The interaction of protein-coated bionanoparticles and surface receptors reevaluated: how important is the number of bonds?

Specifically designed bionanoparticles with a function-oriented protein-coating layer interact with self-prepared receptor surfaces as the counterpart. Based on surface plasmon resonance biosensing experiments, a model framework is validated to estimate the number of bonds formed between these bionanoparticles and the receptor surface based on multivalent interactions. Our multi-site kinetic model is able to analyze the adsorption rate constants and the number of bonds from experimental data of natural and synthetic bionanoparticles. The influence of the mass transport on the adsorption kinetics is modeled including a diffusional boundary layer where a helpful analytical solution has been derived. Our model framework extends previous studies to include a higher number of bonds, ranging from 1 up to 1000. An almost linear relationship between the number of bonds and the adsorption amount of bionanoparticles makes the model framework suitable to predict, for example, ligand density and to further assess coating performance. The proposed model framework can serve as a design tool for multivalent interaction experiments under variable process conditions.

Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae

BACKGROUND

Photosynthetic organisms can be used for renewable and sustainable production of fuels and high-value compounds from natural resources. Costs for design and operation of large-scale algae cultivation systems can be reduced if data from laboratory scale cultivations are combined with detailed mathematical models to evaluate and optimize the process.

RESULTS

In this work we present a flexible modeling formulation for accumulation of high-value storage molecules in microalgae that provides quantitative predictions under various light and nutrient conditions. The modeling approach is based on dynamic flux balance analysis (DFBA) and includes regulatory models to predict the accumulation of pigment molecules. The accuracy of the model predictions is validated through independent experimental data followed by a subsequent model-based fed-batch optimization. In our experimentally validated fed-batch optimization study we increase biomass and [Formula: see text]-carotene density by factors of about 2.5 and 2.1, respectively.

CONCLUSIONS

The analysis shows that a model-based approach can be used to develop and significantly improve biotechnological processes for biofuels and pigments.

Binding kinetics and multi-bond: Finding correlations by synthesizing interactions between ligand-coated bionanoparticles and receptor surfaces

The number of bonds formed between one single bionanoparticle and many surface receptors is an important subject to be studied but is seldom quantitatively investigated. A new evaluation of the correlation between binding kinetics and number of bonds is presented by varying ligand density and receptor density. An experimental system was developed using measurements with surface plasmon resonance spectroscopy. A corresponding multi-site adsorption model elucidated the correlation. The results show that with the increase of the receptor density, the adsorption rate first decreased when the number of bonds was below a maximum value and then increased when the number of bonds stayed at this maximum value. The investigation on ligand density variation suggests that the coating density on top of the bionanoparticle surface may have a particular value below which more ligand will accelerate the adsorption rate. The ratio of ligand amount bound by the receptors to the total ligand amount associated with a single bionanoparticle will remain constant even if one attaches more ligands to a bionanoparticle. We envision that the bionanoparticle desorption will not depend on density changes from either ligand or receptor when the number of bonds reaches a specific efficient value.

Comparison of flocculation methods for harvesting Dunaliella

Low cell concentrations of Dunaliella salina in production scale cultivations require high energy input for biomass harvesting. Flocculation is a potential preconcentration method to lower the dewatering costs for the β-carotene production. In the present study, optimal flocculant dosages were determined for several metal salts, NaOH, Ca(OH)2 and Al-electrolysis. Beside harvesting efficiency ηH and concentration factor CF, also the recyclability of the separated medium as well as the influence of the cell physiology on the harvesting performance were analyzed for selected flocculants. To assess the possible recycle of non-sedimented cells for the inoculation of new cultivations, cell vitality and the photosynthetic activity of D. salina were analyzed after the flocculation. As a result, the flocculation with NaOH led to a clear inhibition of both, the algal growth on recycled medium and the algal photosynthetic activity. The addition of FeCl3 seems most promising to flocculate D. salina.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions

Background

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort.

Results

In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts.

Conclusions

Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0436-5) contains supplementary material, which is available to authorized users.

A dynamic growth model of Dunaliella salina: parameter identification and profile likelihood analysis

In this work, a photoautotrophic growth model incorporating light and nutrient effects on growth and pigmentation of Dunaliella salina was formulated. The model equations were taken from literature and modified according to the experimental setup with special emphasis on model reduction. The proposed model has been evaluated with experimental data of D. salina cultivated in a flat-plate photobioreactor under stressed and non-stressed conditions. Simulation results show that the model can represent the experimental data accurately. The identifiability of the model parameters was studied using the profile likelihood method. This analysis revealed that three model parameters are practically non-identifiable. However, some of these non-identifiabilities can be resolved by model reduction and additional measurements. As a conclusion, our results suggest that the proposed model equations result in a predictive growth model for D. salina.

Experimental design, validation and computational modeling uncover DNA damage sensing by DNA-PK and ATM

Reliable and efficient detection of DNA damage constitutes a vital capability of human cells to maintain genome stability. Following DNA damage, the histone variant H2AX becomes rapidly phosphorylated by the DNA damage response kinases DNA-PKcs and ATM. H2AX phosphorylation plays a central role in signal amplification leading to chromatin remodeling and DNA repair initiation. The contribution of DNA-PKcs and ATM to H2AX phosphorylation is however puzzling. Although ATM is required, DNA-PKcs can substitute for it. Here we analyze the interplay between DNA-PKcs and ATM with a computational model derived by an iterative workflow: switching between experimental design, experiment and model analysis, we generated an extensive set of time-resolved data and identified a conclusive dynamic signaling model out of several alternatives. Our work shows that DNA-PKcs and ATM enforce a biphasic H2AX phosphorylation. DNA-PKcs can be associated to the initial, and ATM to the succeeding phosphorylation phase of H2AX resulting into a signal persistence detection function for reliable damage sensing. Further, our model predictions emphasize that DNA-PKcs inhibition significantly delays H2AX phosphorylation and associated DNA repair initiation.

Combined electrochemical and microscopic study of porous enzymatic electrodes with direct electron transfer mechanism

In the present work electrochemical and microscopic methods have been utilized to get more insight into the complex relationship between the preparation route, structure and activity of porous enzymatic electrodes.

An effective framework for reconstructing gene regulatory networks from genetical genomics data

MOTIVATION

Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible.

RESULTS

We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast.

AVAILABILITY

A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html

CONTACT

klamt@mpi-magdeburg.mpg.de.