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Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Introduction: Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on three-layered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power. Methods: This study compares, for the first time, deep and shallow hybrid modeling in a CHO process development context. Data of 24 fed-batch cultivations of a CHO-K1 cell line expressing a target glycoprotein, comprising 30 measured state variables over time, were used to compare both methodologies. Hybrid models with varying FFNN depths (3-5 layers) were systematically compared using two training methodologies. The classical training is based on the Levenberg-Marquardt algorithm, indirect sensitivity equations and cross-validation. The deep learning is based on the Adaptive Moment Estimation Method (ADAM), stochastic regularization and semidirect sensitivity equations. Results and conclusion: The results point to a systematic generalization improvement of deep hybrid models over shallow hybrid models. Overall, the training and testing errors decreased by 14.0% and 23.6% respectively when applying the deep methodology. The Central Processing Unit (CPU) time for training the deep hybrid model increased by 31.6% mainly due to the higher FFNN complexity. The final deep hybrid model is shown to predict the dynamics of the 30 state variables within the error bounds in every test experiment. Notably, the deep hybrid model could predict the metabolic shifts in key metabolites (e.g., lactate, ammonium, glutamine and glutamate) in the test experiments. We expect deep hybrid modeling to accelerate the deployment of high-fidelity digital twins in the biopharma sector in the near future.

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

Tendon injuries can be difficult to heal and have high rates of relapse due to stress concentrations caused by scar formation and the sutures used in surgical repair. Regeneration of the tendon/ligament-to-bone interface is critical to provide functional graft integration after injury. The objective of this study is to recreate the tendon-to-bone interface using a gradient scaffold which is fabricated by a one-station electrospinning process. Two cell phenotypes were grown on a poly-ε-caprolactone nanofiber scaffold which possesses a gradual transition from random to aligned nanofiber patterns. We assessed the effects of the polymer concentration, tip-to-collector distance, and electrospinning time on the microfiber diameter and density. Osteosarcoma and fibroblast cells were seeded on the random and aligned sections of scaffolds, respectively. A random-to-aligned cocultured tissue interface which mimicked the native transition in composition of enthesis was created after 96 h culturing. The results showed that the microstructure gradient influenced the cell morphology, tissue topology, and promoted enthesis formation. This study demonstrates a heterogeneous nanofiber scaffold strategy for interfacial tissue regeneration. It provides a potential solution for mimicking transitional interface between distinct tissues, and can be further developed as a heterogeneous cellular composition platform to facilitate the formation of multi-tissue complex systems.

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.

DNA has recently emerged as an attractive medium for archival data storage. Recent work has demonstrated proof-of-principle prototype systems; however, very uneven (biased) sequencing coverage has been reported, which indicates inefficiencies in the storage process. Deviations from the average coverage in the sequence copy distribution can either cause wasteful provisioning in sequencing or excessive number of missing sequences. Here, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that the two paramount sources of bias are the synthesis and amplification (PCR) processes. Based on these findings, we develop a statistical model for each molecular process as well as the overall process. We further use our model to explore the trade-offs between synthesis bias, storage physical density, logical redundancy, and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems. DNA is an attractive digital data storing medium due to high information density and longevity. Here the authors use millions of sequences to investigate inherent biases in DNA synthesis and PCR amplification.

Microglia maintain homeostasis in the brain. However, with age, they become primed and respond more strongly to inflammatory stimuli. We show here that microglia from aged mice had upregulated mTOR complex 1 signaling controlling translation, as well as protein levels of inflammatory mediators. Genetic ablation of mTOR signaling showed a dual yet contrasting effect on microglia priming: it caused an NF-κB-dependent upregulation of priming genes at the mRNA level; however, mice displayed reduced cytokine protein levels, diminished microglia activation, and milder sickness behavior. The effect on translation was dependent on reduced phosphorylation of 4EBP1, resulting in decreased binding of eIF4E to eIF4G. Similar changes were present in aged human microglia and in damage-associated microglia, indicating that upregulation of mTOR-dependent translation is an essential aspect of microglia priming in aging and neurodegeneration.

Differentiated sex chromosomes are frequently associated with major transcriptional changes: the evolution of dosage compensation (DC) to equalize gene expression between the sexes and the establishment of meiotic sex chromosome inactivation (MSCI). Our study investigates the mechanisms and developmental dynamics of dosage compensation and meiotic sex chromosome inactivation in the stick insect species T. poppense . Stick insects are characterized by XX/X0 sex determination, with an X chromosome that likely evolved prior to the diversification of insects over 450 Mya. We generated a chromosome-level genome assembly and analyzed gene expression from various tissues (brain, gut, antennae, leg, and reproductive tract) across developmental stages in both sexes. Our results show that complete dosage compensation is maintained in male somatic tissues throughout development, mediated by upregulation of the single X chromosome. Contrarily, in male reproductive tissues, dosage compensation is present only in the early nymphal stages. As males reach the 4th nymphal stage and adulthood, X-linked gene expression diminishes, coinciding with the onset of meiosis and MSCI, which involves classical silencing histone modifications. These findings reveal the dynamic regulation of X-linked gene expression in T. poppense , and suggest that reduced X-expression in insect testes is generally driven by MSCI rather than an absence of dosage compensation mechanisms. Our work provides critical insights into sex chromosome evolution and the complex interplay of dosage compensation and MSCI across tissues and developmental stages.

Ixodes scapularis ticks are expanding their range in parts of northeastern North America, bringing with them pathogens of public health concern. While rodents like the white-footed mouse, Peromyscus leucopus , are considered the primary reservoir of many emerging tick-borne pathogens, the contribution of birds, as alternative hosts and reservoirs, to local transmission cycles has not yet been firmly established. From 2016 to 2018, we collected host-seeking ticks and examined rodent and bird hosts for ticks at 48 sites in a park where blacklegged ticks are established in Quebec, Canada, in order to characterize the distribution of pathogens in ticks and mammalian and avian hosts. We found nearly one third of captured birds (n = 849) and 70% of small mammals (n = 694) were infested with I . scapularis . Five bird and three mammal species transmitted Borrelia burgdorferi to feeding larvae (n larvae tested = 2257) and we estimated that about one fifth of the B . burgdorferi -infected questing nymphs in the park acquired their infection from birds, the remaining being attributable to mice. Ground-foraging bird species were more parasitized than other birds, and species that inhabited open habitat were more frequently infested and were more likely to transmit B . burgdorferi to larval ticks feeding upon them. Female birds were more likely to transmit infection than males, without age differentiation, whereas in mice, adult males were more likely to transmit infection than juveniles and females. We also detected Borrelia miyamotoi in larvae collected from birds, and Anaplasma phagocytophilum from a larva collected from a white-footed mouse. This study highlights the importance of characterising the reservoir potential of alternative reservoir hosts and to quantify their contribution to transmission dynamics in different species assemblages. This information is key to identifying the most effective host-targeted risk mitigation actions.