Biology subjects

Rocha, L. M.

Publications and source records attributed to Rocha, L. M..

3 recordsLinked to original sources

bioRxiv2023

Robustness of biomolecular networks suggests functional modules far from the edge of chaos

A common feature of complex systems is their ability to balance the flexibility needed to adapt to their environment with the rigidity required for robust function. It has been conjectured that living systems accomplish this by existing at the "edge of chaos", i.e., the critical boundary between ordered and disordered dynamics. Simple toy models of gene regulatory networks lend support to this idea, and mathematical tools developed for these toy models yield similar results when applied to experimentally-supported models of specific cellular regulatory mechanisms (functional modules). Here, however, we demonstrate that a deeper inspection of 72 experimentally-supported discrete dynamical models of functional modules reveals previously unobserved order in these systems on long time scales, suggesting greater rigidity in these systems than was previously conjectured. Our analysis relies on new measures that quantify the tendency of perturbations to spread through a discrete dynamical system. A benefit of our new approach is that it accounts for how system trajectories are mapped to phenotypes in practice. Because these measures are computationally expensive to estimate, existing tools were insufficient for the ensemble of models considered here. To simulate the tens of millions of trajectories required for convergence, we developed a multipurpose CUDA-based simulation tool, which we have made available as the open-source Python library cubewalkers. We find that in experimentally-supported models of biomolecular functional modules, perturbation propagation is more transitory than previously thought, and that even in cases where large perturbation cascades persist, their phenotypic effects are often minimal. Moreover, by examining the impact of update scheme on experimentally-supported models, we find evidence that stochasticity and desynchronization can lead to increased recovery from regulatory perturbation cascades in functional modules and uncover previously unreported population-level robustness to even timing perturbations in these systems. We identify specific biological mechanisms underlying these dynamical behaviors and highlight them in experimentally-supported regulatory networks from the systems biology literature. Based on novel measures and simulations, our results suggest that-contrary to current theory-functional modules of biological systems are ordered and far from the edge of chaos.

systems biology↗

bioRxiv2022

The conserved transcriptional program of metazoan male germ cells uncovers ancient origins of human infertility

Male germ cells share a common origin across animal species, therefore they likely retain a conserved genetic program that defines their cellular identity. However, the unique evolutionary dynamics of male germ cells coupled with their widespread leaky transcription pose significant obstacles to the identification of the core spermatogenic program. Through network analysis of the spermatocyte transcriptome of vertebrate and invertebrate species, we describe the conserved evolutionary origin of metazoan male germ cells at the molecular level. We estimate the average functional requirement of a metazoan male germ cell to correspond to the expression of approximately 10,000 protein-coding genes, a third of which defines a genetic scaffold of deeply conserved genes that has been retained throughout evolution. Such scaffold contains a set of 79 functional associations between 104 gene expression regulators that represent a core component of the conserved genetic program of metazoan spermatogenesis. By genetically interfering with the acquisition and maintenance of male germ cell identity, we uncover 161 previously unknown spermatogenesis genes and three new potential genetic causes of human infertility. These findings emphasize the importance of evolutionary history on human reproductive disease and establish a cross-species analytical pipeline that can be repurposed to other cell types and pathologies.

systems biology↗

bioRxiv2022

The metric backbone preserves community structure and is a primary transmission subgraph in contact networks

The structure of social networks strongly affects how different phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its effect on the robustness of transmission is still lacking. This gap is addressed by the metric backbone, a weight- and connectivity-preserving subgraph that is sufficient to compute all shortest paths of weighted graphs. This subgraph is obtained via algebraically-principled axioms and does not require statistical sampling based on null-models. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We also show that the metric backbone preserves the full distribution of shortest paths of the original contact networks--which must include the shortest inter- and intra-community distances that define any community structure--and is a primary subgraph for epidemic transmission based on pure diffusion processes. This suggests that the organization of social contact networks is based on large amounts of shortest-path redundancy which shapes epidemic spread in human populations. Thus, the metric backbone is an important subgraph with regard to epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths. Author summaryIt is through social networks that contagious diseases spread in human populations, as best illustrated by the current pandemic and efforts to contain it. Measuring such networks from human contact data typically results in noisy and dense graphs that need to be simplified for effective analysis, without removal of their essential features. Thus, the identification of a primary subgraph that maintains the social interaction structure and likely transmission pathways is of relevance for studying epidemic spreading phenomena as well as devising intervention strategies to hinder spread. Here we propose and study the metric backbone as an optimal subgraph for sparsification of social contact networks in the study of simple spreading dynamics. We demonstrate that it is a unique, algebraically-principled network subgraph that preserves all shortest paths. We also discover that nine contact networks obtained from proximity sensors in a variety of social contexts contain large amounts of redundant interactions that can be removed with very little impact on community structure and epidemic spread. This reveals that epidemic spread on social networks is very robust to random interaction removal. However, extraction of the metric backbone subgraph reveals which interventions--strategic removal of specific social interactions--are likely to result in maximum impediment to epidemic spread.

systems biology↗