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  1. eLife

    Evaluation Summary:

    In this paper, the authors identify topological metrics in gene-regulatory networks that potentially predict the kinds of phenotypic steady-states that the network allows. In particular, they apply their results to the epithelial-mesenchymal plasticity, showing that the relevant gene regulatory networks are structured as ‘teams' that may be 'strong', yielding stable phenotypes, or 'weak', yielding unstable phenotypes prone to plasticity. The work would be of interest to researchers interested in systems biology and the nonlinear dynamics of biological systems, as well as biologists interested in gene regulatory networks and their (mis)functioning in cancer cells.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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  2. eLife

    Reviewer #1 (Public Review):

    The present manuscript investigates an essential aspect of multicellularity, coordination between gene regulatory networks (GRNs), presented as grouped in so-called teams, and their deregulations in cancer, leading in particular in cancer to epithelial-mesenchymal plasticity (EMP), the propension of plastic cancer cells to go towards an epithelial (MET) or a mesenchymal (EMT) phenotype, according to the needs of the cancer cell population in a given ecosystem.

    The authors have a long experience in molecular systems biology studies and publications in EMP (EMT/MET) and they are well aware of publications in the worldwide community on this topic. The present study proposes a structure of GRNs in 'teams' that may be 'strong', yielding stable phenotypes, or 'weak', yielding unstable phenotypes prone to plasticity.

    The ideas presented in this manuscript, taking advantage of a well-set methodological analysis of gGRNs and their coordination, include fixation on given physiological phenotypes and their stability by such teams, hybrid states, and plastic transitions between states, all based on their newly introduced concept of teams, which might be an illuminating one to understand normal coordination between tissues in multicellular organisms and its deregulation in cancer.

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  3. eLife

    Reviewer #2 (Public Review):

    In this paper, the authors identify topological metrics in gene-regulatory networks that have the potential to predict the sub-types of phenotypic stead states that the network can lead to. The results hold great value for the field of Theoretical Systems Biology.

    The paper becomes too technical too quickly and assumes a lot of knowledge from the reader. Equations and theoretical concepts are not always well defined. In general, I would recommend connecting the results from the simulations/topology metrics to EMP biology earlier in the paper. Alternatively, rather than investigating 5 networks related to EMP, the generalization of the statements could become stronger if the authors explore the trends of the theoretical analysis in networks modeling other biological processes (such as SCLC).

    One of the main findings of the paper is that the distance between the matrix of correlation values between nodes in all steady states obtained from simulation and influence matrix indicates that the mean group strength is a good quantity to identify teams of nodes in the network. However, it remains unclear how to identify groups/teams in the networks based on influence: is it unsupervised (hierarchical?) clustering? How do the authors identify the number of teams of nodes in randomized?

    The authors also explore whether team structure correlates with the stability of relevant biological phenotypes. To characterize stability, they define static (e.g., frustration and stead state frequency) and dynamic network metrics (e.g., coherence and higher-order perturbations), and correlate them to the mean group strength in both WT and randomized networks. Results are promising: team structure and group mean strength show interesting correlative trends with both the static and dynamic metrics. However, everything relies on the mean group strength, which as mentioned before is not convincingly defined in randomized networks.

    Taken together, the conclusions of this paper would be better supported if a better explanation of team identification in gene-regulatory networks would be provided, and if networks related to other biological processes would be investigated.

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  4. Version 1 published on bioRxiv