Understanding genetic variation – the value of systems biology

Hütt, Marc‐Thorsten

doi:10.1111/bcp.12266

Cited by 127 publications

(12 citation statements)

References 60 publications

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“…Our perturbation spreading approach might help bridging the gap between theoretical concepts from statistical physics and biological data integration: Integrating diverse biological information into networks, estimating ‘response patterns’ to systemic perturbations and understanding the multiple systemic manifestations of perturbed, pathological states is perceived as the main challenge in systems medicine (see, e.g., Bauer et al 61 ). Concepts from statistical physics of complex networks may be of enormous importance for this line of research 62 , 63 .…”

Section: Discussionmentioning

confidence: 99%

The interdependent network of gene regulation and metabolism is robust where it needs to be

et al. 2017

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Despite being highly interdependent, the major biochemical networks of the living cell—the networks of interacting genes and of metabolic reactions, respectively—have been approached mostly as separate systems so far. Recently, a framework for interdependent networks has emerged in the context of statistical physics. In a first quantitative application of this framework to systems biology, here we study the interdependent network of gene regulation and metabolism for the model organism Escherichia coli in terms of a biologically motivated percolation model. Particularly, we approach the system’s conflicting tasks of reacting rapidly to (internal and external) perturbations, while being robust to minor environmental fluctuations. Considering its response to perturbations that are localized with respect to functional criteria, we find the interdependent system to be sensitive to gene regulatory and protein-level perturbations, yet robust against metabolic changes. We expect this approach to be applicable to a range of other interdependent networks.

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Section: Discussionmentioning

confidence: 99%

The interdependent network of gene regulation and metabolism is robust where it needs to be

et al. 2017

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“…Over the past two decades, new high-throughput technologies have enabled mathematical modelling, statistical analysis and numerical simulation of biological systems with unprecedented detail for each level of description. At the core of systems thinking in Biology is the concept of networks (see, e.g., Barabasi and Oltvai, 2004 ; Barabasi et al, 2011 ; Cowen et al, 2017 ; Hütt, 2014 ). Together with Statistical Physics (e.g., Albert and Barabasi, 2002 ), Systems Biology has been one of the principal drivers behind the development of a theory of complex networks and a rich set of methods for network analysis (Barabasi and Oltvai, 2004 ).…”

Section: Disciplinary Perspectivesmentioning

confidence: 99%

Connectivity and complex systems: learning from a multi-disciplinary perspective

Turnbull

Hütt

Ioannides³

et al. 2018

Appl Netw Sci

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In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a ‘common toolbox’ underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.

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“…In spite of the tremendous progress made in Systems Biology [1][2][3] and the construction of computational models of biological cells, 4,5 we still lack the appropriate understanding of the underlying principles of genetic regulation to predict, for example, the gene expression pattern of a bacterium. Since the beginning of Systems Biology, the investigation of bacterial gene regulation has been an important source of hypotheses about the principles of biological regulation.…”

Section: Introductionmentioning

confidence: 99%

Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation

et al. 2020

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For a long time it has been hypothesized that bacterial gene regulation involves an intricate interplay of the transcriptional regulatory network (TRN) and the spatial organization of genes in the chromosome. Here we explore this hypothesis both on a structural and on a functional level. On the structural level, we study the TRN as a spatially embedded network. On the functional level, we analyze gene expression patterns from a network perspective ("digital control"), as well as from the perspective of the spatial organization of the chromosome ("analog control"). Our structural analysis reveals the outstanding relevance of the symmetry axis defined by the origin (Ori) and terminus (Ter) of replication for the network embedding and, thus, suggests the coevolution of two regulatory infrastructures, namely the transcriptional regulatory network and the spatial arrangement of genes on the chromosome, to optimize the cross-talk between two fundamental biological processes: genomic expression and replication. This observation is confirmed by the functional analysis based on the differential gene expression patterns of more than 4000 pairs of microarray and RNA-Seq datasets for E. coli from the Colombos Database using complex network and machine learning methods. This large-scale analysis supports the notion that two logically distinct types of genetic control are cooperating to regulate gene expression in a complementary manner. Moreover, we find that the position of the gene relative to the Ori is a feature of very high predictive value for gene expression, indicating that the Ori-Ter symmetry axis coordinates the action of distinct genetic control mechanisms.

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Understanding genetic variation – the value of systems biology

Cited by 127 publications

References 60 publications

The interdependent network of gene regulation and metabolism is robust where it needs to be

The interdependent network of gene regulation and metabolism is robust where it needs to be

Connectivity and complex systems: learning from a multi-disciplinary perspective

Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation

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