ELife Assessment: Complex system modeling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria

Background and Aims Gut microbiota is considered an important factor affecting oxalate handling in the intestine. It has been demonstrated that intestinal oxalate secretion provides a complementary route of excretion, and it becomes more evident when kidney function declines. A diversity of gut oxalate-degrading bacteria (ODB) has been hypothesized to play a role in this process. However, there is a general lack of research on the long-term effects of acute kidney injury (AKI) on ODB and their total oxalate-degrading activity (ODA) in fecal microbiota. In this study, we evaluated whether renal dysfunction could affect intestinal ODB and their total ODA in a rat model of glycerol-induced AKI. Method The Male Wistar rats (200-300 g, n=20) on oxalate-free diet were randomly divided into 2 groups. After 24-h of water deprivation, Group 1 (n=10) received an intramuscular injection of 50% glycerol (10 ml/kg of body weight), and Group 2 (n=10) served as control. The numbers of ODB (incubated in a highly selective Oxalate Medium and determined using culture method) and total fecal ODA were measured after injection on days 7 and 70. The method of redoximetric titration with a KMnO4 solution was adopted to evaluate total ODA in fecal microbiota; the results were expressed as % of oxalate degradation per 0.01 g of feces. Renal injury was assessed by histopathological examination, serum creatinine and daily proteinuria levels after removing the animals from the experiment on day 70. Cortical interstitial fibrosis was measured by computerized image analysis on sections stained with picrosirius red. The median (Me) and the interquartile ranges (Q25; Q75) were calculated and compared using the nonparametric Mann-Whitney test. The Spearman correlation coefficient was used to evaluate association between the examined parameters. Results The obtained results demonstrated: 1) after glycerol injection on day 7, no differences were found in the numbers of ODB and total fecal ODA between the experimental and control groups: 5.9 (5.4-6.0) vs 6.0 (5.4-6.4) CFU/g, p=0.65 and 2.0 (0.1-5.0) vs 2.5 (2.0-9.0) %/0.01g, p=0.24, respectively; 2) after AKI initiation on day 70, the numbers of ODB and total fecal ODA were significantly lower in Group I compared with control Group II (Fig. 1); 3) the higher percentage of renal interstitial fibrosis was, the higher total fecal ODA occurred in the experimental rats (Fig. 2). In addition, the number of ODB in feces in Group 1 had an inverse association with serum creatinine (r=-0.52, p=0.006) and 24-h proteinuria levels (r=-0.86, p<0.0001). Conclusion AKI had the long-term negative effects on the quantitative and qualitative characteristics of ODB in fecal microbiota in rats. Moreover, the results of our study confirmed an increasing trend in total fecal ODA according to the aggravation of renal interstitial fibrosis in rats.

Oxalate is present in environments as diverse as soils or gastrointestinal tracts. This organic acid can be found as free acid or forming metal salts (e.g. calcium, magnesium). Oxalotrophy, the ability to use oxalate as carbon and energy sources, is mainly the result of bacterial catabolism, which can be either aerobic or anaerobic. Although some oxalotrophic bacterial strains are commonly used as probiotics, little is known about the diversity and ecology of this functional group. This review aims at exploring the taxonomic distribution and the phylogenetic diversity of oxalotrophic bacteria across biomes. In silico analyses were conducted using the two key enzymes involved in oxalotrophy: formyl-coenzyme A (CoA) transferase (EC 2.8.3.16) and oxalyl-CoA decarboxylase (EC 4.1.1.8), encoded by the frc and oxc genes, respectively. Our analyses revealed that oxalate-degrading bacteria are restricted to three phyla, namely Actinobacteria, Firmicutes and Proteobacteria and originated from terrestrial, aquatic and clinical environments. Diversity analyses at the protein level suggest that total Oxc diversity is more constrained than Frc diversity and that bacterial oxalotrophic diversity is not yet fully described. Finally, the contribution of oxalotrophic bacteria to ecosystem functioning as well as to the carbon cycle is discussed.

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe- host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, animal, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Microbiome composition through the redundancy of oxalate-degrading bacteria, critically influence oxalate metabolism, explaining variable outcomes of Oxalobacter formigenes therapy and offers insights for improving probiotic selection and clinical trial strategies.

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, animal, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

This paper proposes a new approach to determining the complex system design for a product mix comprising complex hierarchies of subassembly and components. Pareto Ant Colony Optimisation as an especially effective meta-heuristic for solving the problem of complex system design was introduced in this paper. A Pareto Optimal Set of complex system in which only the non dominated solutions allow ants to deposit pheromones over the time and cost pheromone matrices after certain generation runs. Simulation results show that the model for complex system and the hybrid algorithms are effective to the design of complex system.

Unconventional modelling of complex system via cellular automata and differential evolution

Currently, the dynamic and evolutionary nature of complex systems has gained increasing prominence. Intelligent behavior plays an important role in driving the dynamic evolution of complex systems. However, current agent-specific modeling languages (e.g. AML) struggle to integrate with complex system modeling tools (e.g. SysML), while SysML itself faces challenges in directly modeling and simulating intelligent behavior. Compared to traditional methods that combine agent-specific tools with complex system modeling languages, X language provides an integrated approach to modeling both complex systems and intelligent behaviors. However, its focus remains on single-agent system design, with limited support for multi-agent interaction and learning behavior modeling in agents. To solve this problem, this paper expands the agent class of the X language, and provides multi-agent interaction syntax and reinforcement learning (RL)-based learning ability for the X language. At the same time, based on the MADES agent framework, we implemented the compiler algorithm to compile the extended syntax into the MADES simulation file on the basis of the X language compiler. Finally, a multi-agent autonomous driving model was developed using the Q-learning algorithm to verify the modeling syntax. The results show that the extended X language syntax effectively supports building multi-agent interaction systems with learning capabilities.

This article introduces a novel modular approach to the modelling of nonlinear interconnected complex systems. In the approach, a complex system is decomposed into self-contained easily identified modules to achieve easy linking of individual models. Our proposed approach to modelling differs from the traditional approaches in the following important aspects: (1) it is nonlinear, modular based, systematic, and graphical; (2) it is suitable for fault diagnosis; (3) depending on the level of complexity required, each module may be changed separately quite easily without having to change the other modules; and (4) it draws from the powerful capabilities of the widely used packages of Simulinkand Matlab. These features provide for a generic package that can simulate a complex interconnected system of any description and configuration, as well as simulating any kind of fault anywhere in the system by merely changing the input vector. Examples of two power systems are used as case studies, where it is shown that complex systems can be constructed and simulated easily by appropriately combining components together. The simulation studies show the dynamical behaviour of the two systems with and without faults being applied to them.

This article introduces a novel modular approach to the modelling of nonlinear interconnected complex systems. In the approach, a complex system is decomposed into self-contained easily identified modules to achieve easy linking of individual models. Our proposed approach to modelling differs from the traditional approaches in the following important aspects: (1) it is nonlinear, modular based, systematic, and graphical; (2) it is suitable for fault diagnosis; (3) depending on the level of complexity required, each module may be changed separately quite easily without having to change the other modules; and (4) it draws from the powerful capabilities of the widely used packages of SimulinkTM and MatlabTM . These features provide for a generic package that can simulate a complex interconnected system of any description and configuration, as well as simulating any kind of fault anywhere in the system by merely changing the input vector. Examples of two power systems are used as case studies, where it is shown that complex systems can be constructed and simulated easily by appropriately combining components together. The simulation studies show the dynamical behaviour of the two systems with and without faults being applied to them.

A method of determining the stresses that arise in individual elements of a complex system and that lead to deformation of these elements is proposed. Using the proposed method, the relationship between the topology of a surface of an object, the deforming load, and the interference pattern is established.

In large-scale complex system, the fault association is completed and varies with the system working status. In this paper, a new fault description –fuzzy probability SDG model (FPSDG) is proposed by introducing the status association probability and fuzzy information. The modeling method and procedure of diagnosis is also proposed after analyzing the information transfer characteristic of the proposed model. On this basis, the message node and route is introduced to connect the FPSDG of those subsystems, and then the FPSDGS model of whole complex system is constructed. The model can model the complicated association of system malfunction and diagnose. Finally, with the proposed method, a fault diagnosis system of a weapon carrier is built. The experiment result of this system proved the effectivity and utility of this model.

Evolutionary Games and the Modeling of Complex Systems

Several mathematical formalisms can be exploited to model complex systems, in order to capture different features of their dynamic behavior and leverage any available quantitative or qualitative data. Correspondingly, either quantitative models or qualitative models can be defined; bridging the gap between these two worlds would allow us to simultaneously exploit the peculiar advantages provided by each modeling approach. However, to date, the attempts in this direction have been limited to specific fields of research. In this paper, we propose a novel, general-purpose computational framework, named Fuzzy-mechanistic modeling of compleX systems (FuzzX), for the analysis of hybrid models consisting of a quantitative (or mechanistic) module and a qualitative module that can reciprocally control each other's dynamic behavior through a common interface. FuzzX takes advantage of precise quantitative information about the system through the definition and simulation of the mechanistic module. At the same time, it describes the behavior of components and their interactions that are not known in full details, by exploiting fuzzy logic for the definition of the qualitative module. We applied FuzzX for the analysis of a hybrid model of a complex biochemical system, characterized by the presence of positive and negative feedback regulations. We show that FuzzX is able to correctly reproduce known emergent behaviors of this system in normal and perturbed conditions. We envision that FuzzX could be employed to analyze any kind of complex system when quantitative information is limited, as well as to extend existing mechanistic models with fuzzy modules to describe those components and interactions of the system that are not fully characterized.