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Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

Microtubules (MTs) are cytoplasmic biopolymers that are common in eukaryotic cells. The MT is assembled by αβ tubulin dimer subunits that can be in either a GTPor GDP-bound nucleotide state. These dimer subunit connect with longitudinal bonds to form linear strands called protofilements (PFs). Lateral bonds connect 13 PFs together to form the tube-like structure of a MT. GTP-bound subunits collect near the MT tip region to form a GTP-cap, which helps maintain the bonds that hold the MT structure intact. Losing the GTP-cap exposes GDP-bound subunits which are more likely to break their bonds, and promote subunits to detach from the MT structure. The MT length changes in time by undergoing spontaneous switches between periods of sustained growth and rapid shortening, which characterize the behavior called dynamic instability (DI). The molecular reactions that drive MT dynamics primarily affect the tip portion of the structure. Therefore, a study of the connection between MT tip structures and macro-level phases is needed to gain a better understanding of the mechanisms that drive phase changes in DI. Laboratory conditions limit the level of detail that can be experimentally collected from MT structures. Computational models are a vital tool that provide this level of information, and they have helped understand how molecular level reactions alter the micro-level MT structure, which drives the MT length changes observed at the macro-level. The detailed 13-PF MT model was capable of running long-time simulations that display DI behavior with a low computational cost, but it made use of an approximation that skips over MT structural states. This study first develops the extended 13-PF MT model in order to simulate a biochemically exact trajectory of all the MT structural states resulting from possible reactions events. Then, the minimal MT structure that includes the lateral bond is considered to present the simplified 2-PF MT model, a novel consideration which helps make calculations of the MT tip structure features more feasible while successfully simulating DI behavior. The high frequency and low amplitude fluctuations present in simulated MT length history data make it difficult to pinpoint where DI phases begin and end, and where phase transitions occur. To this end, an unsupervised machine learning method based on K-means clustering is presented to identify, classify, and analyze macro-level phases present in MT length history data. Application of this method revealed an intermediate phase called “stutters”, during which the rate of MT length change is smaller in magnitude compared to classically recognized growth and shortening phases. Additionally, stutter phases commonly appeared as a transitional phase during catastrophe events, between growth and shortening phases. This indicated that before a catastrophe event takes place, a MT is likely to first undergo structural changes that do not alter the MT length, which result in structural configurations prone to entering a period of rapid depolymerization. The proposed DI phase classification method now can identify these periods, which in past experimental studies have been observed, but not separately considered as a unique class of behavior [21]. Furthermore, the stutter events specifically provide a target region to study the mechanisms involved with catastrophe events. Finally, a supervised machine learning approach called Random Forest was used to test the ability for micro-level tip structure features to predict their corresponding macro-level DI phases, and to forecast upcoming phase transitions. The results indicated that the GTP-cap size and it's relative position to the cracked tip region are important factors in predicting which DI phase a MT is in. In addition to the GTP-cap size, information on the PF-tip lengths and the dispersion of GTP-bound subunits in the tip region were found to be important in forecasting upcoming phase transitions. Thus, specific MT tip structures and the reaction events that create them are identified as the mechanisms that drive respective transitions between DI phases.

ABSTRACT Microtubules (MTs) are cytoskeletal fibers that undergo dynamic instability (DI), a remarkable process involving phases of growth and shortening separated by stochastic transitions called catastrophe and rescue. Dissecting dynamic instability mechanism(s) requires first characterizing and quantifying these dynamics, a subjective process that often ignores complexity in MT behavior. We present a Statistical Tool for Automated Dynamic Instability Analysis (STADIA), which identifies and quantifies not only growth and shortening, but also a category of intermediate behaviors that we term ‘stutters.’ During stutters, the rate of MT length change tends to be smaller in magnitude than during typical growth or shortening phases. Quantifying stutters and other behaviors with STADIA demonstrates that stutters precede most catastrophes in our dimer-scale MT simulations and in vitro experiments, suggesting that stutters are mechanistically involved in catastrophes. Related to this idea, we show that the anti-catastrophe factor CLASP2γ works by promoting the return of stuttering MTs to growth. STADIA enables more comprehensive and data-driven analysis of MT dynamics compared to previous methods. The treatment of stutters as distinct and quantifiable DI behaviors provides new opportunities for analyzing mechanisms of MT dynamics and their regulation by binding proteins. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵* Co-first authors * This version of the manuscript has been thoroughly revised. Particularly significant is the new 'parameter sweep' analysis (provided in Supplement Sections 2,3 and 4) that demonstrates the robustness of the method and its conclusions.

The concept of critical concentration (CC) is central to understanding behaviors of microtubules and other cytoskeletal polymers. Traditionally, these polymers are understood to have one CC, measured multiple ways and assumed to be the subunit concentration necessary for polymer assembly. However, this framework does not incorporate dynamic instability (DI), and there is work indicating that microtubules have two CCs. We use our previously established simulations to confirm that microtubules have (at least) two experimentally relevant CCs and to clarify the behaviors of individuals and populations relative to the CCs. At free subunit concentrations above the lower CC (CC_IndGrow), growth phases of individual filaments can occur transiently; above the higher CC (CC_PopGrow), the population's polymer mass will increase persistently. Our results demonstrate that most experimental CC measurements correspond to CC_PopGrow, meaning \"typical\" DI occurs below the concentration traditionally considered necessary for polymer assembly. We report that [free tubulin] at steady state does not equal CC_PopGrow, but instead approaches CC_PopGrow asymptotically as [total tubulin] increases and depends on the number of stable microtubule seeds. We show that the degree of separation between CC_IndGrow and CC_PopGrow depends on the rate of nucleotide hydrolysis. This clarified framework helps explain and unify many experimental observations. Footnotes * The manuscript was extensively revised and expanded. The most significant addition was inclusion of work on the effect of nucleotide hydrolysis on the separation between the two major CCs.

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.