Article: The Physics behind Systems Biology[Show abstract] [Hide abstract] ABSTRACT: Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.
- [Show abstract] [Hide abstract] ABSTRACT: Objective An inadequate host response to the intestinal microbiota likely contributes to the manifestation and progression of human inflammatory bowel disease (IBD). However, molecular approaches to unravelling the nature of the defective crosstalk and its consequences for intestinal metabolic and immunological networks are lacking. We assessed the mucosal transcript levels, splicing architecture and mucosa-attached microbial communities of patients with IBD to obtain a comprehensive view of the underlying, hitherto poorly characterised interactions, and how these are altered in IBD. Design Mucosal biopsies from Crohn's disease and patients with UC, disease controls and healthy individuals (n=63) were subjected to microbiome, transcriptome and splicing analysis, employing next-generation sequencing. The three data levels were integrated by different bioinformatic approaches, including systems biology-inspired network and pathway analysis. Results Microbiota, host transcript levels and host splicing patterns were influenced most strongly by tissue differences, followed by the effect of inflammation. Both factors point towards a substantial disease-related alteration of metabolic processes. We also observed a strong enrichment of splicing events in inflamed tissues, accompanied by an alteration of the mucosa-attached bacterial taxa. Finally, we noted a striking uncoupling of the three molecular entities when moving from healthy individuals via disease controls to patients with IBD. Conclusions Our results provide strong evidence that the interplay between microbiome and host transcriptome, which normally characterises a state of intestinal homeostasis, is drastically perturbed in Crohn's disease and UC. Consequently, integrating multiple OMICs levels appears to be a promising approach to further disentangle the complexity of IBD.
- [Show abstract] [Hide abstract] ABSTRACT: Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.
- [Show abstract] [Hide abstract] ABSTRACT: Background Measuring the agreement between a gene expression profile and a known transcriptional regulatory network is an important step in the functional interpretation of bacterial physiological state. In this way, general design principles can be explored. One such interpretive framework is the relationship of digital control, that is, the impact of sequence-specific interactions, and analog control, i.e., the extent of the influence of chromosomal structure. Methods and ResultsHere, we present time-resolved gene expression profiles of Escherichia coli’s growth cycle as measured by RNA-seq. We extend methods which have been developed for discrete sets of differentially expressed genes and apply them to the wild type and two mutant time-series for which the global transcriptional regulators fis and hns were inactivated. We test our continuous methods using simulated ‘expression profiles’ generated from random Boolean network dynamics where we observe a clear trade-off between maximum response and level of detail included. In the real time-course expression data, we find strong interdependent changes of digital and analog control during the exponential growth phase and a dominance of analog control during the stationary phase. Conclusions Our investigation puts forward a simple and reliable method for quantifying the match between time-resolved gene expression profiles and a transcriptional regulatory network. The method reveals a systematic compensatory interplay of digital and analog control in the genetic regulation of E. coli’s growth cycle.
- [Show abstract] [Hide abstract] ABSTRACT: To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co-activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric-coordinates layout and a multi-dimensional scaling approach allow for the analysis of excitation ratio and co-activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.
- [Show abstract] [Hide abstract] ABSTRACT: The term ‘synchronization’ in manufacturing refers to the provision of the right components to the subsequent production steps at the right moment in time. It is widely assumed that synchronization is beneficial to the logistics performance of manufacturing systems. However, it has been shown that synchronization phenomena can be detrimental to systems in which they emerge. To study if synchronization phenomena also occur in and affect manufacturing systems’ performance, a formal quantification and holistic understanding of the types of synchronization phenomena emerging in manufacturing are needed. This article aims to fill this research gap by developing synchronization measures for manufacturing systems, applying these measures to real-world production feedback data and utilising them to test the assumption about synchronization’s beneficial effect on logistics performance. We identify two distinct synchronization types occurring in manufacturing systems, logistics and physics synchronization, and show that they are negatively correlated. Further, we show that logistics synchronization and due date performance exhibit anti-correlation and thus question the assumption that synchronization leads to higher efficiency in manufacturing systems. This article aids production managers in designing and optimising production systems, and supports further empirical research in production planning and control and production system design.
- [Show abstract] [Hide abstract] ABSTRACT: Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by 'informed non-experts' but at the same time attuned to existing, more sophisticated analysis tools.
- [Show abstract] [Hide abstract] ABSTRACT: RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information. Database URL: www.regulondb.ccg.unam.mx
Data: Supplementary Data
- [Show abstract] [Hide abstract] ABSTRACT: Viele Aspekte der Bioinformatik handeln davon, biologische Situationen in eine symbolische Sprache zu übertragen. In Kap. 1 haben wir mit der Reduktion des linearen Moleküls DNA auf die Abfolge der unterschiedlichen Basen und der Identifikation eines Proteins mit der Abfolge von Aminosäuren entlang des Polypeptidstrangs schon Beispiele für solche Übertragungen kennengelernt. Diese Reduktion der Komplexität realer biologischer Zusammenhänge bildet eine notwendige Voraussetzung für weitere Untersuchungen. Eine wichtige Fragestellung ist dabei, welche biologischen (oder chemischen) Eigenschaften durch diese Übertragung ausgeblendet werden, also einer Analyse auf der Grundlage der Symbolsequenz nicht mehr zugänglich sind, und - umgekehrt - welche biologischen Eigenschaften in der Symbolsequenz gerade einfacher diskutiert werden können.
- [Show abstract] [Hide abstract] ABSTRACT: Im vorangegangenen Kapitel haben wir mit Methoden der Informationstheorie eine Eigenschaft eukaryotischer DNA-Sequenzen auf der Skala ganzer Chromosomen sichtbar gemacht, die sehr fundamental scheint: Die statistischen Korrelationen zwischen den Symbolen einer solchen Sequenz klingen unerwartet langsam mit dem Symbolabstand ab. Es gibt viele Vermutungen über die Ursache dieser langreichweitigen Korrelationen. So wird unter anderem vermutet, dass sie (zumindest zum Teil) eine Folge der DNA-Struktur sind oder mit mobilen Elementen oder Mikrosatelliten (und damit mit Aspekten der Genomevolution) in Verbindung gebracht werden können.
Chapter: Skizze des Fachs[Show abstract] [Hide abstract] ABSTRACT: Aus unserer Sicht hat die Bioinformatik die Aufgabe, mathematische Methoden und Algorithmen für die Analyse von DNA- und Proteinsequenzen bereitzustellen, ebenso für die Untersuchung von aus solchen Sequenzdaten abgeleiteter biologischer Information (etwa die dreidimensionalen Strukturen der beteiligten Makromoleküle oder ihre Vernetzung durch Interaktionen und biochemische Reaktionen). Produkte der Bioinformatik sind folglich mathematische Aussagen über allgemeine Zusammenhänge dieser Gegenstände (z.B. Sequenz - Struktur) und Software, die den Anwendern in der Biologie solche im Rahmen der Bioinformatik entwickelten Algorithmen zur Verfügung stellt. Diese Vorstellung von Bioinformatik werden wir im Verlaufe des Buchs ausführlich in einer Vielzahl von Fallbeispielen darstellen.
Chapter: Praktische Bioinformatik[Show abstract] [Hide abstract] ABSTRACT: In Kap. 2 bildeten die unbehandelten, elementaren Sequenzdaten den Ausgangspunkt und es wurde versucht, Schritt für Schritt mit immer fortgeschritteneren mathematischen Methoden Informationen aus diesen Daten zu extrahieren. In diesem Kapitel kehrt sich diese Blickrichtung um. Nun werden wir vielfach etablierte Analysemethoden und Betrachtungsweisen in den Vordergrund stellen, um dann hinter die Kulissen dieser Methoden zu schauen und die dort wirksamen mathematischen Verfahren zu entdecken und zu verstehen. Den Anfang bilden Verfahren des Sequenzvergleichs, gefolgt von phylogenetischen Analysen, die ähnliche Sequenzsegmente aufgrund der quantifizierten Unterschiede zwischen den Segmenten in einen kausalen Zusammenhang bringen. Am Ende steht eine Diskussion bioinformatischer Datenbanken.
- [Show abstract] [Hide abstract] ABSTRACT: Die formalen Übersetzungsprozesse der Proteinbiosynthese, nämlich DNA → RNA und RNA → Protein, sowie einige Aspekte des zugrunde liegenden komplexen biochemischen Apparats haben wir in Kap. 1 diskutiert.Mit der Proteinsequenz sind wesentliche Aspekte der Proteinstruktur und in einem gewissen Rahmen auch der Funktion des Proteins festgelegt. Neben diesem lokalen Blick auf eine DNA-Sequenz, der letztlich in diese Fragen der Proteinstruktur und -funktion mündet, gibt es noch eine globale statistische Perspektive auf DNA-Sequenzen, der wir in diesem Kapitel nachgehen wollen.
- [Show abstract] [Hide abstract] ABSTRACT: Elementary flux modes (EFMs) are a concept from Systems Biology, where they serve as an indicator of component relevance in metabolic networks. An elementary flux mode is a functionally relevant, non-decomposable path through a given network. In this paper, we apply elementary flux mode analysis to manufacturing systems, with the aim of using the number of EFMs as a predictor for resource significance in the manufacturing system. For this, we formulate a network representation of a manufacturing process, which allows us to define the manufacturing equivalent of a stoichiometric matrix to draw an analogy between metabolic and manufacturing systems. This, in turn, allows the computation of EFMs, which we conduct in a case-study for a real manufacturing system. We further show that the change of EFMs under resource breakdown is a good indicator of the average order lateness in the manufacturing system. In this way, EFMs provide insight into the relationship of network structure and function in manufacturing.
- [Show abstract] [Hide abstract] ABSTRACT: Author Summary Pattern formation is abundant in nature—from the rich ornaments of sea shells and the diversity of animal coat patterns to the myriad of fractal structures in biology and pattern-forming colonies of bacteria. Particularly fascinating are patterns changing with time, spatiotemporal patterns, like propagating waves and aggregation streams. Bacteria form large branched and nested aggregation-like patterns to immobilize themselves against water flow. The individual amoeba in Dictyostelium discoideum colonies initiates a transition to a collective multicellular state via a quorum-sensing form of communication—a cAMP signal propagating through the community in the form of spiral waves—and the subsequent chemotactic response of the cells leads to branch-like aggregation streams. The theoretical principle underlying most of these spatial and spatiotemporal patterns is self-organization, in which local interactions lead to patterns as large-scale collective”modes” of the system. Over more than half a century, these patterns have been classified and analyzed according to a”physics paradigm,” investigating such questions as how parameters regulate the transitions among patterns, which (types of) interactions lead to such large-scale patterns, and whether there are "critical parameter values" marking the sharp, spontaneous onset of patterns. A fundamental discovery has been that simple local interaction rules can lead to complex large-scale patterns. The specific pattern "layouts" (i.e., their spatial arrangement and their geometric constraints) have received less attention. However, there is a major difference between patterns in physics and chemistry on the one hand and patterns in biology on the other: in biology, patterns often have an important functional role for the biological system and can be considered to be under evolutionary selection. From this perspective, we can expect that individual biological elements exert some control on the emerging patterns. Here we explore spiral wave patterns as a prominent example to illustrate the regulation of spatiotemporal patterns by biological variability. We propose a new approach to studying spatiotemporal data in biology: analyzing the correlation between the spatial distribution of the constituents’ properties and the features of the spatiotemporal pattern. This general concept is illustrated by simulated patterns and experimental data of a model organism of biological pattern formation, the slime mold Dictyostelium discoideum. We introduce patterns starting from Turing (stripe and spot) patterns, together with target waves and spiral waves. The biological relevance of these patterns is illustrated by snapshots from real and theoretical biological systems. The principles of spiral wave formation are first explored in a stylized cellular automaton model and then reproduced in a model of Dictyostelium signaling. The shaping of spatiotemporal patterns by biological variability (i.e., by a spatial distribution of cell-to-cell differences) is demonstrated, focusing on two Dictyostelium models. Building up on this foundation, we then discuss in more detail how the nonlinearities in biological models translate the distribution of cell properties into pattern events, leaving characteristic geometric signatures.
- [Show abstract] [Hide abstract] ABSTRACT: Random walks are one of the best investigated dynamical processes on graphs. A particularly fascinating phenomenon is the scaling relationship of fluctuations $\sigma $ with the average flux $\langle f \rangle $. Here we analyze how network topology and nodes with finite capacity lead to deviations from a simple scaling law $\sigma \sim \langle f \rangle ^\alpha$. Sources of randomness are the random walk itself (internal noise) and the fluctuation of the number of walkers (external noise). We obtained exact results for the extreme case of a star network which are indicative of the behavior of large scale systems with a broad degree distribution.The latter are subsequently studied using Monte Carlo simulations. We find that the network heterogeneity amplifies the effects of external noise. By computing the `effective' scaling of each node we show that multiple scaling relationships can coexist in a graph with a heterogeneous degree distribution at an intermediate level of external noise. Finally, we analyze the effect of a finite capacity of nodes for random walkers and find that this also can lead to a heterogeneous scaling of fluctuations.
- [Show abstract] [Hide abstract] ABSTRACT: We study the interplay between correlations, dynamics, and networks for repeated attacks on a socio-economic network. As a model system we consider an insurance scheme against disasters that randomly hit nodes, where a node in need receives support from its network neighbors. The model is motivated by gift giving among the Maasai called Osotua. Survival of nodes under different disaster scenarios (uncorrelated, spatially, temporally and spatio-temporally correlated) and for different network architectures are studied with agent-based numerical simulations. We find that the survival rate of a node depends dramatically on the type of correlation of the disasters: Spatially and spatio-temporally correlated disasters increase the survival rate; purely temporally correlated disasters decrease it. The type of correlation also leads to strong inequality among the surviving nodes. We introduce the concept of disaster masking to explain some of the results of our simulations. We also analyze the subsets of the networks that were activated to provide support after fifty years of random disasters. They show qualitative differences for the different disaster scenarios measured by path length, degree, clustering coefficient, and number of cycles.
- [Show abstract] [Hide abstract] ABSTRACT: The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible -excited -refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.
Universität BremenBremen, Bremen, Germany