scholarly journals Shifting levels of ecological network's analysis reveals different system properties

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190326 ◽  
Author(s):  
Nathalie Niquil ◽  
Matilda Haraldsson ◽  
Télesphore Sime-Ngando ◽  
Philippe Huneman ◽  
Stuart R. Borrett
Keyword(s):  
Biological Networks ◽  
General Pattern ◽  
Network Models ◽  
Time Step ◽  
Philosophical Foundations ◽  
Network Analyses ◽  
Planktonic Food ◽  
Node Level ◽  
System Properties ◽  
Level Of Analysis

Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but consider individual nodes. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

scholarly journals Network explanations and explanatory directionality

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190318 ◽  
Author(s):  
Lina Jansson
Keyword(s):  
Biological Networks ◽  
Scientific Explanation ◽  
Network Models ◽  
Causal Models ◽  
Topological Properties ◽  
Theme Issue ◽  
Causal Explanations ◽  
Philosophical Foundations ◽  
Direction Of Causation

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Evolving complexity: how tinkering shapes cells, software and ecological networks

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190325 ◽  
Author(s):  
Ricard Solé ◽  
Sergi Valverde
Keyword(s):  
Biological Networks ◽  
Network Models ◽  
Scale Free ◽  
Philosophical Foundations ◽  
Evolutionary Origins ◽  
Proper Formulation ◽  
Modular Architectures ◽  
The Individual ◽  
Insight Into ◽  
Selection Of

A common trait of complex systems is that they can be represented by means of a network of interacting parts. It is, in fact, the network organization (more than the parts) that largely conditions most higher-level properties, which are not reducible to the properties of the individual parts. Can the topological organization of these webs provide some insight into their evolutionary origins? Both biological and artificial networks share some common architectural traits. They are often heterogeneous and sparse, and most exhibit different types of correlations, such as nestedness, modularity or hierarchical patterns. These properties have often been attributed to the selection of functionally meaningful traits. However, a proper formulation of generative network models suggests a rather different picture. Against the standard selection–optimization argument, some networks reveal the inevitable generation of complex patterns resulting from reuse and can be modelled using duplication–rewiring rules lacking functionality. These give rise to the observed heterogeneous, scale-free and modular architectures. Here, we examine the evidence for tinkering in cellular, technological and ecological webs and its impact in shaping their architecture. Our analysis suggests a serious consideration of the role played by selection as the origin of network topology. Instead, we suggest that the amplification processes associated with reuse might shape these graphs at the topological level. In biological systems, selection forces would take advantage of emergent patterns. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Exploring modularity in biological networks

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190316 ◽  
Author(s):  
Maria Serban
Keyword(s):  
Biological Networks ◽  
Network Models ◽  
Exploratory Research ◽  
Systematic Analysis ◽  
Philosophical Foundations ◽  
Theoretical Approaches ◽  
New Directions ◽  
Biology I ◽  
Complementary Set

Network theoretical approaches have shaped our understanding of many different kinds of biological modularity. This essay makes the case that to capture these contributions, it is useful to think about the role of network models in exploratory research. The overall point is that it is possible to provide a systematic analysis of the exploratory functions of network models in bioscientific research. Using two examples from molecular and developmental biology, I argue that often the same modelling approach can perform one or more exploratory functions, such as introducing new directions of research, offering a complementary set of concepts, methods and algorithms for individuating important features of natural phenomena, generating proofs of principle demonstrations and potential explanations for phenomena of interest and enlarging the scope of certain research agendas. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals ‘Hierarchy’ in the organization of brain networks

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190319 ◽  
Author(s):  
Claus C. Hilgetag ◽  
Alexandros Goulas
Keyword(s):  
Biological Networks ◽  
Brain Networks ◽  
Theme Issue ◽  
Philosophical Foundations ◽  
Systems Neuroscience ◽  
Functional Implications ◽  
Different Dimensions ◽  
Cortical System ◽  
Visual Cortical

Concepts shape the interpretation of facts. One of the most popular concepts in systems neuroscience is that of ‘hierarchy’. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Using the example of the organization of the primate visual cortical system, we explore several contexts in which ‘hierarchy’ is currently used in the description of brain networks. We distinguish at least four different uses, specifically, ‘hierarchy’ as a topological sequence of projections, as a gradient of features, as a progression of scales, or as a sorting of laminar projection patterns. We discuss the interpretation and functional implications of the different notions of ‘hierarchy’ in these contexts and suggest that more specific terms than ‘hierarchy’ should be used for a deeper understanding of the different dimensions of the organization of brain networks. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals SOM-based aggregation for graph convolutional neural networks

2020 ◽  
Author(s):  
Luca Pasa ◽  
Nicolò Navarin ◽  
Alessandro Sperduti
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Predictive Performance ◽  
Aggregation Operator ◽  
Graph Representation ◽  
Approximation Properties ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Node Level ◽  
Real World Datasets

AbstractGraph property prediction is becoming more and more popular due to the increasing availability of scientific and social data naturally represented in a graph form. Because of that, many researchers are focusing on the development of improved graph neural network models. One of the main components of a graph neural network is the aggregation operator, needed to generate a graph-level representation from a set of node-level embeddings. The aggregation operator is critical since it should, in principle, provide a representation of the graph that is isomorphism invariant, i.e. the graph representation should be a function of graph nodes treated as a set. DeepSets (in: Advances in neural information processing systems, pp 3391–3401, 2017) provides a framework to construct a set-aggregation operator with universal approximation properties. In this paper, we propose a DeepSets aggregation operator, based on Self-Organizing Maps (SOM), to transform a set of node-level representations into a single graph-level one. The adoption of SOMs allows to compute node representations that embed the information about their mutual similarity. Experimental results on several real-world datasets show that our proposed approach achieves improved predictive performance compared to the commonly adopted sum aggregation and many state-of-the-art graph neural network architectures in the literature.

scholarly journals Network architectures supporting learnability

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190323 ◽  
Author(s):  
Perry Zurn ◽  
Danielle S. Bassett
Keyword(s):  
Biological Networks ◽  
Statistical Physics ◽  
Knowledge Network ◽  
Network Architectures ◽  
Philosophical Discussion ◽  
Hierarchical Networks ◽  
Integrated Network ◽  
Physical Constraints ◽  
Philosophical Foundations ◽  
Relational Knowledge

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit—the brain—that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human’s knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Analysing brain networks in population neuroscience: a case for the Bayesian philosophy

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190661 ◽  
Author(s):  
Danilo Bzdok ◽  
Dorothea L. Floris ◽  
Andre F. Marquand
Keyword(s):  
Biological Networks ◽  
Network Connectivity ◽  
Autism Spectrum ◽  
Single Subject ◽  
Model Parameters ◽  
Philosophical Foundations ◽  
Probability Estimates ◽  
Rich Information ◽  
Epistemological Uncertainty ◽  
Coupling Phenomena

Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here, we contemplate the advantages of analysing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena, provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner, usher us towards avenues to go beyond binary statements on existence versus non-existence of an effect, and afford credibility estimates around all model parameters at play which thus enable single-subject predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

scholarly journals Echo State Network models for nonlinear Granger causality

2019 ◽  
Author(s):  
Andrea Duggento ◽  
Maria Guerrisi ◽  
Nicola Toschi
Keyword(s):  
Granger Causality ◽  
Biological Networks ◽  
Short Term Memory ◽  
Real Life ◽  
Network Models ◽  
Echo State Networks ◽  
Linear Behavior ◽  
Novel Approach ◽  
Human Connectome Project ◽  
Random Initialization

AbstractWhile Granger Causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable non-linear behavior, hence undermining the validity of MVAR-based GC (MVAR-GC). Current nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks (RNN) or Long short-term memory (LSTM) networks, which present considerable training difficulties and tailoring needs. We define a novel approach to estimating nonlinear, directed within-network interactions through a RNN class termed echo-state networks (ESN), where training is replaced by random initialization of an internal basis based on orthonormal matrices. We reformulate the GC framework in terms of ESN-based models, our ESN-based Granger Causality (ES-GC) estimator in a network of noisy Duffing oscillators, showing a net advantage of ES-GC in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. ES-GC performs better than commonly used and recently developed GC approaches, making it a valuable tool for the analysis of e.g. multivariate biological networks.

scholarly journals Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

2017 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Daniel J H Nightingale ◽  
Valerie Wood ◽  
Kathryn S Lilley ◽  
Stephen G Oliver
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Topological Analysis ◽  
Network Size ◽  
Transcriptional Level ◽  
Transcriptional Regulatory Networks ◽  
Network Function ◽  
Network Analyses ◽  
Transcriptional Regulatory

AbstractThe topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

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