We present a simplified phenomenological computational framework that integrates the GABA shunt into established metabolic mechanisms underlying pancreatic beta cell insulin secretion. The GABA shunt introduces carbon into the tricarboxylic acid (TCA) cycle via succinate, thereby functioning as an anaplerotic pathway. This anaplerotic input is coupled to oscillatory cataplerotic fluxes, primarily involving α-ketoglutarate, whose effective extrusion requires coordinated counter-fluxes of malate and aspartate. Within the model, these cataplerotic exchanges are facilitated by UCP2-mediated transport processes and necessitate complementary anaplerotic replenishment through pyruvate carboxylase (PC). Based on this functional interdependence, we introduce the Dual Anaplerotic Model (DAM), which conceptually links two anaplerotic routes—the GABA shunt-mediated pathway and the glucose-dependent PC pathway—into a unified metabolic response module. DAM describes a coordinated, breathing-like redistribution of carbon between mitochondrial and cytosolic metabolite pools, while efficient oxidative metabolism of glucose-derived carbon entering the TCA cycle via pyruvate dehydrogenase is maintained. The model is driven by experimentally observed ATP/ADP and Ca2+ dynamics and is not intended to generate autonomous oscillations. Instead, it enables qualitative, phase-dependent visualization of how dual anaplerotic fluxes constrain and shape oscillatory metabolic states in beta cells. DAM provides an integrative conceptual scaffold for interpreting experimental observations and for motivating future quantitative modeling and experimental studies addressing metabolic regulation in physiological and pathophysiological contexts.

Background/Objectives: Gamma-aminobutyric acid (GABA) is increasingly recognized as an important modulator of pancreatic beta-cell function, but the mechanisms by which it regulates intracellular Ca2+ oscillations and coordinated beta-cell activity remain insufficiently understood. The aim of this study was to investigate how GABA influences the amplitude, frequency, phase adjustment, entrainment, and synchronization of beta-cell Ca2+ oscillations. Methods: We developed a reduced ATP–Ca2+ oscillation model, based on established beta-cell oscillatory frameworks, and coupled it to the GABA-shunt subsystem derived from our previously established Dual Anaplerotic Model. The model incorporates explicit dynamics of cytosolic Ca2+, endoplasmic reticulum Ca2+, ATP, and a regulatory variable controlling Ca2+ influx, while the interstitial GABA signal is represented as a delayed feedback signal acting on cellular excitability. Single-cell and two-cell simulations were performed to analyze GABA-dependent oscillatory regulation and intercellular coupling. Results: The model reproduced key experimental observations under both control and GABA-deficient conditions, including reduced Ca2+-oscillation amplitude and a prolonged oscillation period when GABA production was suppressed. Mechanistically, GABA affected single-cell oscillations through two complementary pathways: metabolically, by modulating ATP production through PEP-related and TCA-related contributions linked to the GABA shunt, and as an interstitial/paracrine signal, by adjusting the phase of Ca2+ influx through fast and delayed inhibitory feedback. In the reduced two-cell model, delayed interstitial GABA signaling could phase-lock non-identical oscillators over finite ranges of parameter mismatch. When included as an additional weak effective term, electrical coupling broadened these ranges, consistent with a complementary interaction between GABA-mediated phase adjustment and established electrical coupling. Conclusions: GABA acts as a dual regulator of beta-cell dynamics, linking intracellular metabolism to Ca2+-oscillation patterning and promoting coordinated activity through intercellular phase adjustment. The model provides a mechanistic framework connecting GABA metabolism, ATP dynamics, Ca2+ signaling, and beta-cell synchronization in pancreatic islets.

Abstract Gastrointestinal bacterial infections are associated with substantial perturbations of the gut microbiota, yet most microbiome studies have examined individual pathogens in isolation, limiting identification of shared and pathogen-specific microbial signatures across enteric infections. We performed a comparative analysis of fecal microbiota profiles from 586 stool samples using 16S rRNA gene sequencing, encompassing infections caused by Clostridioides difficile , Campylobacter spp., and Salmonella spp., alongside viral gastroenteritis, diagnostic-negative samples, and healthy controls. Fecal calprotectin concentrations were measured in a subset of samples to assess intestinal inflammation. Comparative analyses revealed two major dysbiotic configurations among bacterial enteric infections. C. difficile infection was characterized by an Enterococcus-dominated community structure, whereas Campylobacter and Salmonella infections were associated with enrichment of a tightly correlated consortium of oral-associated taxa, including Streptococcus , Granulicatella , and Haemophilus . These taxa were among the most informative features in an XGBoost machine-learning classifier, which accurately discriminated bacterial infection types from one another and from viral infections and healthy-associated microbiota profiles (macro F1 score = 0.74). In contrast, expansion of Enterobacteriaceae represented a shared, non-specific signature of intestinal disturbance and was associated with elevated fecal calprotectin concentrations. Together, these findings demonstrate that bacterial enteric infections are associated with distinct microbiota configurations that distinguish pathogen-specific signatures from general infection-related dysbiosis. The enrichment of oral-associated taxa in Campylobacter and Salmonella infections suggests a potential role for the oral–gut microbial axis in bacterial gastroenteritis and provides a foundation for future mechanistic studies and the development of microbiota-informed diagnostic, preventive, and therapeutic strategies. Author Summary Bacterial infections of the intestine can cause severe diarrhea and inflammation, but their effects on the community of microbes living in the gut are not fully understood. Most previous studies have focused on a single disease-causing organism, making it difficult to determine which microbiome changes are shared across infections and which are specific to particular pathogens. In this study, we compared gut microbiota profiles from people infected with Clostridioides difficile , Campylobacter , or Salmonella , and examined these alongside samples from individuals with viral gastroenteritis, diagnostic-negative diarrhea, and healthy controls. We found that bacterial enteric infections were associated with two distinct patterns of microbiome disruption. C. difficile infection was linked to an overgrowth of Enterococcus , whereas Campylobacter and Salmonella infections were characterized by increased levels of several bacterial groups that are commonly found in the mouth. In contrast, expansion of Enterobacteriaceae was observed across different infections and appeared to reflect general intestinal disturbance rather than a specific pathogen. Our findings identify microbial signatures that distinguish different bacterial infections and suggest that bacteria originating from the oral cavity may play an important role in some forms of gastroenteritis. These results provide a foundation for future studies aimed at understanding how microbial communities influence intestinal infection and recovery.

We present a model of ATP production in pancreatic beta cells, focusing on ATP dynamics within the bulk cytosol, submembrane region, and microdomains near KATP channels. ATP is generated through glycolysis, the mitochondrial oxidative phosphorylation (OxPhos) and glycolytic pyruvate kinase-mediated phosphoenolpyruvate (PEP) production, supported by PEP cycling between mitochondria and the cytosol. The model examines ATP production in relation to Ca2+ oscillations, elucidating their interdependent dynamics. Our findings demonstrate that both mitochondrial OxPhos and PEP-mediated ATP production contribute substantially to cellular ATP levels. Specifically, glycolysis and mitochondrial OxPhos are crucial for the initial (first-phase) increase in bulk and subplasmalemmal ATP, effectively “filling up” the ATP pool in beta cells. In the second phase, coordinated cycling between OxPhos and PEP pathways enables cost-effective fine-tuning of ATP levels, with localized effects in the KATP channel microdomains. This model addresses and clarifies the recent debate regarding the mechanisms by which sufficient ATP concentrations are achieved to close KATP channels in glucose-stimulated beta cells, offering novel insights into the regulation of energy production and KATP channel activity.

Pancreatic beta cells regulate insulin secretion in response to glucose by generating ATP, which modulates ATP-sensitive potassium channels (KATP) channel activity and Ca2+ dynamics. We present a model of ATP production in pancreatic beta cells, focusing on ATP dynamics within the bulk cytosol, submembrane region, and microdomains near KATP channels. ATP is generated through glycolysis, mitochondrial oxidative phosphorylation (OxPhos), and glycolytic pyruvate kinase-mediated phosphoenolpyruvate (PEP) production, supported by PEP cycling between mitochondria and the cytosol. The model examines ATP production in relation to Ca2+ oscillations, elucidating their interdependent dynamics. Our findings demonstrate that both mitochondrial OxPhos and PEP-mediated ATP production contribute substantially to cellular ATP levels. Specifically, glycolysis and mitochondrial OxPhos are crucial for the initial (first-phase) increase in bulk and subplasmalemmal ATP, effectively “filling up” the ATP pool in beta cells. In the second phase, coordinated cycling between OxPhos and PEP pathways enables cost-effective fine-tuning of ATP levels, with localized effects in the KATP channel microdomains. This model addresses and clarifies the recent debate regarding the mechanisms by which sufficient ATP concentrations are achieved to close KATP channels in glucose-stimulated beta cells, offering novel insights into the regulation of energy production and KATP channel activity.

The rising incidence of skin cancer, particularly melanoma, underscores the need for improved diagnostic tools in dermatology. Accurate imaging plays a crucial role in early detection, yet challenges related to color accuracy, image distortion, and resolution persist, leading to diagnostic errors. This study addresses these issues by evaluating color reproduction accuracy across various imaging devices and lighting conditions. Using a ColorChecker test chart, color deviations were measured through Euclidean distances (ΔE*, ΔC*), and nonlinear color differences (ΔE00, ΔC00), while the color rendering index (CRI) and television lighting consistency index (TLCI) were used to evaluate the influence of light sources on image accuracy. Significant color discrepancies were identified among mobile phones, DSLRs, and mirrorless cameras, with inadequate dermatoscope lighting systems contributing to further inaccuracies. We demonstrate practical applications, including manual camera adjustments, grayscale reference cards, post-processing techniques, and optimized lighting conditions, to improve color accuracy. This study provides applicable solutions for enhancing color accuracy in dermatological imaging, emphasizing the need for standardized calibration techniques and imaging protocols to improve diagnostic reliability, support AI-assisted skin cancer detection, and contribute to high-quality image databases for clinical and automated analysis.

This study investigates the metabolic parallels between stimulated pancreatic beta cells and cancer cells, focusing on glucose and glutamine metabolism. Addressing the significant public health challenges of Type 2 Diabetes (T2D) and cancer, we aim to deepen our understanding of the mechanisms driving insulin secretion and cellular proliferation. Our analysis of anaplerotic cycles and the role of NADPH in biosynthesis elucidates their vital functions in both processes. Additionally, we point out that both cell types share an antioxidative response mediated by the Nrf2 signaling pathway, glutathione synthesis, and UCP2 upregulation. Notably, UCP2 facilitates the transfer of C4 metabolites, enhancing reductive TCA cycle metabolism. Furthermore, we observe that hypoxic responses are transient in beta cells post-stimulation but persistent in cancer cells. By synthesizing these insights, the research may suggest novel therapeutic targets for T2D, highlighting the shared metabolic strategies of stimulated beta cells and cancer cells. This comparative analysis not only illuminates the metabolic complexity of these conditions but also emphasizes the crucial role of metabolic pathways in cell function and survival, offering fresh perspectives for tackling T2D and cancer challenges.

In recent years, a notable diversification in the geographical representation of the World’s top universities could be observed. Chinese universities play an increasingly prominent role in these rankings, thus indicating that we might be in the midst of a regional shift in academic performance. To explore this dynamics, we analyze seven years’ worth of data used for university rankings by academic subjects from the Academic Ranking of World Universities (ARWU). We focus on China, Europe, the USA, and other global regions of the world. We find that China has indeed seen an unprecedented growth in Engineering and Life Sciences research, positioning itself rather firmly as a leader in these fields. Conversely, the USA leads in the Social Sciences, while Europe excels in Geography, Ecology, Public and Business Administration, and Pharmacy. Other regions worldwide stand out in Transportation Science, Nursing, and Hospitality Tourism Management. These results reveal the evolving landscape of global academic research, highlighting regional strengths and emerging world trends in subject-specific excellence.

Background: The interrelation between COVID-19 and various cardiovascular and metabolic disorders has been a critical areaof study. There is a growing need to understand how comorbidities such as cardiovascular diseases (CVDs) and metabolic disordersaffect the risk and severity of COVID-19. Objective: The objective of this study is to systematically analyze the association between COVID-19 and cardiovascular andmetabolic disorders. The focus is on comorbidity, examining the roles of CVDs such as embolism, thrombosis, hypertension,and heart failure, as well as metabolic disorders such as disorders of glucose and iron metabolism. Methods: Our study involved a systematic search in PubMed for literature published from 2000 to 2022. We established 2databases: one for COVID-19-related articles and another for CVD-related articles, ensuring all were peer-reviewed. In termsof data analysis, statistical methods were applied to compare the frequency and relevance of MeSH (Medical Subject Headings)terms between the 2 databases. This involved analyzing the differences and ratios in the usage of these terms and employingstatistical tests to determine their significance in relation to key CVDs within the COVID-19 research context. Results: The study revealed that "Cardiovascular Diseases" and "Nutritional and Metabolic Diseases" were highly relevant aslevel 1 Medical Subject Headings descriptors in COVID-19 comorbidity research. Detailed analysis at level 2 and level 3 showed"Vascular Disease" and "Heart Disease" as prominent descriptors under CVDs. Significantly, "Glucose Metabolism Disorders"were frequently associated with COVID-19 comorbidities such as embolism, thrombosis, and heart failure. Furthermore, irondeficiency (ID) was notably different in its occurrence between COVID-19 and CVD articles, underlining its significance in thecontext of COVID-19 comorbidities. Statistical analysis underscored these differences, highlighting the importance of bothglucose and iron metabolism disorders in COVID-19 research. Conclusions: This work lays the foundation for future research that utilizes a knowledge-based approach to elucidate the intricaterelationships between these conditions, aiming to develop more effective health care strategies and interventions in the face ofongoing pandemic challenges

We propose a detailed computational beta cell model that emphasizes the role of anaplerotic metabolism under glucose and glucose-glutamine stimulation. This model goes beyond the traditional focus on mitochondrial oxidative phosphorylation and ATP-sensitive K+ channels, highlighting the predominant generation of ATP from phosphoenolpyruvate in the vicinity of KATP channels. It also underlines the modulatory role of H2O2 as a signaling molecule in the first phase of glucose-stimulated insulin secretion. In the second phase, the model emphasizes the critical role of anaplerotic pathways, activated by glucose stimulation via pyruvate carboxylase and by glutamine via glutamate dehydrogenase. It particularly focuses on the production of NADPH and glutamate as key enhancers of insulin secretion. The predictions of the model are consistent with empirical data, highlighting the complex interplay of metabolic pathways and emphasizing the primary role of glucose and the facilitating role of glutamine in insulin secretion. By delineating these crucial metabolic pathways, the model provides valuable insights into potential therapeutic targets for diabetes.

The self-organization of open reaction systems is closely related to specific mechanisms that allow the export of internally generated entropy from systems to their environment. According to the second law of thermodynamics, systems with effective entropy export to the environment are better internally organized. Therefore, they are in thermodynamic states with low entropy. In this context, we study how self-organization in enzymatic reactions depends on their kinetic reaction mechanisms. Enzymatic reactions in an open system are considered to operate in a non-equilibrium steady state, which is achieved by satisfying the principle of maximum entropy production (MEPP). The latter is a general theoretical framework for our theoretical analysis. Detailed theoretical studies and comparisons of the linear irreversible kinetic schemes of an enzyme reaction in two and three states are performed. In both cases, in the optimal and statistically most probable thermodynamic steady state, a diffusion-limited flux is predicted by MEPP. Several thermodynamic quantities and enzymatic kinetic parameters, such as the entropy production rate, the Shannon information entropy, reaction stability, sensitivity, and specificity constants, are predicted. Our results show that the optimal enzyme performance may strongly depend on the number of reaction steps when linear reaction mechanisms are considered. Simple reaction mechanisms with a smaller number of intermediate reaction steps could be better organized internally and could allow fast and stable catalysis. These could be features of the evolutionary mechanisms of highly specialized enzymes.

Introduction:Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases. Methods:We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering. Results:Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75. Conclusion:Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

Background: The pathogenesis of type 2 diabetes mellitus is complex and still unclear in some details. The main feature of diabetes mellitus is high serum glucose, and the question arises of whether there are other statistically observable dysregulations in laboratory measurements before the state of hyperglycemia becomes severe. In the present study, we aim to examine glucose and lipid profiles in the context of age, sex, medication use, and mortality. Methods: We conducted an observational study by analyzing laboratory data from 506,083 anonymized laboratory tests from 63,606 different patients performed by a regional laboratory in Slovenia between 2008 and 2019. Laboratory data-based results were evaluated in the context of medication use and mortality. The medication use database contains anonymized records of 1,632,441 patients from 2013 to 2018, and mortality data were obtained for the entire Slovenian population. Results: We show that the highest percentage of the population with elevated glucose levels occurs approximately 20 years later than the highest percentage with lipid dysregulation. Remarkably, two distinct inflection points were observed in these laboratory results. The first inflection point occurs at ages 55 to 59 years, corresponding to the greatest increase in medication use, and the second coincides with the sharp increase in mortality at ages 75 to 79 years. Conclusions: Our results suggest that medications and mortality are important factors affecting population statistics and must be considered when studying metabolic disorders such as dyslipidemia and hyperglycemia using laboratory data.

Type 2 Diabetes Mellitus (T2DM) is a burdensome problem in modern society, and intensive research is focused on better understanding the underlying cellular mechanisms of hormone secretion for blood glucose regulation. T2DM is a bi-hormonal disease, and in addition to 100 years of increasing knowledge about the importance of insulin, the second hormone glucagon, secreted by pancreatic alpha cells, is becoming increasingly important. We have developed a mathematical model for glucagon secretion that incorporates all major metabolic processes of glucose, fatty acids, and glutamine as the most abundant postprandial amino acid in blood. In addition, we consider cAMP signaling in alpha cells. The model predictions quantitatively estimate the relative importance of specific metabolic and signaling pathways and particularly emphasize the important role of glutamine in promoting glucagon secretion, which is in good agreement with known experimental data.

Hyperlipidemia is a common metabolic disorder in modern society and may precede hyperglycemia and diabetes by several years. Exactly how disorders of lipid and glucose metabolism are related is still a mystery in many respects. We analyze the effects of hyperlipidemia, particularly free fatty acids, on pancreatic beta cells and insulin secretion. We have developed a computational model to quantitatively estimate the effects of specific metabolic pathways on insulin secretion and to assess the effects of short- and long-term exposure of beta cells to elevated concentrations of free fatty acids. We show that the major trigger for insulin secretion is the anaplerotic pathway via the phosphoenolpyruvate cycle, which is affected by free fatty acids via uncoupling protein 2 and proton leak and is particularly destructive in long-term chronic exposure to free fatty acids, leading to increased insulin secretion at low blood glucose and inadequate insulin secretion at high blood glucose. This results in beta cells remaining highly active in the “resting” state at low glucose and being unable to respond to anaplerotic signals at high pyruvate levels, as is the case with high blood glucose. The observed fatty-acid-induced disruption of anaplerotic pathways makes sense in the context of the physiological role of insulin as one of the major anabolic hormones.

Glucose metabolism plays a crucial role in modulating glucagon secretion in pancreatic alpha cells. However, the downstream effects of glucose metabolism and the activated signaling pathways influencing glucagon granule exocytosis are still obscure. We developed a computational alpha cell model, implementing metabolic pathways of glucose and free fatty acids (FFA) catabolism and an intrinsically activated cAMP signaling pathway. According to the model predictions, increased catabolic activity is able to suppress the cAMP signaling pathway, reducing exocytosis in a Ca2+-dependent and Ca2+ independent manner. The effect is synergistic to the pathway involving ATP-dependent closure of KATP channels and consequent reduction of Ca2+. We analyze the contribution of each pathway to glucagon secretion and show that both play decisive roles, providing a kind of “secure double switch”. The cAMP-driven signaling switch plays a dominant role, while the ATP-driven metabolic switch is less favored. The ratio is approximately 60:40, according to the most recent experimental evidence.

NMDA receptors promote repolarization in pancreatic beta cells and thereby reduce glucose-stimulated insulin secretion. Therefore, NMDA receptors are a potential therapeutic target for diabetes. While the mechanism of NMDA receptor inhibition in beta cells is rather well understood at the molecular level, its possible effects on the collective cellular activity have not been addressed to date, even though proper insulin secretion patterns result from well-synchronized beta cell behavior. The latter is enabled by strong intercellular connectivity, which governs propagating calcium waves across the islets and makes the heterogeneous beta cell population work in synchrony. Since a disrupted collective activity is an important and possibly early contributor to impaired insulin secretion and glucose intolerance, it is of utmost importance to understand possible effects of NMDA receptor inhibition on beta cell functional connectivity. To address this issue, we combined confocal functional multicellular calcium imaging in mouse tissue slices with network science approaches. Our results revealed that NMDA receptor inhibition increases, synchronizes, and stabilizes beta cell activity without affecting the velocity or size of calcium waves. To explore intercellular interactions more precisely, we made use of the multilayer network formalism by regarding each calcium wave as an individual network layer, with weighted directed connections portraying the intercellular propagation. NMDA receptor inhibition stabilized both the role of wave initiators and the course of waves. The findings obtained with the experimental antagonist of NMDA receptors, MK-801, were additionally validated with dextrorphan, the active metabolite of the approved drug dextromethorphan, as well as with experiments on NMDA receptor KO mice. In sum, our results provide additional and new evidence for a possible role of NMDA receptor inhibition in treatment of type 2 diabetes and introduce the multilayer network paradigm as a general strategy to examine effects of drugs on connectivity in multicellular systems.

We propose and study an epidemiological model on a social network that takes into account heterogeneity of the population and different vaccination strategies. In particular, we study how the COVID-19 epidemics evolves and how it is contained by different vaccination scenarios by taking into account data showing that older people, as well as individuals with comorbidities and poor metabolic health, and people coming from economically depressed areas with lower quality of life in general, are more likely to develop severe COVID-19 symptoms, and quicker loss of immunity and are therefore more prone to reinfection. Our results reveal that the structure and the spatial arrangement of subpopulations are important epidemiological determinants. In a healthier society the disease spreads more rapidly but the consequences are less disastrous as in a society with more prevalent chronic comorbidities. If individuals with poor health are segregated within one community, the epidemic outcome is less favorable. Moreover, we show that, contrary to currently widely adopted vaccination policies, prioritizing elderly and other higher-risk groups is beneficial only if the supply of vaccine is high. If, however, the vaccination availability is limited, and if the demographic distribution across the social network is homogeneous, better epidemic outcomes are achieved if healthy people are vaccinated first. Only when higher-risk groups are segregated, like in elderly homes, their prioritization will lead to lower COVID-19 related deaths. Accordingly, young and healthy individuals should view vaccine uptake as not only protecting them, but perhaps even more so protecting the more vulnerable socio-demographic groups.

Beta cells within the pancreatic islets of Langerhans respond to stimulation with coherent oscillations of membrane potential and intracellular calcium concentration that presumably drive the pulsatile exocytosis of insulin. Their rhythmic activity is multimodal, resulting from networked feedback interactions of various oscillatory subsystems, such as the glycolytic, mitochondrial, and electrical/calcium components. How these oscillatory modules interact and affect the collective cellular activity, which is a prerequisite for proper hormone release, is incompletely understood. In the present work, we combined advanced confocal Ca 2+ imaging in fresh mouse pancreas tissue slices with time series analysis and network science approaches to unveil the glucose-dependent characteristics of different oscillatory components on both the intra- and inter-cellular level. Our results reveal an interrelationship between the metabolically driven low-frequency component and the electrically driven high-frequency component, with the latter exhibiting the highest bursting rates around the peaks of the slow component and the lowest around the nadirs. Moreover, the activity, as well as the average synchronicity of the fast component, considerably increased with increasing stimulatory glucose concentration, whereas the stimulation level did not affect any of these parameters in the slow component domain. Remarkably, in both dynamical components, the average correlation decreased similarly with intercellular distance, which implies that intercellular communication affects the synchronicity of both types of oscillations. To explore the intra-islet synchronization patterns in more detail, we constructed functional connectivity maps. The subsequent comparison of network characteristics of different oscillatory components showed more locally clustered and segregated networks of fast oscillatory activity, while the slow oscillations were more global, resulting in several long-range connections and a more cohesive structure. Besides the structural differences, we found a relatively weak relationship between the fast and slow network layer, which suggests that different synchronization mechanisms shape the collective cellular activity in islets, a finding which has to be kept in mind in future studies employing different oscillations for constructing networks.

We investigate the relations between the enzyme kinetic flexibility, the rate of entropy production, and the Shannon information entropy in a steady-state enzyme reaction. All these quantities are maximized with respect to enzyme rate constants. We show that the steady-state, which is characterized by the most flexible enzymatic transitions between the enzyme conformational states, coincides with the global maxima of the Shannon information entropy and the rate of entropy production. This steady-state of an enzyme is referred to as globally optimal. This theoretical approach is then used for the analysis of the kinetic and the thermodynamic performance of the enzyme triose-phosphate isomerase. The analysis reveals that there exist well-defined maxima of the kinetic flexibility, the rate of entropy production, and the Shannon information entropy with respect to any arbitrarily chosen rate constant of the enzyme and that these maxima, calculated from the measured kinetic rate constants for the triose-phosphate isomerase are lower, however of the same order of magnitude, as the maxima of the globally optimal state of the enzyme. This suggests that the triose-phosphate isomerase could be a well, but not fully evolved enzyme, as it was previously claimed. Herein presented theoretical investigations also provide clear evidence that the flexibility of enzymatic transitions between the enzyme conformational states is a requirement for the maximal Shannon information entropy and the maximal rate of entropy production.