MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Negative Pressure Wound Therapy for Gas Gangrene of the Fingertip with Prolonged Infection.

Background: We encountered a case of infected soft tissue defect of the fingertip treated using negative pressure wound therapy (NPWT). The development of NPWT was started in the early 1990s, and it is a relatively new treatment method included in insurance coverage in Japan in 2010. NPWT is used for intractable wounds; some reports have examined its use on infected wounds. However, to the best of our knowledge, no study has examined its use on infected fingertip wounds. Methods: A patient with an infected soft tissue defect in the fingertip whose epithelialization period was prolonged despite continued antibiotic therapy was treated using NPWT in combination. Results: After NPWT was started, signs of infection and wound granulation were good. Additionally, completion of epithelialization was confirmed 7 weeks after NPWT started. Conclusions: Conventionally, skin flap or graft by hand surgeons have been performed on fingertip soft tissue defects with infection. NPWT does not require specialized and advanced surgical techniques; treatment for infected soft tissue defects can be administered by anyone if they have the required skills. In conclusion, NPWT may be considered a suitable alternative when treatment options such as flaps and skin grafts are not feasible.

Variations in the Extensor Pollicis Brevis-Extensor Pollicis Longus Tendon Complex.

Despite several reports on the running of the extensor pollicis brevis (EPB) tendons, the classification of tendon insertions remains ununified due to differences in reports. This diversity in tendon patterning is attributed to the process of tendon development. In this study, we assessed the running of the EPB tendons of 44 cadaver hands fixed in ethanol/formalin in detail and examined the existing classification method. The specimens were obtained from 15 women and seven men, with an average age of 86 years. Consistent with previous reports, we observed a wide diversity in the running of the EPB tendons. Further, we found that EPB tendon insertions showed diverse variations in the proportion and running of fibers, making it difficult to classify them into independent patterns. It is speculated that the EPB tendon develops through a different process than that of the muscle body of the EPB and that the entire muscle-tendon module of the EPB is evolving. The diversity of the EPB tendons observed in this study may reflect the ongoing process of evolution. In clinical practice, a wide variation in the running of the EPB tendons should be considered.

Comparison of Bone Evaluation and Metal Artifact between Photon-Counting CT and Five Energy-Integrating-Detector CT under Standardized Conditions Using Cadaveric Forearms.

BACKGROUND: To compare the potential of various bone evaluations by considering photon-counting CT (PCCT) and multiple energy-integrating-detector CT (EIDCT), including three dual-energy CT (DECT) scanners with standardized various parameters in both standard resolution (STD) and ultra-high-resolution (UHR) modes. METHODS: Four cadaveric forearms were scanned using PCCT and five EIDCTs, by applying STD and UHR modes. Visibility of bone architecture, image quality, and a non-displaced fracture were subjectively scored against a reference EIDCT image by using a five-point scale. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also compared. To assess metal artifacts, a forearm with radial plate fixation was scanned by with and without Tin filter (Sn+ and Sn-), and virtual monoenergetic image (VMI) at 120 keV was created. Regarding Sn+ and VMI, images were only obtained from the technically available scanners. Subjective scores and the areas of streak artifacts were compared. RESULTS: PCCT demonstrated significantly lower noise (p < 0.001) and higher bone SNR and CNR (p < 0.001) than all EIDCTs in both resolution modes. However, there was no significant difference between PCCT and EIDCTs in almost all subjective scores, regardless of scan modes, except for image quality where a significant difference was observed, compared to several EIDCTs. Metal artifact analysis revealed PCCT had larger artifact in Sn- and Sn+ (p < 0.001), but fewer in VMIs than three DECTs (p < 0.001 or 0.001). CONCLUSIONS: Under standardized conditions, while PCCT had almost no subjective superiority in visualizing bone structures and fracture line when compared to EIDCTs, it outperformed in quantitative analysis related to image quality, especially in lower noise and higher tissue contrast. When using PCCT to assess cases with metal implants, it may be recommended to use VMIs to minimize the possible tendency for artifact to be pronounced.

Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach.

Per- and polyfluoroalkyl substances (PFAS) are widely employed anthropogenic fluorinated chemicals known to disrupt hepatic lipid metabolism by binding to human peroxisome proliferator-activated receptor alpha (PPARα). Therefore, screening for PFAS that bind to PPARα is of critical importance. Machine learning approaches are promising techniques for rapid screening of PFAS. However, traditional machine learning approaches lack interpretability, posing challenges in investigating the relationship between molecular descriptors and PPARα binding. In this study, we aimed to develop a novel, explainable machine learning approach to rapidly screen for PFAS that bind to PPARα. We calculated the PPARα-PFAS binding score and 206 molecular descriptors for PFAS. Through systematic and objective selection of important molecular descriptors, we developed a machine learning model with good predictive performance using only three descriptors. The molecular size (b_single) and electrostatic properties (BCUT_PEOE_3 and PEOE_VSA_PPOS) are important for PPARα-PFAS binding. Alternative PFAS are considered safer than their legacy predecessors. However, we found that alternative PFAS with many carbon atoms and ether groups exhibited a higher affinity for PPARα. Therefore, confirming the toxicity of these alternative PFAS compounds with such characteristics through biological experiments is important.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach.

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.

Neuromuscular activation of the quadriceps femoris, including the vastus intermedius, during isokinetic knee extensions.

The purpose of this study was to compare the neuromuscular activation patterns of the individual muscles of the quadriceps femoris (QF), including the vastus intermedius (VI), during isokinetic concentric (CON) and eccentric (ECC) contractions. Thirteen healthy men performed maximum isokinetic CON and ECC knee extensions at angular velocities of 30, 90, and 120°/sec at knee joint angles from 80 to 180° (180° = full extension). The surface electromyographic (EMG) activities of the four individual muscles of the QF were recorded. The root mean squares of the EMG signals were normalized by the root mean square (nRMS) during CON contraction at 30°/sec. To investigate the nRMS changes, we classified the range of motion into four subcategories for each CON and ECC contraction. The nRMS of the VI was significantly higher in the flexed position during CON and ECC contractions at all velocities, and gradually decreased toward the extended positions regardless of the type of muscle contraction or angular velocity. These results suggest that the QF undergoes neuromuscular activation in a joint angle-dependent manner. In particular, the VI may contribute greatly during flexed contractions, independent of the type of contraction and angular velocity.

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT.

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Characteristics of O/W emulsion gels stabilized by soy protein-xanthan gum complex for plant-based processed meat products.

The aim of the present study is to characterize the mechanical properties of O/W emulsion gels stabilized through soy protein isolate (SPI)-xanthan gum (XG) complex and to elucidate their practical usefulness in plant-based processed meat products. O/W emulsions prepared by mixing aqueous solutions of SPI-XG complexes, which was formed via electrostatic interactions under acidic conditions (pH 4.0), with plant oil were gelled in the presence of several hydrocolloids. Effects of hydrocolloid composition, oil type and load, and oil droplet size on the mechanical properties of the emulsion gels were investigated by dynamic viscoelasticity measurements, and fluorescence microscopy was performed for observation of the oil droplet dispersion. Results indicated that methylcellulose should be required to provide the gels with heat resistance and that the type of oil used should affect dynamic storage modulus (G') of the gels particularly at lower temperatures. It was also found that increased oil load should decrease the gel's resistance to deformation, making the gel structure brittle, and that oil drop size should affect G' and dynamic loss modulus (G") at lower strains. Food application tests indicated that the emulsion gels used had a great impact on the mechanical properties of plant-based meat patties. These findings would contribute to the utilization of the emulsion gels as a lipid portion in plant-based processed meat products, leading to the progress of industrial practice.

Age-related change in mammographic breast density of women without history of breast cancer over a 10-year retrospective study.

Background: Women with higher breast density are at higher risk of developing breast cancer. Breast density is known to affect sensitivity to mammography and to decrease with age. However, the age change and associated factors involved are still unknown. This study aimed to investigate changes in breast density and the associated factors over a 10-year period. Materials and Methods: The study included 221 women who had undergone eight or more mammograms for 10 years (2011-2020), were between 25 and 65 years of age, and had no abnormalities as of 2011. Breast density on mammographic images was classified into four categories: fatty, scattered, heterogeneously dense, and extremely dense. Breast density was determined using an image classification program with a Microsoft Lobe's machine-learning model. The temporal changes in breast density over a 10-year period were classified into three categories: no change, decrease, and increase. An ordinal logistic analysis was performed with the three groups of temporal changes in breast density categories as the objective variable and the four items of breast density at the start, BMI, age, and changes in BMI as explanatory variables. Results: As of 2011, the mean age of the 221 patients was 47 ± 7.3 years, and breast density category 3 scattered was the most common (67.0%). The 10-year change in breast density was 64.7% unchanged, 25.3% decreased, and 10% increased. BMI was increased by 64.7% of women. Breast density decreased in 76.6% of the category at the start: extremely dense breast density at the start was correlated with body mass index (BMI). The results of the ordinal logistic analysis indicated that contributing factors to breast density classification were higher breast density at the start (odds ratio = 0.044; 95% CI [0.025-0.076]), higher BMI at the start (odds ratio = 0.76; 95% CI [0.70-0.83]), increased BMI (odds ratio = 0.57; 95% CI [0.36-0.92]), and age in the 40s at the start (odds ratio = 0.49; 95% CI [0.24-0.99]). No statistically significant differences were found for medical history. Conclusion: Breast density decreased in approximately 25% of women over a 10-year period. Women with decreased breast density tended to have higher breast density or higher BMI at the start. This effect was more pronounced among women in their 40s at the start. Women with these conditions may experience changes in breast density over time. The present study would be useful to consider effective screening mammography based on breast density.

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.

BACKGROUND: Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). RESULTS: To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. CONCLUSIONS: MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.

Simulation of the crosstalk between glucose and acetaminophen metabolism in a liver zonation model.

The liver metabolizes a variety of substances that sometimes interact and regulate each other. The modeling of a single cell or a single metabolic pathway does not represent the complexity of the organ, including metabolic zonation (heterogeneity of functions) along with liver sinusoids. Here, we integrated multiple metabolic pathways into a single numerical liver zonation model, including drug and glucose metabolism. The model simulated the time-course of metabolite concentrations by the combination of dynamic simulation and metabolic flux analysis and successfully reproduced metabolic zonation and localized hepatotoxicity induced by acetaminophen (APAP). Drug metabolism was affected by nutritional status as the glucuronidation reaction rate changed. Moreover, sensitivity analysis suggested that the reported metabolic characteristics of obese adults and healthy infants in glucose metabolism could be associated with the metabolic features of those in drug metabolism. High activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphate phosphatase in obese adults led to increased APAP oxidation by cytochrome P450 2E1. In contrast, the high activity of glycogen synthase and low activities of PEPCK and glycogen phosphorylase in healthy infants led to low glucuronidation and high sulfation rates of APAP. In summary, this model showed the effects of glucose metabolism on drug metabolism by integrating multiple pathways into a single liver metabolic zonation model.

Inflammation and Bone Metabolism in Rheumatoid Arthritis: Molecular Mechanisms of Joint Destruction and Pharmacological Treatments.

Rheumatoid arthritis (RA) is an inflammatory disease characterized by a variety of symptoms and pathologies often presenting with polyarthritis. The primary symptom in the initial stage is joint swelling due to synovitis. With disease progression, cartilage and bone are affected to cause joint deformities. Advanced osteoarticular destruction and deformation can cause irreversible physical disabilities. Physical disabilities not only deteriorate patients' quality of life but also have substantial medical economic effects on society. Therefore, prevention of the progression of osteoarticular destruction and deformation is an important task. Recent studies have progressively improved our understanding of the molecular mechanism by which synovitis caused by immune disorders results in activation of osteoclasts; activated osteoclasts in turn cause bone destruction and para-articular osteoporosis. In this paper, we review the mechanisms of bone metabolism under physiological and RA conditions, and we describe the effects of therapeutic intervention against RA on bone.

Chair sit-to-stand performance is associated with diagnostic features of sarcopenia in older men and women.

BACKGROUND: Standing from a chair is a fundamental activity of daily living, and it can be applied to assess the physical function, especially in older individuals. AIM: The aim of this study was to elucidate the characteristics of mechanical and temporal parameters during chair stand based on the relationship with skeletal muscle and physical functional parameters in older men and women. METHODS: Eighty older men and women participated in this study. We measured four parameters of chair stand performance: ground reaction force (GRF), rate of force development (RFD), and chair rise time (CRT) were calculated from the foot-floor force data; sit-to-stand (STS) was also assessed by measuring the time needed to complete 10 chair stand repetitions. The muscle thickness (MT) and echo intensity, as indexes of muscle size and quality, respectively, were measured using axial B-mode ultrasound images from quadriceps femoris. The gait speed and handgrip strength were measured as physical functional parameters. RESULTS: Partial correlation was used to determine the association of chair stand performance with MT, echo intensity, and physical parameters while considering the height, body mass, and age. GRF, RFD, and STS were significantly correlated with MT (r = 0.35, 0.26, and -0.49), gait speed (r = 0.32, 0.31, and -0.67), and handgrip strength (r = 0.57, 0.59, and -0.49). As the result of regression analysis, MT, gait speed, and handgrip strength were estimated by GRF and STS. CONCLUSION: These results suggest that chair stand performance is useful as it reflects the muscle size and physical functions in older individuals.

Gefitinib initiates sterile inflammation by promoting IL-1ß and HMGB1 release via two distinct mechanisms.

Anticancer drug gefitinib causes inflammation-based side effects, such as interstitial pneumonitis. However, its mechanisms remain unknown. Here, we provide evidence that gefitinib elicits pro-inflammatory responses by promoting mature-interleukin-1ß (IL-1ß) and high-mobility group box 1 (HMGB1) release. Mitochondrial reactive oxygen species (mtROS) driven by gefitinib stimulated the formation of the NLRP3 (NACHT, LRR and PYD-containing protein 3) inflammasome, leading to mature-IL-1ß release. Notably, gefitinib also stimulated HMGB1 release, which is, however, not mediated by the NLRP3 inflammasome. On the other hand, gefitinib-driven mtROS promoted the accumulation of γH2AX, a hallmark of DNA damage, leading to the activation of poly (ADP-ribose) polymerase-1 (PARP-1) and subsequent active release of HMGB1. Together our results reveal the potential ability of gefitinib to initiate sterile inflammation via two distinct mechanisms, and identified IL-1ß and HMGB1 as key determinants of gefitinib-induced inflammation that may provide insights into gefitinib-induced interstitial pneumonitis.

A Case of an Elderly Patient With Rubber Band Syndrome.

Rubber band syndrome is a relatively rare disease in which a rubber band around a limb becomes embedded under the skin, resulting in tissue damage. Most reported cases are in children, and its occurrence in adults is considered extremely rare. We present a case of a 71-year-old patient with cognitive impairment, in whom a rubber band around the wrist became embedded under the skin. The examination of the distinctive circumferential scar, ultrasonography, x-ray, and magnetic resonance imaging led to the diagnosis of rubber band syndrome. To avoid further damage to the tissue, surgical removal of the band was conducted. When elderly patients with cognitive impairment present with chief complaints of swelling and contracture in the limbs due to an unknown cause, accompanied by a circumferential scar on the affected limb, rubber band syndrome should be considered. Due to risk of deep tissue necrosis, prompt band removal is necessary.

Comparison of muscle quality and functional capacity between Japanese and Brazilian older individuals.

Muscle quality is well-known to decrease with aging and is a risk factor for metabolic abnormalities. However, there is a lack of information on race-associated differences in muscle quality and other neuromuscular features related to functional performance. This study aimed to compare muscle quality, function, and morphological characteristics in Japanese and Brazilian older individuals. Eighty-four participants aged 65-87 years were enrolled in the study (42 Japanese: 23 men, 19 women, mean age 70.4 years; 42 Brazilians: 23 men, 19 women, mean age 70.8 years). Echo intensity (EI) and muscle thickness (MT) of the quadriceps femoris were measured using B-mode ultrasonography. A stepwise multiple linear regression analysis with EI as a dependent variable revealed that MT was a significant variable for Japanese participants (R2 = 0.424, P = 0.001), while MT and subcutaneous adipose tissue (SCAT) thickness were significant variables for Brazilian participants (R2 = 0.490, P = 0.001). A second stepwise multiple linear regression analysis was performed after excluding MT and SCAT thickness from the independent variables. Sex and age for Japanese participants (R2 = 0.381, P = 0.001) and lean body mass and body mass index for Brazilian participants (R2 = 0.385, P = 0.001) were identified as significant independent variables. The present results suggest that MT is closely correlated with muscle quality in Japanese and Brazilian older individuals. Increases in muscle size may induce decreases in intramuscular adipose tissue and/or connective tissues, which are beneficial for reducing the risks of metabolic impairments in Japanese and Brazilian older individuals.

Ex Vivo Gene Therapy Treats Bone Complications of Mucopolysaccharidosis Type II Mouse Models through Bone Remodeling Reactivation.

Mucopolysaccharidosis type II is a disease caused by organ accumulation of glycosaminoglycans due to iduronate 2-sulfatase deficiency. This study investigated the pathophysiology of the bone complications associated with mucopolysaccharidosis II and the effect of lentivirus-mediated gene therapy of hematopoietic stem cells on bone lesions of mucopolysaccharidosis type II mouse models in comparison with enzyme replacement therapy. Bone volume, density, strength, and trabecular number were significantly higher in the untreated mucopolysaccharidosis type II mice than in wild-type mice. Accumulation of glycosaminoglycans caused reduced bone metabolism. Specifically, persistent high serum iduronate 2-sulfatase levels and release of glycosaminoglycans from osteoblasts and osteoclasts in mucopolysaccharidosis type II mice that had undergone gene therapy reactivated bone lineage remodeling, subsequently reducing bone mineral density, strength, and trabecular number to a similar degree as that observed in wild-type mice. Bone formation, resorption parameters, and mineral density in the diaphysis edge did not appear to have been affected by the irradiation administered as a pre-treatment for gene therapy. Hence, the therapeutic effect of gene therapy on the bone complications of mucopolysaccharidosis type II mice possibly outweighed that of enzyme replacement therapy in many aspects.

The Regulation of Bone Metabolism and Disorders by Wnt Signaling.

Wnt, a secreted glycoprotein, has an approximate molecular weight of 40 kDa, and it is a cytokine involved in various biological phenomena including ontogeny, morphogenesis, carcinogenesis, and maintenance of stem cells. The Wnt signaling pathway can be classified into two main pathways: canonical and non-canonical. Of these, the canonical Wnt signaling pathway promotes osteogenesis. Sclerostin produced by osteocytes is an inhibitor of this pathway, thereby inhibiting osteogenesis. Recently, osteoporosis treatment using an anti-sclerostin therapy has been introduced. In this review, the basics of Wnt signaling, its role in bone metabolism and its involvement in skeletal disorders have been covered. Furthermore, the clinical significance and future scopes of Wnt signaling in osteoporosis, osteoarthritis, rheumatoid arthritis and neoplasia are discussed.

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network.

The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a 'rubber band method' to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.