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Results 1,892

Periodicals

Simultaneous Design of Microbe and Bioreactor

When developing a biotechnological process, the microorganism is first designed, e.g., using metabolic engineering. Then, the optimum fermentation parameters are determined on a laboratory scale, and lastly, they are transferred to the bioreactor scale. However, this step-by-step approach is costly and time-consuming and may result in suboptimal configurations. Herein, we present the bilevel optimization formulation SimulKnockReactor, which connects bioreactor design with microbial strain design, an extension of our previous formulation, SimulKnock (Ziegler et al., 2024, AIChE J.). At the upper (bioreactor) level, we minimize investment and operation costs for agitation, aeration, and pH control by determining the size and operating conditions of a continuous stirred-tank reactor - without selecting specific devices like the stirrer type. The lower (cellular) level is based on flux balance analysis and implements optimal reaction knockouts predicted by the upper level. Our results with a core and a genome-scale metabolic model of Escherichia coli show that the substrate is the largest cost factor. Our simultaneous approach outperforms a sequential approach using OptKnock. Namely, the knockouts proposed by OptKnock cannot guarantee the required production capacity in all cases considered. In the case that both approaches deliver feasible results, the total annual costs are the same or lower with SimulKnockReactor, highlighting the advantage of combining cellular and bioreactor levels. This work is a further step towards a fully integrated bioprocess design.

Biotechnology• 2025• arXiv
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Periodicals

Enhancing reinforcement learning for population setpoint tracking in co-cultures

Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division of labor. In this study, we employ reinforcement learning as a control method for population setpoint tracking in co-cultures, focusing on policy-gradient techniques where the control policy is parameterized by neural networks. However, achieving accurate tracking across multiple setpoints is a significant challenge in reinforcement learning, as the agent must effectively balance the contributions of various setpoints to maximize the expected system performance. Traditional return functions, such as those based on a quadratic cost, often yield suboptimal performance due to their inability to efficiently guide the agent toward the simultaneous satisfaction of all setpoints. To overcome this, we propose a novel return function that rewards the simultaneous satisfaction of multiple setpoints and diminishes overall reward gains otherwise, accounting for both stage and terminal system performance. This return function includes parameters to fine-tune the desired smoothness and steepness of the learning process. We demonstrate our approach considering an $\textit{Escherichia coli}$ co-culture in a chemostat with optogenetic control over amino acid synthesis pathways, leveraging auxotrophies to modulate growth.

Biotechnology• 2024• arXiv
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Periodicals

Deep Learning in Proteomics Informatics: Applications, Challenges, and Future Directions

Deep learning is an advanced technology that relies on large-scale data and complex models for feature extraction and pattern recognition. It has been widely applied across various fields, including computer vision, natural language processing, and speech recognition. In recent years, deep learning has demonstrated significant potential in the realm of proteomics informatics, particularly in deciphering complex biological information. The introduction of this technology not only accelerates the processing speed of protein data but also enhances the accuracy of predictions regarding protein structure and function. This provides robust support for both fundamental biology research and applied biotechnological studies. Currently, deep learning is primarily focused on applications such as protein sequence analysis, three-dimensional structure prediction, functional annotation, and the construction of protein interaction networks. These applications offer numerous advantages to proteomic research. Despite its growing prevalence in this field, deep learning faces several challenges including data scarcity, insufficient model interpretability, and computational complexity; these factors hinder its further advancement within proteomics. This paper comprehensively reviews the applications of deep learning in proteomics along with the challenges it encounters. The aim is to provide a systematic theoretical discussion and practical basis for research in this domain to facilitate ongoing development and innovation of deep learning technologies within proteomics.

Biotechnology• 2024• arXiv
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Periodicals

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p=0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.

Biotechnology• 2019• arXiv
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Periodicals

The Virtual Institute for Integrative Biology (VIIB)

The Virtual Institute for Integrative Biology (VIIB) is a Latin American initiative for achieving global collaborative e-Science in the areas of bioinformatics, genome biology, systems biology, metagenomics, medical applications and nanobiotechnolgy. The scientific agenda of VIIB includes: construction of databases for comparative genomics, the AlterORF database for alternate open reading frames discovery in genomes, bioinformatics services and protein simulations for biotechnological and medical applications. Human resource development has been promoted through co-sponsored students and shared teaching and seminars via video conferencing. E-Science challenges include: interoperability and connectivity concerns, high performance computing limitations, and the development of customized computational frameworks and flexible workflows to efficiently exploit shared resources without causing impediments to the user. Outreach programs include training workshops and classes for high school teachers and students and the new Adopt-a-Gene initiative. The VIIB has proved an effective way for small teams to transcend the critical mass problem, to overcome geographic limitations, to harness the power of large scale, collaborative science and improve the visibility of Latin American science It may provide a useful paradigm for developing further e-Science initiatives in Latin America and other emerging regions.

Biotechnology• 2010• arXiv
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Periodicals

Pressure Loss and Sound Generated In a Miniature Pig Airway Tree Model

Background: Pulmonary auscultation is a common tool for diagnosing various respiratory diseases. Previous studies have documented many details of pulmonary sounds in humans. However, information on sound generation and pressure loss inside animal airways is scarce. Since the morphology of animal airways can be significantly different from human, the characteristics of pulmonary sounds and pressure loss inside animal airways can be different. Objective: The objective of this study is to investigate the sound and static pressure loss measured at the trachea of a miniature pig airway tree model based on the geometric details extracted from physical measurements. Methods: In the current study, static pressure loss and sound generation measured in the trachea was documented at different flow rates of a miniature pig airway tree. Results: Results showed that the static pressure and the amplitude of the recorded sound at the trachea increased as the flow rate increased. The dominant frequency was found to be around 1840-1870 Hz for flow rates of 0.2-0.55 lit/s. Conclusion: The results suggested that the dominant frequency of the measured sounds remained similar for flow rates from 0.20 to 0.55 lit/s. Further investigation is needed to study sound generation under different inlet flow and pulsatile flow conditions.

Biotechnology• 2017• arXiv
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Periodicals

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks

Stirred tanks are vital in chemical and biotechnological processes, particularly as bioreactors. Although computational fluid dynamics (CFD) is widely used to model the flow in stirred tanks, its high computational cost$-$especially in multi-query scenarios for process design and optimization$-$drives the need for efficient data-driven surrogate models. However, acquiring sufficiently large datasets can be costly. Physics-informed neural networks (PINNs) offer a promising solution to reduce data requirements while maintaining accuracy by embedding underlying physics into neural network (NN) training. This study quantifies the data requirements of vanilla PINNs for developing surrogate models of a flow field in a 2D stirred tank. We compare these requirements with classical supervised neural networks and boundary-informed neural networks (BINNs). Our findings demonstrate that surrogate models can achieve prediction errors around 3% across Reynolds numbers from 50 to 5000 using as few as six datapoints. Moreover, employing an approximation of the velocity profile in place of real data labels leads to prediction errors of around 2.5%. These results indicate that even with limited or approximate datasets, PINNs can be effectively trained to deliver high accuracy comparable to high-fidelity data.

Biotechnology• 2025• arXiv
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Periodicals

EnviroPiNet: A Physics-Guided AI Model for Predicting Biofilter Performance

Environmental biotechnologies, such as drinking water biofilters, rely on complex interactions between microbial communities and their surrounding physical-chemical environments. Predicting the performance of these systems is challenging due to high-dimensional, sparse datasets that lack diversity and fail to fully capture system behaviour. Accurate predictive models require innovative, science-guided approaches. In this study, we present the first application of Buckingham Pi theory to modelling biofilter performance. This dimensionality reduction technique identifies meaningful, dimensionless variables that enhance predictive accuracy and improve model interpretability. Using these variables, we developed the Environmental Buckingham Pi Neural Network (EnviroPiNet), a physics-guided model benchmarked against traditional data-driven methods, including Principal Component Analysis (PCA) and autoencoder neural networks. Our findings demonstrate that the EnviroPiNet model achieves an R^2 value of 0.9236 on the testing dataset, significantly outperforming PCA and autoencoder methods. The Buckingham Pi variables also provide insights into the physical and chemical relationships governing biofilter behaviour, with implications for system design and optimization. This study highlights the potential of combining physical principles with AI approaches to model complex environmental systems characterized by sparse, high-dimensional datasets.

Biotechnology• 2025• arXiv
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Periodicals

Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires

The adaptive immune system is a natural diagnostic and therapeutic. It recognizes threats earlier than clinical symptoms manifest and neutralizes antigen with exquisite specificity. Recognition specificity and broad reactivity is enabled via adaptive B- and T-cell receptors: the immune receptor repertoire. The human immune system, however, is not omnipotent. Our natural defense system sometimes loses the battle to parasites and microbes and even turns against us in the case of cancer, autoimmune and inflammatory disease. A long-standing dream of immunoengineers has been, therefore, to mechanistically understand how the immune system sees, reacts and remembers antigens. Only very recently, experimental and computational methods have achieved sufficient quantitative resolution to start querying and engineering adaptive immunity with great precision. In specific, these innovations have been applied with the greatest fervency and success in immunotherapy, autoimmunity and vaccine design. The work here highlights advances, challenges and future directions of quantitative approaches which seek to advance the fundamental understanding of immunological phenomena, and reverse engineer the immune system to produce auspicious biopharmaceutical drugs and immunodiagnostics. Our review indicates that the merger of fundamental immunology, computational immunology and digital-biotechnology minimizes black box engineering, thereby advancing both immunological knowledge and as well immunoengineering methodologies.

Biotechnology• 2019• arXiv
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Periodicals

Nanoscale Substrate Roughness Hinders Domain Formation in Supported Lipid Bilayers

Supported Lipid Bilayers (SLBs) are model membranes formed at solid substrate surfaces. This architecture renders the membrane experimentally accessible to surface sensitive techniques used to study their properties, including Atomic Force Microscopy (AFM), optical fluorescence microscopy, Quartz Crystal Microbalance (QCM) and X-Ray/Neutron Reflectometry, and allows integration with technology for potential biotechnological applications such as drug screening devices. The experimental technique often dictates substrate choice or treatment, and it is anecdotally recognised that certain substrates are suitable for the particular experiment, but the exact influence of the substrate has not been comprehensively investigated. Here, we study the behavior of a simple model bilayer, phase separating on a variety of commonly used substrates, including glass, mica, silicon and quartz, with drastically different results. The distinct micron scale domains observed on mica, identical to those seen in free-floating Giant Unilamellar Vesicles (GUVs), are reduced to nanometer scale domains on glass and quartz. The mechanism for the arrest of domain formation is investigated, and the most likely candidate is nanoscale surface roughness, acting as a drag on the hydrodynamic motion of small domains during phase separation. Evidence was found that the physico-chemical properties of the surface have a mediating effect, most likely due to changes in the lubricating interstitial water layer between surface and bilayer.

Biotechnology• 2019• arXiv
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Periodicals

Study of the combined effect of temperature, pH and water activity on the radial growth rate of the white-rot basidiomycete Physisporinus vitreus by using a hyphal growth model

The present work investigates environmental effects on the growth of fungal colonies of P. vitreus by using a lattice-free discrete modelling approach called FGM (Fuhr et al. (2010), arXiv:1101.1747), in which hyphae and nutrients are considered as discrete structures. A discrete modelling approach allows studying the underlying mechanistic rule concerning the basic architecture and dynamic of fungal networks on the scale of a single colony. By comparing simulations of the FGM with laboratory experiments of growing fungal colonies on malt extract agar we show that combined effect of temperature, pH and water activity on the radial growth rate of a fungal colony on a macroscopic scale may be explained by a power law for the growth costs of hyphal expansion on a microscopic scale. The information about the response of the fungal mycelium on a microscopic scale to environmental conditions is essential to simulate its behavior in complex structure substrates such as wood, where the impact of the fungus to the wood (i.e. the degradation of bordered pits or the creation of bore holes and cavities) changes the local environmental condition, e.g. the permeability of the substrate and therefore the water activity level in a colonized wood cell lumen. A combination of diffusion and moisture processes into wood with the FGM may brighten the knowledge about the colonization strategy of P. vitreus and helps to optimize its growth behavior for biotechnological application such as bioincising.

Biotechnology• 2011• arXiv
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Periodicals

TriSig: Assessing the statistical significance of triclusters

Tensor data analysis allows researchers to uncover novel patterns and relationships that cannot be obtained from matrix data alone. The information inferred from the patterns provides valuable insights into disease progression, bioproduction processes, weather fluctuations, and group dynamics. However, spurious and redundant patterns hamper this process. This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations, extending well-established principles for assessing the statistical significance of patterns in matrix data. A comprehensive discussion on binomial testing for false positive discoveries is entailed at the light of: variable dependencies, temporal dependencies and misalignments, and \textit{p}-value corrections under the Benjamini-Hochberg procedure. Results gathered from the application of state-of-the-art triclustering algorithms over distinct real-world case studies in biochemical and biotechnological domains confer validity to the proposed statistical frame while revealing vulnerabilities of some triclustering searches. The proposed assessment can be incorporated into existing triclustering algorithms to mitigate false positive/spurious discoveries and further prune the search space, reducing their computational complexity. Availability: The code is freely available at https://github.com/JupitersMight/TriSig under the MIT license.

Biotechnology• 2023• arXiv
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Periodicals

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization

While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the combination of local search and bandit learning method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient and able to find top designs using reasonably small mutation counts.

Biotechnology• 2024• arXiv
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Periodicals

Systematic identification of abundant A-to-I editing sites in the human transcriptome

RNA editing by members of the double-stranded RNA-specific ADAR family leads to site-specific conversion of adenosine to inosine (A-to-I) in precursor messenger RNAs. Editing by ADARs is believed to occur in all metazoa, and is essential for mammalian development. Currently, only a limited number of human ADAR substrates are known, while indirect evidence suggests a substantial fraction of all pre-mRNAs being affected. Here we describe a computational search for ADAR editing sites in the human transcriptome, using millions of available expressed sequences. 12,723 A-to-I editing sites were mapped in 1,637 different genes, with an estimated accuracy of 95%, raising the number of known editing sites by two orders of magnitude. We experimentally validated our method by verifying the occurrence of editing in 26 novel substrates. A-to-I editing in humans primarily occurs in non-coding regions of the RNA, typically in Alu repeats. Analysis of the large set of editing sites indicates the role of editing in controlling dsRNA stability.

Biotechnology• 2004• arXiv
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Periodicals

Scaling pattern mining through non-overlapping variable partitioning

Biclustering algorithms play a central role in the biotechnological and biomedical domains. The knowledge extracted supports the extraction of putative regulatory modules, essential to understanding diseases, aiding therapy research, and advancing biological knowledge. However, given the NP-hard nature of the biclustering task, algorithms with optimality guarantees tend to scale poorly in the presence of high-dimensionality data. To this end, we propose a pipeline for clustering-based vertical partitioning that takes into consideration both parallelization and cross-partition pattern merging needs. Given a specific type of pattern coherence, these clusters are built based on the likelihood that variables form those patterns. Subsequently, the extracted patterns per cluster are then merged together into a final set of closed patterns. This approach is evaluated using five published datasets. Results show that in some of the tested data, execution times yield statistically significant improvements when variables are clustered together based on the likelihood to form specific types of patterns, as opposed to partitions based on dissimilarity or randomness. This work offers a departuring step on the efficiency impact of vertical partitioning criteria along the different stages of pattern mining and biclustering algorithms. Availability: All the code is freely available at https://github.com/JupitersMight/pattern_merge under the MIT license.

Biotechnology• 2022• arXiv
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Periodicals

Protein Inverse Folding From Structure Feedback

The inverse folding problem, aiming to design amino acid sequences that fold into desired three-dimensional structures, is pivotal for various biotechnological applications. Here, we introduce a novel approach leveraging Direct Preference Optimization (DPO) to fine-tune an inverse folding model using feedback from a protein folding model. Given a target protein structure, we begin by sampling candidate sequences from the inverse-folding model, then predict the three-dimensional structure of each sequence with the folding model to generate pairwise structural-preference labels. These labels are used to fine-tune the inverse-folding model under the DPO objective. Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning not only improves sequence recovery of baseline models but also leads to a significant improvement in average TM-Score from 0.77 to 0.81, indicating enhanced structure similarity. Furthermore, iterative application of our DPO-based method on challenging protein structures yields substantial gains, with an average TM-Score increase of 79.5\% with regard to the baseline model. This work establishes a promising direction for enhancing protein sequence design ability from structure feedback by effectively utilizing preference optimization.

Biotechnology• 2025• arXiv
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Periodicals

Learning to engineer protein flexibility

Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus on the design of proteins with a fixed backbone structure, which leads to their limited ability to account for protein flexibility, one of the crucial properties for protein function. Learning to engineer protein flexibility is problematic because the available data are scarce, heterogeneous, and costly to obtain using computational as well as experimental methods. Our contributions to address this problem are three-fold. First, we comprehensively compare methods for quantifying protein flexibility and identify data relevant to learning. Second, we design and train flexibility predictors utilizing sequential or both sequential and structural information on the input. We overcome the data scarcity issue by leveraging a pre-trained protein language model. Third, we introduce a method for fine-tuning a protein inverse folding model to steer it toward desired flexibility in specified regions. We demonstrate that our method Flexpert-Design enables guidance of inverse folding models toward increased flexibility. This opens up new possibilities for protein flexibility engineering and the development of proteins with enhanced biological activities.

Biotechnology• 2024• arXiv
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Periodicals

Binding Group of Oligonucleotides on TiO 2 Surfaces: Phosphate Anions or Nucleobases?

Although the immobilization of oligonucleotides (nucleic acid) on mineral surfaces is at the basis of different biotechnological applications, an atomistic understanding of the interaction of the nucleic acid components with the titanium dioxide surfaces has not yet been achieved. Here, the adsorption of the phosphate anion, of the four DNA bases (adenine, guanine, thymine, and cytosine) and of some entire nucleotides and dinucleotides on the TiO 2 anatase (101) surface is studied through dispersion-corrected hybrid density functional theory (DFT) calculations. Several adsorption configurations are identified for the separated entities (phosphate anion or base) and then considered when studying the adsorption of the entire nucleotides. The analysis shows that both the phosphate anion and each base may anchor the nucleotides to the surface in a collaborative and synergistic adsorption mode. The tendency is that the nucleotides containing the guanine base present the strongest adsorption while those made up with the thymine base have the lowest adsorption energies. Nucleotides based on adenine and cytosine have a similar intermediate behavior. Finally, we investigated the adsorption of competing water molecules to understand whether in the presence of the aqueous solvent, the nucleotides would remain bonded to the surface or desorb.

Biotechnology• 2021• arXiv
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Periodicals

MOSAIC: Codon Harmonization of Monte Carlo-Based Simulated Annealing for Linked Codons in Heterologous Protein Expression

Codon usage bias has a crucial impact on the translation efficiency and co-translational folding of proteins, necessitating the algorithmic development of codon optimization/harmonization methods, particularly for heterologous recombinant protein expression. Codon harmonization is especially valuable for proteins sensitive to translation rates, because it can potentially replicate native translation speeds, preserving proper folding and maintaining protein activity. This work proposes a Monte Carlo-based codon harmonization algorithm, MOSAIC (Monte Carlo-based Simulated Annealing for Linked Codons), for the harmonization of a set of linked codons, which differs from conventional codon harmonization, by focusing on the codon sets rather than individual ones. Our MOSAIC demonstrates robust computational performance on ribosomal proteins (S18, S15, S10, and L11) as model systems. Among them, the harmonized gene of RP S18 was expressed and compared with the expression of the wild-type gene. The harmonized gene clearly yielded a larger quantity of the protein, from which the amount of the soluble protein was also significant. These results underscored the potential of the linked codon harmonization approach to enhance the expression and functionality of sensitive proteins, setting the stage for more efficient production of recombinant proteins in various biotechnological and pharmaceutical applications.

Biotechnology• 2025• arXiv
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Periodicals

Kinetic Arrest in Polyion-Induced Inhomogeneously-Charged Colloidal Particle Aggregation

Polymer chains adsorbed onto oppositely charged spherical colloidal particles can significantly modify the particle-particle interactions. For sufficient amounts of added polymers, the original electrostatic repulsion can even turn into an effective attraction and relatively large kinetically stable aggregates can form which display several unexpected and interesting peculiarities and some intriguing biotechnological implications. The attractive interaction contribution between two oppositely particles arises from the correlated adsorption of polyions at the oppositely charged particle surfaces, resulting in a non-homogeneous surface charge distribution. Here, we investigate the aggregation kinetics of polyion-induced colloidal complexes through Monte Carlo simulation, in which the effect of charge anisotropy is taken into account by a DLVO-like intra-particle potential, as recentely proposed by Velegol and Thwar [D. Velegol and P.K. Thwar, Langmuir, 17, 2001]. The results reveal that in the presence of a charge heterogeneity the aggregation process slows down due to the progressive increase of the potential barrier height upon clustering. Within this framework, the experimentally observed cluster phases in polyelectrolyte-liposomes solutions should be considered as a kinetic arrested state.

Biotechnology• 2008• arXiv
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Periodicals

Exploring and mapping the universe of evolutionary graphs

Population structure can be modelled by evolutionary graphs, which can have a substantial, but very subtle influence on the fate of the arising mutants. Individuals are located on the nodes of these graphs, competing with each other to eventually take over the graph via the links. Many applications for this framework can be envisioned, from the ecology of river systems and cancer initiation in colonic crypts to biotechnological search for optimal mutations. In all these applications, it is not only important where and when novel variants arise and how likely it is that they ultimately take over, but also how long this process takes. More concretely, how is the probability to take over the population related to the associated time? We study this problem for all possible undirected and unweighted graphs up to a certain size. To move beyond the graph size where an exhaustive search is possible, we devise a genetic algorithm to find graphs with either high or low fixation probability and either short or long fixation time and study their structure in detail searching for common themes. Our work unravels structural properties that maximize or minimize fixation probability and time, which allows us to contribute to a first map of the universe of evolutionary graphs.

Biotechnology• 2018• arXiv
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Periodicals

Emulsion-templated formation of poly(N-isopropylacrylamide):surfactant mixed shells by thermo-enhanced interfacial complexation

The encapsulation of fragile biomacromolecules is crucial for many biotechnological applications but remains challenging. Interfacial complexation (IC) in water-in-oil emulsions turned out to be an efficient process for the formation of protective polymer layers at the surface of capsule-precursor water droplets. We propose to further enhance this IC process by introducing thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) strands in the interfacial polymer layer. In this work, we implement surfactant-polymer IC in water-in-fluorocarbon oil emulsions between a water-soluble poly(L-lysine)-g-poly(N-isopropylacrylamide) cationic copolymer (PLL-g-PNIPAM) and an oil-soluble anionic surfactant. We demonstrate that the thermal collapse transition of PNIPAM strands triggers an enrichment of the polymer layer initially formed by IC. This process is leveraged to irreversibly segregate water-soluble nanoparticles in the interfacial polymer layer, resulting in gel-like mixed shells. We demonstrate that this thermo-enhancement of conventional IC is a promising approach for the formation, strengthening and functionalization of capsule shells. As implemented in mild conditions, thermo-enhanced IC is additionally compatible with the encapsulation of proteins, paving the way for new delivery systems of biomacromolecules.

Biotechnology• 2021• arXiv
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Validation of Subject-Specific Knee Models from In Vivo Measurements

Calibration to experimental data is vital when developing subject-specific models towards developing digital twins. Yet, to date, subject-specific models are largely based on cadaveric testing, as in vivo data to calibrate against has been difficult to obtain until recently. To support our overall goal of building subject-specific models of the living knee, we aimed to show that subject-specific computational models built and calibrated using in vivo measurements would have accuracy comparable to models built using in vitro measurements. Two knee specimens were imaged using a combination of computed tomography (CT), and surface scans. Knee laxity measurements were made with a custom apparatus used for the living knee and from a robotic knee simulator. Models of the knees were built using the CT geometry and surface scans, and then calibrated with either laxity data from the robotic knee simulator or from the knee laxity apparatus. Model performance was compared by simulation of passive flexion, knee laxity and a clinically relevant pivot shift. Performance was similar with differences during simulated anterior-posterior laxity tests of less than 2.5 mm. Additionally, model predictions of a pivot shift were similar with differences less than 3 deg or 3 mm for rotations and translations, respectively. Still, differences in the predicted ligament loads and calibrated material properties emerged, highlighting a need for methods to include ligament load as part of the underlying calibration process. Overall, the results showed that currently available methods of measuring knee laxity in vivo are sufficient to calibrate models comparable with existing in vitro techniques, and the workflows described here may provide a basis for modeling the living knee. The models, data, and code are publicly available.

Biotechnology• 2024• arXiv
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Advancing Drug Resistance Research Through Quantitative Modeling and Synthetic Biology

Antimicrobial resistance is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of antimicrobial resistance to discover effective therapies against drug-resistant infections.

Biotechnology• 2020• arXiv
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