About this Research Topic
Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science, and computer science (just to name a few) and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions.
We are now in an era of ‘big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at a nation-wide level thus giving us another source of highly related (causal) 'big data'.
This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key viewpoint leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing’.
A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks. In our first volume, Network Bioscience, we reported results on foundational issues, novel algorithms and tools, as well as several applicative scenarios.
In this instalment, we aim to provide a focus for the research community and disseminate the most promising breakthroughs in the area of network science applied to genetics and systems biology. In the past 2 years since our first Research Topic, we are now particularly interested in addressing both foundational and applicative aspects of network bioscience with a specific focus on the challenges arising from the current COVID-19 pandemic, and those likely to be relevant for future similar pandemics.
The sub-topics in this area include but are not limited to:
(1) network-based approaches to modelling medical, biological, genomic and epidemiologic aspects of viral epidemics/pandemics;
(2) New experiments, technologies, algorithms and software for building and analyzing biological networks from 'omics' data, from symptoms, disease and phenotypic data, including integrated pipelines and visualization tools;
(3) Statistical models for biological networks and association studies involving network features and phenotypes;
(4) Algorithms and statistical models for biological networks in genetically heterogeneous samples (e.g. tumor data vs controls, tumor clonal heterogeneity);
(5) Performance evaluation of existing or novel network analysis methods using simulated and experimental genomic data sets;
(6) Statistical models and tools for building biological networks in model and non-model organisms.
(7) Network causality, network dynamics, network evolution, network-based hypothesis generation, network-based interpretation of biological systems;
(8) Formal properties of biological networks (e.g. scale-free, modularity, lethality, redundancy) and their biological interpretation;
(9) Applications of Advanced BioNetwork and AI techniques (e.g. graph neural networks , graphons)
We are now in an era of ‘big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at a nation-wide level thus giving us another source of highly related (causal) 'big data'.
This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key viewpoint leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing’.
A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks. In our first volume, Network Bioscience, we reported results on foundational issues, novel algorithms and tools, as well as several applicative scenarios.
In this instalment, we aim to provide a focus for the research community and disseminate the most promising breakthroughs in the area of network science applied to genetics and systems biology. In the past 2 years since our first Research Topic, we are now particularly interested in addressing both foundational and applicative aspects of network bioscience with a specific focus on the challenges arising from the current COVID-19 pandemic, and those likely to be relevant for future similar pandemics.
The sub-topics in this area include but are not limited to:
(1) network-based approaches to modelling medical, biological, genomic and epidemiologic aspects of viral epidemics/pandemics;
(2) New experiments, technologies, algorithms and software for building and analyzing biological networks from 'omics' data, from symptoms, disease and phenotypic data, including integrated pipelines and visualization tools;
(3) Statistical models for biological networks and association studies involving network features and phenotypes;
(4) Algorithms and statistical models for biological networks in genetically heterogeneous samples (e.g. tumor data vs controls, tumor clonal heterogeneity);
(5) Performance evaluation of existing or novel network analysis methods using simulated and experimental genomic data sets;
(6) Statistical models and tools for building biological networks in model and non-model organisms.
(7) Network causality, network dynamics, network evolution, network-based hypothesis generation, network-based interpretation of biological systems;
(8) Formal properties of biological networks (e.g. scale-free, modularity, lethality, redundancy) and their biological interpretation;
(9) Applications of Advanced BioNetwork and AI techniques (e.g. graph neural networks , graphons)
Keywords: systems biology, network science, cancer networks, hypothesis generation, Regularization, graph neural networks, network biology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
