A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics.

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Cohort-based smoothing methods for age-specific contact rates.

The use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent's and contact's age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach-taking into account the reciprocal nature of contacts-introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing.

Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage.

Deoxyribonucleic acid (DNA) is an attractive medium for long-term digital data storage due to its extremely high storage density, low maintenance cost and longevity. However, during the process of synthesis, amplification and sequencing of DNA sequences with homopolymers of large run-length, three different types of errors, namely, insertion, deletion and substitution errors frequently occur. Meanwhile, DNA sequences with large imbalances between GC and AT content exhibit high dropout rates and are prone to errors. These limitations severely hinder the widespread use of DNA-based data storage. In order to reduce and correct these errors in DNA storage, this paper proposes a novel coding schema called DNA-LC, which converts binary sequences into DNA base sequences that satisfy both the GC balance and run-length constraints. Furthermore, our coding mode is able to detect and correct multiple errors with a higher error correction capability than the other methods targeting single error correction within a single strand. The decoding algorithm has been implemented in practice. Simulation results indicate that our proposed coding scheme can offer outstanding error protection to DNA sequences. The source code is freely accessible at https://github.com/XiayangLi2301/DNA.

Physiological ramifications of constrained collective cell migration.

Constraining collective cell migration in vitro using different types of engineered substrates such as microstructured surfaces or adhesive patterns of different shapes and sizes often leads to the emergence of specific patterns of motion. Recently, analogies between the behavior of cellular assemblies and that of active fluids have enabled significant advances in our understanding of collective cell migration; however, the physiological relevance and potential functional consequences of the resulting migration patterns remain elusive. Here we describe the different patterns of collective cell migration that have been reported in vitro in response to geometrical constraints, explore the in vivo pertinence of the in vitro systems used to impose the geometrical constraints, and discuss the potential physiological ramifications of the collective migration patterns that emerge as a result of physical constraints. We conclude by highlighting key upcoming challenges in the exciting field of constrained collective cell migration.

Anomalous magnetic noise in an imperfectly flat landscape in the topological magnet Dy2Ti2O7.

Noise generated by motion of charge and spin provides a unique window into materials at the atomic scale. From temperature of resistors to electrons breaking into fractional quasiparticles, "listening" to the noise spectrum is a powerful way to decode underlying dynamics. Here, we use ultrasensitive superconducting quantum interference device (SQUIDs) to probe the puzzling noise in a frustrated magnet, the spin-ice compound Dy2Ti2O7 (DTO), revealing cooperative and memory effects. DTO is a topological magnet in three dimensions-characterized by emergent magnetostatics and telltale fractionalized magnetic monopole quasiparticles-whose real-time dynamical properties have been an enigma from the very beginning. We show that DTO exhibits highly anomalous noise spectra, differing significantly from the expected Brownian noise of monopole random walks, in three qualitatively different regimes: equilibrium spin ice, a "frozen" regime extending to ultralow temperatures, and a high-temperature "anomalous" paramagnet. We present several distinct mechanisms that give rise to varied colored noise spectra. In addition, we identify the structure of the local spin-flip dynamics as a crucial ingredient for any modeling. Thus, the dynamics of spin ice reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such, it points to intriguing cooperative and memory effects for a broad class of magnetic materials.

Deep phosphotyrosine characterisation of primary murine T cells using broad spectrum optimisation of selective triggering.

Sequencing the tyrosine phosphoproteome using MS-based proteomics is challenging due to the low abundance of tyrosine phosphorylation in cells, a challenge compounded in scarce samples like primary cells or clinical samples. The broad-spectrum optimisation of selective triggering (BOOST) method was recently developed to increase phosphotyrosine sequencing in low protein input samples by leveraging tandem mass tags (TMT), phosphotyrosine enrichment, and a phosphotyrosine-loaded carrier channel. Here, we demonstrate the viability of BOOST in T cell receptor (TCR)-stimulated primary murine T cells by benchmarking the accuracy and precision of the BOOST method and discerning significant alterations in the phosphoproteome associated with receptor stimulation. Using 1 mg of protein input (about 20 million cells) and BOOST, we identify and precisely quantify more than 2000 unique pY sites compared to about 300 unique pY sites in non-BOOST control samples. We show that although replicate variation increases when using the BOOST method, BOOST does not jeopardise quantitative precision or the ability to determine statistical significance for peptides measured in triplicate. Many pY previously uncharacterised sites on important T cell signalling proteins are quantified using BOOST, and we identify new TCR responsive pY sites observable only with BOOST. Finally, we determine that the phase-spectrum deconvolution method on Orbitrap instruments can impair pY quantitation in BOOST experiments.

Shape-constrained deformable brain segmentation: Methods and quantitative validation.

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

Comparative Biophysical Study of Clinical Surfactants using Constrained Drop Surfactometry.

Surfactant replacement therapy is crucial in managing neonatal respiratory distress syndrome (RDS). Currently licensed clinical surfactants in the United States and Europe, including Survanta, Infasurf, Curosurf, and Alveofact, are all derived from bovine or porcine sources. We conducted a comprehensive examination of the biophysical properties of these four clinical surfactant preparations under physiologically relevant conditions, utilizing constrained drop surfactometry (CDS). The assessed biophysical properties included the adsorption rate, quasi-static and dynamic surface activity, resistance to surfactant inhibition by meconium, and the morphology of the adsorbed surfactant films. This comparative study unveiled distinct in vitro biophysical properties of these clinical surfactants and revealed correlations between their chemical composition, lateral film structure, and biophysical functionality. Notably, at 1 mg/mL, Survanta exhibited a significantly lower adsorption rate compared to the other preparations at the same surfactant concentration. At 10 mg/mL, Infasurf, Curosurf, and Survanta all demonstrated excellent dynamic surface activity, while Alveofact exhibited the poorest quasi-static and dynamic surface activity. The suboptimal surface activity of Alveofact is found to be correlated with its unique monolayer-predominant morphology, in contrast to other surfactants forming multilayers. Curosurf, in particular, showcased superior resistance to biophysical inhibition by meconium compared to other preparations. Understanding the diverse biophysical behaviors of clinical surfactants provides crucial insights for precision and personalized design in treating RDS and other respiratory conditions. The findings from this study contribute valuable perspectives for development of more efficacious and fully synthetic surfactant preparations.

Dynamic primitives in constrained action: systematic changes in the zero-force trajectory.

Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge that gap, this study examined kinematically constrained motion as an intermediate step between the widely studied unconstrained motions and sparsely studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds (fast, medium, and very slow) as instructed via visual display. With the hand constrained to move in a circle, nonzero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to "subtract" peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion, a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction; that is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 s, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifesting as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.NEW & NOTEWORTHY Control using primitive dynamic actions may explain why human performance is superior to robots despite seemingly inferior "wetware"; however, this also implies limitations. For a crank-turning task, this work quantified two such informative limitations. Force was exerted even though it produced no mechanical work, the underlying zero-force trajectory was roughly elliptical, and its orientation differed with turning direction, evidence of oscillatory control. At slow speeds, speed variability increased substantially, indicating intermittent control via submovements.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context.

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation.

OBJECTIVES: Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth. METHODS: Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes. RESULTS: Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05). CONCLUSIONS: CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

OBJECTIVE: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo. METHODS: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO-CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. RESULTS: The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. CONCLUSIONS: These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology.

Inhibition of Aurora-A/N-Myc Protein-Protein Interaction Using Peptidomimetics: Understanding the Role of Peptide Cyclization.

Using N-Myc61-89 as a starting template we showcase the systematic use of truncation and maleimide constraining to develop peptidomimetic inhibitors of the N-Myc/Aurora-A protein-protein interaction (PPI); a potential anticancer drug discovery target. The most promising of these - N-Myc73-94-N85C/G89C-mal - is shown to favour a more Aurora-A compliant binding ensemble in comparison to the linear wild-type sequence as observed through fluorescence anisotropy competition assays, circular dichroism (CD) and nuclear magnetic resonance (NMR) experiments. Further in silico investigation of this peptide in its Aurora-A bound state, by molecular dynamics (MD) simulations, imply (i) the bound conformation is more stable as a consequence of the constraint, which likely suppresses dissociation and (ii) the constraint may make further stabilizing interactions with the Aurora-A surface. Taken together this work unveils the first orthosteric N-Myc/Aurora-A inhibitor and provides useful insights on the biophysical properties and thus design of constrained peptides, an attractive therapeutic modality.

A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2.

The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.

Diffusion tensor brain imaging at 0.55T: A feasibility study.

PURPOSE: To investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T. METHODS: Diffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal-to-noise ratio (SNR) of the 0.55T data was improved using a previous SNR-enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k-space using shared-edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test-retest assessment of repeatability at each field strength. RESULTS: After applying SNR-enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated ( R 2 ≥ 0 . 70 $$ {R}^2\ge 0.70 $$ ) with those obtained from 3T data. Test-retest analysis showed that SNR-enhancing reconstruction improved the repeatability of the 0.55T diffusion tensor parameters. CONCLUSION: High-resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise-mitigation strategies are applied.

Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.

PURPOSE: To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data. METHODS: The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem. RESULTS: The proposed method has been evaluated using simulations as well as in vivo 1 $$ {}^1 $$ H and 31 $$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6 × $$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100 × $$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method. CONCLUSION: A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.

Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by Saccharomyces cerevisiae.

Enzyme-constrained genome-scale models (ecGEMs) have potential to predict phenotypes in a variety of conditions, such as growth rates or carbon sources. This study investigated if ecGEMs can guide metabolic engineering efforts to swap anaerobic redox-neutral ATP-providing pathways in yeast from alcoholic fermentation to equimolar co-production of 2,3-butanediol and glycerol. With proven pathways and low product toxicity, the ecGEM solution space aligned well with observed phenotypes. Since this catabolic pathway provides only one-third of the ATP of alcoholic fermentation (2/3 versus 2 ATP per glucose), the ecGEM predicted a growth decrease from 0.36 h-1 in the reference to 0.175 h-1 in the engineered strain. However, this <3-fold decrease would require the specific glucose consumption rate to increase. Surprisingly, after the pathway swap the engineered strain immediately grew at 0.15 h-1 with a glucose consumption rate of 29 mmol (g CDW)-1 h-1, which was indeed higher than reference (23 mmol (g CDW)-1 h-1) and one of the highest reported for S. cerevisiae. The accompanying 2,3-butanediol- (15.8 mmol (g CDW)-1 h-1) and glycerol (19.6 mmol (g CDW)-1 h-1) production rates were close to predicted values. Proteomics confirmed that this increased consumption rate was facilitated by enzyme reallocation from especially ribosomes (from 25.5 to 18.5 %) towards glycolysis (from 28.7 to 43.5 %). Subsequently, 200 generations of sequential transfer did not improve growth of the engineered strain, showing the use of ecGEMs in predicting opportunity space for laboratory evolution. The observations in this study illustrate both the current potential, as well as future improvements, of ecGEMs as a tool for both metabolic engineering and laboratory evolution.

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM.

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Ligand Centered Reactivity of a Transition Metal Bound Geometrically Constrained Phosphine.

The electronic properties, coordination chemistry and reactivity of metal complexes of a planar (C2v symmetric) acridane-derived geometrically constrained phosphine, P(NNN), are described. On complexation to metal centers, the phosphine was found to adopt a distorted trigonal pyramidal structure with a high barrier to pyramidal inversion (22.3 kcal/mol at 298 K for Au[P(NNN)]Cl). Spectroscopic data and theoretical calculations carried out at the density functional level of theory indicate that P(NNN) is a moderate σ-donor, with significant π-acceptor properties. Despite the distortion undergone by the phosphorus atom on coordination to metal centers, the P(NNN) ligand retains its ability to react with small molecule substrates with polar E-H bonds (MeOH, NH2Ph, NH3). It does so in a concerted fashion across one of the P-N bonds, and reversibly in the case of amine substrates. This cooperative bond activation chemistry may ultimately prove beneficial in catalysis.