A Data-Driven Space-Time-Parameter Reduced-Order Model with Manifold Learning for Coupled Problems: Application to Deformable Capsules Flowing in Microchannels.

An innovative data-driven model-order reduction technique is proposed to model dilute micrometric or nanometric suspensions of microcapsules, i.e., microdrops protected in a thin hyperelastic membrane, which are used in Healthcare as innovative drug vehicles. We consider a microcapsule flowing in a similar-size microfluidic channel and vary systematically the governing parameter, namely the capillary number, ratio of the viscous to elastic forces, and the confinement ratio, ratio of the capsule to tube size. The resulting space-time-parameter problem is solved using two global POD reduced bases, determined in the offline stage for the space and parameter variables, respectively. A suitable low-order spatial reduced basis is then computed in the online stage for any new parameter instance. The time evolution of the capsule dynamics is achieved by identifying the nonlinear low-order manifold of the reduced variables; for that, a point cloud of reduced data is computed and a diffuse approximation method is used. Numerical comparisons between the full-order fluid-structure interaction model and the reduced-order one confirm both accuracy and stability of the reduction technique over the whole admissible parameter domain. We believe that such an approach can be applied to a broad range of coupled problems especially involving quasistatic models of structural mechanics.

Integrated Multiomics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs.

Data-driven research led by computational systems biology methods, encompassing bioinformatics of multiomics datasets and mathematical modeling, are critical for discovery. Herein, we describe a multiomics (metabolomics-fluxomics) approach as applied to heart function in diabetes. The methodology presented has general applicability and enables the quantification of the fluxome or set of metabolic fluxes from cytoplasmic and mitochondrial compartments in central catabolic pathways of glucose and fatty acids. Additionally, we present, for the first time, a general method to reduce the dimension of detailed kinetic, and in general stoichiometric models of metabolic networks at the steady state, to facilitate their optimization and avoid numerical problems. Representative results illustrate the powerful mechanistic insights that can be gained from this integrative and quantitative methodology.

A new approach to define optimized range of medium composition for enhancement of hairy root production in fed-batch process.

This work proposes a new methodology to identify the best medium concentrations for fed-batch production of hairy root using Datura innoxia as a model. Firstly, the role of each component on the growth rate is investigated separately. Then, an experimental design allows refining the optimization studying the interactions between the major species. The result analysis let to define concentration range optimized for fed-batch process. The work novelties lie in two aspects. Firstly, concentrations have been kept constant during each run. Thus, biomass uptakes do not affect the optimization and the growth rate is maintained constant during the exponential phase. Secondly, the effects of salts are generally studied. In this work, the influences of each ion are investigated in order to avoid bias due to the counter-ion effects. Compared to the classical B(5) medium, the optimized medium shows a significant improvement leading to more than 80% increase of final biomass production.

13C-Metabolic Flux Analysis in Developing Flax (Linum usitatissinum L.) Embryos to Understand Storage Lipid Biosynthesis.

Flax (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3). This polyunsaturated fatty acid is well known for its nutritional role in human and animal diets. Understanding storage lipid biosynthesis in developing flax embryos can lead to an increase in seed yield via marker-assisted selection. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryo development, a comprehensive analysis of metabolic flux in flax is still lacking. In this context, we have utilized in vitro cultured developing embryos of flax and determined net fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic fluxes were estimated by computer-aided modeling of the central metabolic network including 11 cofactors of 118 reactions of the central metabolism and 12 pseudo-fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our hands, glucose was determined to be the main source of carbon in flax embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-metabolic flux analysis provided new insights into the flax embryo metabolic processes involved in storage lipid biosynthesis. The elucidation of the metabolic network of this important crop plant reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Modeling and optimization of hairy root growth in fed-batch process.

This article proposes a feeding strategy based on a kinetic model to enhance hairy roots growth. A new approach for modeling hairy root growth is used, considering that there is no nutrient limitation thanks to an appropriate feeding, and the intracellular pools are supposed to be always saturated. Thus, the model describes the specific growth rate from extracellular concentration of the major nutrients and nutrient uptakes depend on biomass growth. An optimized feeding strategy was determined thanks to the model to maintain the major nutrient levels at their optimum assuming optimal initial concentrations. The optimal feed rate is computed in open loop using kinetic model prediction or in closed loop using conductivity measurements to estimate biomass growth. Datura innoxia was chosen as the model culture system. Shake flask cultures were used to calibrate the model. Finally, cultures in bioreactor were performed to validate the model and the control laws.