotoxic effects and to discriminate early and late cell responses. In this work, we evaluated a real-time label-free cell analysis system as a tool to investigate in vitro cyto- and photocyto-toxicity of nanoparticles-based photosensitizers compared with classical metabolic assays. To do so, we introduced a dynamic approach based on real-time cell impedance monitoring and a mathematical model-based analysis to characterize the measured dynamic cell response. Analysis of real-time cell responses requires indeed new modeling approaches able to describe suited use of dynamic models. In a first step, a multivariate analysis of variance associated with a canonical analysis of the obtained normalized cell index (NCI) values allowed us to identify different relevant time periods following nanoparticles exposure. After light irradiation, we evidenced discriminant profiles of cell index (CI) kinetics in a concentration- and light dose-dependent manner. In a second step, we proposed a full factorial design of experiments associated with a mixed effect kinetic model of the CI time responses. The estimated model parameters led to a new characterization of the dynamic cell responses such as the magnitude and the time constant of the transient phase in response to the photo-induced dynamic effects. These parameters allowed us to characterize totally the in vitro photodynamic response according to nanoparticle-grafted photosensitizer concentration and light dose. They also let us estimate the strength of the synergic photodynamic effect. This dynamic approach based on statistical modeling furnishes new insights for in vitro characterization of nanoparticles-mediated effects on cell proliferation with or without light irradiation.     

Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables

Dynamic models based on non-linear differential equations are increasingly being used in many biological applications. Highly informative dynamic experiments are valuable for the identification of these dynamic models. The storage of fresh fruit and vegetables is one such application where dynamic experimentation is gaining momentum. In this paper, we construct optimal O₂ and CO₂ gas input profiles to estimate the respiration and fermentation kinetics of pear fruit. The optimal input profiles, however, depend on the true values of the respiration and fermentation parameters. Locally optimal design of input profiles, which uses a single initial guess for the parameters, is the traditional method to deal with this issue. This method, however, is very sensitive to the initial values selected for the model parameters. Therefore, we present a robust experimental design approach that can handle uncertainty on the model parameters.     

The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal

Success in experiments and/or technology mainly depends on a properly designed process or product. The traditional method of process optimization involves the study of one variable at a time, which requires a number of combinations of experiments that are time, cost and labor intensive. The Taguchi method of design of experiments is a simple statistical tool involving a system of tabulated designs (arrays) that allows a maximum number of main effects to be estimated in an unbiased (orthogonal) fashion with a minimum number of experimental runs. It has been applied to predict the significant contribution of the design variable(s) and the optimum combination of each variable by conducting experiments on a real-time basis. The modeling that is performed essentially relates signal-to-noise ratio to the control variables in a 'main effect only' approach. This approach enables both multiple response and dynamic problems to be studied by handling noise factors. Taguchi principles and concepts have made extensive contributions to industry by bringing focused awareness to robustness, noise and quality. This methodology has been widely applied in many industrial sectors; however, its application in biological sciences has been limited. In the present review, the application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.     

Inference of dynamic biological networks based on responses to drug perturbations

Drugs that target specific proteins are a major paradigm in cancer research. In this article, we extend a modeling framework for drug sensitivity prediction and combination therapy design based on drug perturbation experiments. The recently proposed target inhibition map approach can infer stationary pathway models from drug perturbation experiments, but the method is limited to a steady-state snapshot of the underlying dynamical model. We consider the inverse problem of possible dynamic models that can generate the static target inhibition map model. From a deterministic viewpoint, we analyze the inference of Boolean networks that can generate the observed binarized sensitivities under different target inhibition scenarios. From a stochastic perspective, we investigate the generation of Markov chain models that satisfy the observed target inhibition sensitivities.     

A theoretical approach to "macroscopic fluctuations" effect

At present there is no generally accepted theory of the effect of macroscopic fluctuations. It the article, an attempt is made to relate this effect to some basic properties of quantum systems, in particular, to the formal absence of dynamic chaos in these systems. Based on this approach, it was shown why the level of the effect must be of the same order of magnitude as the level of noise and cannot exceed this level as the number of experiments increases. It was shown qualitatively what is the cause of the similarity of histograms observed in different experiments.     

Direct measurement of single-molecule visco-elasticity in atomic force microscope force-extension experiments

Measuring the visco-elastic properties of biological macromolecules constitutes an important step towards the understanding of dynamic biological processes, such as cell adhesion, muscle function, or plant cell wall stability. Force spectroscopy techniques based on the atomic force microscope (AFM) are increasingly used to study the complex visco-elastic response of (bio-)molecules on a single-molecule level. These experiments either require that the AFM cantilever is actively oscillated or that the molecule is clamped at constant force to monitor thermal cantilever motion. Here we demonstrate that the visco-elasticity of single bio-molecules can readily be extracted from the Brownian cantilever motion during conventional force-extension measurements. It is shown that the characteristics of the cantilever determine the signal-to-noise (S/N) ratio and time resolution. Using a small cantilever, the visco-elastic properties of single dextran molecules were resolved with a time resolution of 8.3 ms. The presented approach can be directly applied to probe the dynamic response of complex bio-molecular systems or proteins in force-extension experiments. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Multigradient method for optimization of slow biotechnological processes

A new method (named a "jumping spider") is introduced for the optimization of slow biotechnological processes. The more traditional sequential experimentation (i.e., gradient search, simplex, etc.) is not well suited for slow dynamic processes, e.g., plant cell culture and differentiation. Therefore, a more simultaneous approach is proposed. A large number of initial experiments are performed, on the basis of which several of the initial experiments are selected as starting points. A search is then performed simultaneously from several gradient directions and the optimum is estimated by a quadratic approximation. In simulations, the spider generally climbs up the slopes quickly and the final estimator yields good maximum point estimates even on a complex topography. The spider may even approach more than one local maximum point simultaneously. As a model application, the average xylitol conversion rate of Candida guilliermondii was optimized in relation to cultivation volume (oxygen availability) and the concentration of nitrogen and phosphorus in the medium. A threefold increase in xylitol production was obtained with three experimental steps. (c) 1993 John Wiley & Sons, Inc.     

Opposition site selection and clutch size in insects

Oviposition site selection and clutch size in parasitic insects can be viewed as problems in foraging theory. In this paper, a number of models for site selection and clutch size are developed, based on a dynamic state variable approach to optimal oviposition strategies. The models lead to predictions that are consistent with existing experimental data and suggest future experiments. Using these models shows the importance of constraints and state variables in the analysis of behavioral problems.     

Highly multiplexed targeted proteomics using precise control of peptide retention time

Large-scale proteomics applications using SRM analysis on triple quadrupole mass spectrometers present new challenges to LC-MS/MS experimental design. Despite the automation of building large-scale LC-SRM methods, the increased numbers of targeted peptides can compromise the balance between sensitivity and selectivity. To facilitate large target numbers, time-scheduled SRM transition acquisition is performed. Previously published results have demonstrated incorporation of a well-characterized set of synthetic peptides enabled chromatographic characterization of the elution profile for most endogenous peptides. We have extended this application of peptide trainer kits to not only build SRM methods but to facilitate real-time elution profile characterization that enables automated adjustment of the scheduled detection windows. Incorporation of dynamic retention time adjustments better facilitate targeted assays lasting several days without the need for constant supervision. This paper provides an overview of how the dynamic retention correction approach identifies and corrects for commonly observed LC variations. This adjustment dramatically improves robustness in targeted discovery experiments as well as routine quantification experiments.     

A dynamic approach to tolerate soft errors

Dynamic implementation for software-based soft error tolerance method which can protect more types of codes can cover more soft errors. This paper explores soft error tolerance with dynamic software-based method. We propose a new dynamic software-based approach to tolerate soft errors. In our approach, the objective which is protected is dynamic program. For those protected dynamic binary codes, we make sure right control flow and right data flow to significant extent in our approach. Our approach copies every data and operates every operation twice to ensure those data stored into memory are right. Additionally, we ensure every branch instruction can jump to the right address by checking condition and destination address. Our approach is implemented by the technique dynamic binary instrumentation. Specifically, our tool is implemented on the basis of valgrind framework which is a heavyweight dynamic binary instrumentation tool. Our experimental results demonstrate that our approach can get higher reliability of dynamic software than those approaches which is implemented with static program protection method. However, our approach is only suitable for the system which has a strict requirement of reliability because our approach also sacrifices more performance of software than those static program protection methods.     

Identifying type III effectors of plant pathogens and analyzing their interaction with plant cells

Many bacterial pathogens cause disease by injecting virulence proteins (effectors) into host cells via the specialized type III secretion system. Recently, exceptional progress in identifying effectors was made in the phytopathogen Pseudomonas syringae using a novel genetic screen and bioinformatic approach. These studies, along with localization experiments, suggest that most P. syringae effectors function by targeting the plasma membrane, chloroplasts or mitochondria of host cells. The type III secretome of P. syringae is highly variable and dynamic, a lesson gleaned from a comparative genomic analysis. Variation in the effector repertoire is likely to facilitate the adaptation of P. syringae to different hosts.     

Kinetic and dynamic computational model-based characterization of new proteins in mice: application to interferon alpha linked to apolipoprotein a-I

Interferon alpha linked to apolipoprotein A-I has been recently proposed as an improved interferon-based therapy. In the present study, we aimed to develop a computational model to gain further insight into the in vivo behaviour of this new fusion protein. In order to facilitate in vivo evaluation of interferon and the fusion protein without altering their biological properties, green fluorescent protein was incorporated into their structures. Kinetic and dynamic behaviour of both compounds was successfully described after plasmid hydrodynamic administration and in situ synthesis of the studied proteins. Results from the modelling exercise showed that apolipoprotein A-I conferred a modified kinetic behaviour, varying molecule distribution and prolonging half-life without altering liver dynamic performance. However, differences in the gene expression activity were observed at brain level between both compounds. Those differences could be explained by modifications in the dynamic, but also in the biodistribution properties, which would be worth evaluating in future experiments. Therefore, the modelling approach provided a global comprehension of a complex system and allowed us to compare the in vivo behaviour of both compounds and to identify critical aspects that might be important to understand the system better and suggests a need for new model-based experiments.     

Network slicing to improve multicasting in HPC clusters

In high performance computing (HPC) resources’ extensive experiments are frequently executed. HPC resources (e.g. computing machines and switches) should be able to handle running several experiments in parallel. Typically HPC utilizes parallelization in programs, processing and data. The underlying network is seen as the only non-parallelized HPC component (i.e. no dynamic virtual slicing based on HPC jobs). In this scope we present an approach in this paper to utilize software defined networking (SDN) to parallelize HPC clusters among the different running experiments. We propose to accomplish this through two major components: A passive module (network mapper/remapper) to select for each experiment as soon as it starts the least busy resources in the network, and an SDN-HPC active load balancer to perform more complex and intelligent operations. Active load balancer can logically divide the network based on experiments’ host files. The goal is to reduce traffic to unnecessary hosts or ports. An HPC experiment should multicast, rather than broadcast to only cluster nodes that are used by the experiment. We use virtual tenant network modules in Opendaylight controller to create VLANs based on HPC experiments. In each HPC host, virtual interfaces are created to isolate traffic from the different experiments. The traffic between the different physical hosts that belong to the same experiment can be distinguished based on the VLAN ID assigned to each experiment. We evaluate the new approach using several HPC public benchmarks. Results show a significant enhancement in experiments’ performance especially when HPC cluster experiences running several heavy load experiments simultaneously. Results show also that this multi-casting approach can significantly reduce casting overhead that is caused by using a single cast for all resources in the HPC cluster. In comparison with InfiniBand networks that offer interconnect services with low latency and high bandwidth, HPC services based on SDN can provide two distinguished objectives that may not be possible with InfiniBand: The first objective is the integration of HPC with Ethernet enterprise networks and hence expanding HPC usage to much wider domains. The second objective is the ability to enable users and their applications to customize HPC services with different QoS requirements that fit the different needs of those applications and optimize the usage of HPC clusters.