About this Research Topic
As there are many methods for recovering and modeling networks from biological and biomedical data, the field of computational systems biology is growing so rapidly that it is virtually impossible to cover it in its entirety in a single comprehensive yet sufficiently detailed review paper.
Complementary to diverse families of methods, there exists a wide variety of biological datatypes, modalities, and activities - from protein-protein interaction networks to gene regulatory networks to multiscale biological networks, to name just a few. At the same time, the field is evolving at such a fast clip that it is difficult to overestimate the utility and salience of timely and representative review papers for guiding and shaping future research, both methodological and applied.
With this research topic, we welcome sufficiently narrow-focused reviews in the broader field of
computational systems biology, under an umbrella of “building different types of networks from different types of biological and biomedical data”. We envision each review paper as concentrating on a particular network methodology (e.g., Bayesian networks, Markov networks, correlation networks, networks grounded in information theory, graph neural networks) or a particular application (e.g., gene regulatory networks in scRNA-seq data analysis, multivariable flow cytometry data analysis, metabolic networks, cell signaling networks). Adjacent subjects (e.g., causality, simulation of synthetic network data, new criteria for intra-network dependencies, and evaluation of network properties) will also be of interest.
We believe that this research topic will offer a useful compendium of starting points (and corresponding critical assessments) for investigators who are interested in the established and novel network-centered approaches to computational systems biology as an addition (or an alternative) to the more “conventional” statistical, shallow and deep learning methodologies and tools.
Complementary to diverse families of methods, there exists a wide variety of biological datatypes, modalities, and activities - from protein-protein interaction networks to gene regulatory networks to multiscale biological networks, to name just a few. At the same time, the field is evolving at such a fast clip that it is difficult to overestimate the utility and salience of timely and representative review papers for guiding and shaping future research, both methodological and applied.
With this research topic, we welcome sufficiently narrow-focused reviews in the broader field of
computational systems biology, under an umbrella of “building different types of networks from different types of biological and biomedical data”. We envision each review paper as concentrating on a particular network methodology (e.g., Bayesian networks, Markov networks, correlation networks, networks grounded in information theory, graph neural networks) or a particular application (e.g., gene regulatory networks in scRNA-seq data analysis, multivariable flow cytometry data analysis, metabolic networks, cell signaling networks). Adjacent subjects (e.g., causality, simulation of synthetic network data, new criteria for intra-network dependencies, and evaluation of network properties) will also be of interest.
We believe that this research topic will offer a useful compendium of starting points (and corresponding critical assessments) for investigators who are interested in the established and novel network-centered approaches to computational systems biology as an addition (or an alternative) to the more “conventional” statistical, shallow and deep learning methodologies and tools.
Keywords: Computational Systems 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.
