Analysis of large-scale metabolic networks: organization theory, phenotype prediction and elementary flux patterns

Analyzing metabolic networks is one of the central topics in systems biology. Thus, different methods for analyzing the structure of such networks have been proposed. The methods used here can be broadly divided into two types: flux-centered approaches like elementary mode analysis (EM analysis), the closely related extreme pathway analysis as well as flux balance analysis (FBA) and approaches like chemical organization theory (OT), that additionally explicitly takes into account metabolites. The aim of this work is the integration of these concepts in order to allow for a more comprehensive analysis of metabolic networks. Indeed, EM analysis and OT can complement each other in two ways. First, the set of chemical organizations of a reaction network allows for a clustering of EMs and helps to identify those EMs that cannot operate at steady state. Second, the set of EMs can be used to compute chemical organizations. In another direction, the combination FBA and EM analysis helps to overcome the problem that the entire set of EMs cannot be computed in large metabolic networks. The framework behind this integration, elementary flux pattern analysis (EFP analysis), allows one to identify all possible routes through a subsystem that are part of a pathway in a genome-scale metabolic network. An important benefit of this concept is that it allows to apply many approaches building on EM analysis to genome-scale metabolic networks. Furthermore, using EFP analysis, we identified several EMs in a subsystem of a metabolic model of Escherichia coli that are not compatible with a pathway on the genome scale. Additionally, we discovered several alternative routes to metabolic pathways in the central metabolism of E. coli. Using EFP analysis in a genome-scale metabolic model of humans we found several pathways that contradict the widely held assumption in biochemistry that the conversion of even-chain fatty acids into glucose is infeasible in humans

Phenotype prediction in regulated metabolic networks

<p>Abstract</p> <p>Background</p> <p>Due to the growing amount of biological knowledge that is incorporated into metabolic network models, their analysis has become more and more challenging. Here, we examine the capabilities of the recently introduced chemical organization theory (OT) to ease this task. Considering only network stoichiometry, the theory allows the prediction of all potentially persistent species sets and therewith rigorously relates the structure of a network to its potential dynamics. By this, the phenotypes implied by a metabolic network can be predicted without the need for explicit knowledge of the detailed reaction kinetics.</p> <p>Results</p> <p>We propose an approach to deal with regulation – and especially inhibitory interactions – in chemical organization theory. One advantage of this approach is that the metabolic network and its regulation are represented in an integrated way as one reaction network. To demonstrate the feasibility of this approach we examine a model by Covert and Palsson (J Biol Chem, 277(31), 2002) of the central metabolism of <it>E. coli </it>that incorporates the regulation of all involved genes. Our method correctly predicts the known growth phenotypes on 16 different substrates. Without specific assumptions, organization theory correctly predicts the lethality of knockout experiments in 101 out of 116 cases. Taking into account the same model specific assumptions as in the regulatory flux balance analysis (rFBA) by Covert and Palsson, the same performance is achieved (106 correctly predicted cases). Two model specific assumptions had to be considered: first, we have to assume that secreted molecules do not influence the regulatory system, and second, that metabolites with increasing concentrations indicate a lethal state.</p> <p>Conclusion</p> <p>The introduced approach to model a metabolic network and its regulation in an integrated way as one reaction network makes organization analysis a universal technique to study the potential behavior of biological network models. Applying multiple methods like OT and rFBA is shown to be valuable to uncover critical assumptions and helps to improve model coherence.</p

Penerapan Pendekatan Kontekstual untuk Meningkatkan Prestasi Belajar Matematika pada Materi Lingkaran Bagi Siswa Kelas VIII C SMP Negeri 1 Karangawen Demak Tahun Pelajaran 2008/2009

Sampai saat ini, pendidikan masih memegang peranan yang sangat penting. Dengan adanya pendidikan, sumber daya manusia dapat berkembang menuju ke arah yang lebih baik dan salah satunyanya adalah dapat dilihat dari prestasi belajar yang telah dicapai oleh peserta didik. Dalam perkembangannya, guru harus memiliki keahlian untuk memilih dan menggunakan metode pengajaran yang sesuai dengan mata pelajaran khususnya matematika serta mengetahui kondisi peserta didik disamping penguasaan keterampilan yang lain karena matematika merupakan mata pelajaran yang dianggap sulit oleh sebagian besar peserta didik. Untuk mengatasi permasalahan tersebut, diperlukan suatu metode pembelajaran yang berguna untuk meningkatkan minat agar aktivitas dan prestasi belajar peserta didik optimal yaitu dengan menggunakan pendekatan kontekstual. Penelitian ini ditempuh melalui 3 siklus, tiap siklus terdiri atas 4 tahapan, yaitu perencanaan, pelaksanaan/tindakan, pengamatan/observasi, dan refleksi. Metode pengambilan data dengan pemanfaatan Lembar Kerja, tes tertulis (evaluasi), lembar pengamatan guru dan lembar pengamatan peserta didik serta angket refleksi peserta didik. Hasil penelitian menunjukkan persentase ketuntasan belajar mengalami peningkatan yaitu dari 86,55 % pada siklus I menjadi 87,14 % pada siklus II dan meningkat lagi menjadi 90,81 % pada siklus III. Aktivitas peserta didik mengalami peningkatan dari persentase 72,5 % pada siklus I menjadi 88,75 % pada siklus II dan meningkat lagi menjadi 90 % pada siklus III. Aktivitas guru yang diperoleh lembar pengamatan aktivitas guru mengalami peningkatan dari persentase 71,6 % pada siklus I menjadi 87,5 % pada siklus II dan meningkat lagi menjadi 92 % pada siklus III. Dari hasil yang diperoleh dapat disimpulkan bahwa dengan penerapan pendekatan kontekstual pada materi lingkaran dapat meningkatkan prestasi belajar dan aktivitas peserta didik kelas VIII?é?á C SMP Negeri 1 Karangawen Demak sesuai dengan indikator yang ditentukan. ?é?

Parkinson’s Disease and the Metal–Microbiome–Gut–Brain Axis: A Systems Toxicology Approach

Parkinson’s Disease (PD) is a neurodegenerative disease, leading to motor and non-motor complications. Autonomic alterations, including gastrointestinal symptoms, precede motor defects and act as early warning signs. Chronic exposure to dietary, environmental heavy metals impacts the gastrointestinal system and host-associated microbiome, eventually affecting the central nervous system. The correlation between dysbiosis and PD suggests a functional and bidirectional communication between the gut and the brain. The bioaccumulation of metals promotes stress mechanisms by increasing reactive oxygen species, likely altering the bidirectional gut–brain link. To better understand the differing molecular mechanisms underlying PD, integrative modeling approaches are necessary to connect multifactorial perturbations in this heterogeneous disorder. By exploring the effects of gut microbiota modulation on dietary heavy metal exposure in relation to PD onset, the modification of the host-associated microbiome to mitigate neurological stress may be a future treatment option against neurodegeneration through bioremediation. The progressive movement towards a systems toxicology framework for precision medicine can uncover molecular mechanisms underlying PD onset such as metal regulation and microbial community interactions by developing predictive models to better understand PD etiology to identify options for novel treatments and beyond. Several methodologies recently addressed the complexity of this interaction from different perspectives; however, to date, a comprehensive review of these approaches is still lacking. Therefore, our main aim through this manuscript is to fill this gap in the scientific literature by reviewing recently published papers to address the surrounding questions regarding the underlying molecular mechanisms between metals, microbiota, and the gut–brain-axis, as well as the regulation of this system to prevent neurodegeneration

Impact of Chromosomal Architecture on the Function and Evolution of Bacterial Genomes

The bacterial nucleoid is highly condensed and forms compartment-like structures within the cell. Much attention has been devoted to investigating the dynamic topology and organization of the nucleoid. In contrast, the specific nucleoid organization, and the relationship between nucleoid structure and function is often neglected with regard to importance for adaption to changing environments and horizontal gene acquisition. In this review, we focus on the structure-function relationship in the bacterial nucleoid. We provide an overview of the fundamental properties that shape the chromosome as a structured yet dynamic macromolecule. These fundamental properties are then considered in the context of the living cell, with focus on how the informational flow affects the nucleoid structure, which in turn impacts on the genetic output. Subsequently, the dynamic living nucleoid will be discussed in the context of evolution. We will address how the acquisition of foreign DNA impacts nucleoid structure, and conversely, how nucleoid structure constrains the successful and sustainable chromosomal integration of novel DNA. Finally, we will discuss current challenges and directions of research in understanding the role of chromosomal architecture in bacterial survival and adaptation

Using chemical organization theory for model checking

Motivation: The increasing number and complexity of biomodels makes automatic procedures for checking the models' properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws

BacArena: Individual-Based Metabolic Modeling of Heterogeneous Microbes in Complex Communities

Recent advances focusing on the metabolic interactions within and between cellular populations, have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena), to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations, which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena

In Silico Approaches and the Role of Ontologies in Aging Research

The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin

Voluntary Wheel Running in Old C57BL/6 Mice Reduces Age-Related Inflammation in the Colon but Not in the Brain

Inflammation is considered a possible cause of cognitive decline during aging. This study investigates the influence of physical activity and social isolation in old mice on their cognitive functions and inflammation. The Barnes maze task was performed to assess spatial learning and memory in 3, 9, 15, 24, and 28 months old male C57BL/6 mice as well as following voluntary wheel running (VWR) and social isolation (SI) in 20 months old mice. Inflammatory gene expression was analyzed in hippocampal and colonic samples by qPCR. Cognitive decline occurs in mice between 15 and 24 months of age. VWR improved cognitive functions while SI had negative effects. Expression of inflammatory markers changed during aging in the hippocampus ( Il1a / Il6 / S100b / Iba1 / Adgre1 / Cd68 / Itgam ) and colon ( Tnf / Il6 / Il1ra / P2rx7 ). VWR attenuates inflammaging specifically in the colon ( Ifng / Il10 / Ccl2 / S100b / Iba1 ), while SI regulates intestinal Il1b and Gfap . Inflammatory markers in the hippocampus were not altered following VWR and SI. The main finding of our study is that both the hippocampus and colon exhibit an increase in inflammatory markers during aging, and that voluntary wheel running in old age exclusively attenuates intestinal inflammation. Based on the existence of the gut-brain axis, our results extend therapeutic approaches preserving cognitive functions in the elderly to the colon