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 β-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

Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation

BACKGROUND: The data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects. RESULTS: In this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges. CONCLUSIONS: This paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation

TRANSWESD: inferring cellular networks with transitive reduction

Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic

A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data.

Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study

Carotenoid Production Process Using Green Microalgae of the <i>Dunaliella</i> Genus: Model-Based Analysis of Interspecies Variability

The engineering of photosynthetic bioprocesses is associated with many hurdles due to limited mechanistic knowledge and inherent biological variability. Because of their ability to accumulate high amounts of β-carotene, green microalgae of the <i>Dunaliella</i> genus are of high commercial relevance for the production of food, feed, and high-value fine chemicals. This work aims at investigating the interspecies differences between two industrially relevant <i>Dunaliella</i> species, namely <i>D. salina</i> and <i>D. parva</i>. A systematic work flow composed of experiments and mathematical modeling was developed and applied to both species. The approach combining flow cytometry and pulse amplitude modulation (PAM) fluorometry with biochemical methods enabled a coherent view on the metabolism during the adaptational stress response of <i>Dunaliella</i> under carotenogenic conditions. The experimental data was used to formulate a dynamic-kinetic reactor model that covered the effects of light and nutrient availability on biomass growth, internal nutrient status, and pigment fraction in the biomass. Profile likelihood analysis was performed to ensure the identifiability of the model parameters and to point out targets for model reduction. The experimental and computational results revealed significant variability between <i>D. salina</i> and <i>D. parva</i> in terms of morphology, biomass, and β-carotene productivity as well as differences in photoacclimation and photoinhibition. The synergistic approach combining experimental and mathematical methods provides a systems-level understanding of the microalgal carotenogenesis under fluctuating environmental conditions and thereby drive the development of sustainable and economically feasible phototrophic processes

Optimal Reactor Design via Flux Profile Analysis for an Integrated Hydroformylation Process

Different operational modes, various scales, and complex phenomena make the design of a chemical process a challenging task. Besides conducting basic lab experiments and deriving fundamental kinetic and thermodynamic models, a crucial task within the entire process design is the synthesis of an optimal reactor-network constituting the core of a chemical process. However, instead of directly up-scaling the process to large devices, it is wise to investigate process characteristics on the miniplant scale. For an existing miniplant for the hydroformylation of 1-dodecene using a rhodium catalyst and a thermomorphic solvent system for catalyst recovery, two optimized reactor designs are derived. Suitable reactor-networks were synthesized by applying the <i>Flux Profile Analysis</i> approach introduced in Kaiser et al. (2017). The combination of a first reactor with dynamic/distributed control options and a subsequent back-mixed continuous stirred tank reactor (CSTR) arose to be the most promising configurations. The technical design under miniplant conditions was carried out for two possible realizations of this network, namely (i) a continuous flow reactor and (ii) a periodically operated semibatch reactor, both followed by the existing CSTR which was originally operated in the miniplant. An optimization of the two optimal reactor configurations within an overall process including a liquid–liquid phase separation for catalyst recovery and a distillation column for separating the solvents and reactant evinced a selectivity with respect to the linear aldehyde around 94% and a conversion around 98%. This is a large improvement of the process performance of 24% linear aldehyde selectivity and 40% conversion when using the existing CSTR

From feature to model selection.

<p>(A-C) Mutual information maximization (MIM) and Fisher criterion applied on linear separable and nonseparable two-class problems (red and green). (D-G) In contrast to the correlation coefficient mutual information (MI) recognizes linear and nonlinear dependencies between random variables. (H-M) The classification of a two class problem (red and green) using classifiers with different complexities display variations concerning the decision boundary. (N) The reshuffling of training and validation set (cross validation) helps to judge the model stability. For a performance check, the best model is used to classify the test set, which consist of so far unseen data not used for model selection. (O) To find the model best suited for discrimination (black arrow) the sum of two sources of errors (black, dashed) has to be minimized. Bias (light gray, dashed) decreases with increasing model complexity, whereas variance (dark gray, solid) increases.</p

Cell state classification for imaging flow cytometry.

<p>(A) Machine learning is used to predict the unknown composition of cellular populations (<i>e.g.</i> viable (green) and apoptotic (red)). (B) The combination of experimental expertise (1, 2, 3) and data mining (3, 4, 5) facilitates the derivation of useful information from huge amounts of biological data.</p