Machado Rodriguez, G. | Search bioRxiv

bioRxiv2022

Study and Modeling of Biological Noise-Filtering Properties of Conserved Gene Regulatory Networks Motifs in Animal Development

Biological noise results from heterogeneous gene expression levels among a group of cells [1]. This heterogeneity is due to the variation in gene expression that occurs over time at the single-cell level. Some noise-filtering mechanisms like redundancy in genetic circuits have been identified. Likewise, the feed-forward loop network motif has been found to have noise-filtering capacities in animal development. On the other hand, previous studies have contradictory conclusions about the noise-filtering capacities of the feedback loop and none of them have studied this capacity in the activator-inhibitor regulatory system. Here we studied some dynamical properties, such as noise and expression levels, in self-activated and activator-inhibitor regulatory systems, both at the unicellular and multicellular levels. These systems are essential in the self-patterning and community effect processes occurring in development and differentiation. We used the three-stage model to represent the expression of a gene with promoter regulation and Hill functions to represent the regulatory connections between genes. We used Gillespies Algorithm and the Chemical Langevin Equation for simulations. The regulatory systems evaluated do not reduce the biological noise. On the contrary, the noise remains at the same level or increases in comparison with an unregulated gene. The noise levels in these systems depend on the gene expression type of both the regulator and the regulated gene. In this way, the particular forms in which genes connect to each other in these regulatory systems do not explain the noise in expression. However, the noise has a propagation pattern different for activation and inactivation types of regulation. Finally, the diffusion and colony size could be mechanisms of noise filtering in gene expression in a colony of cells. The increase in diffusion rate and colony size are necessary to synchronize gene expression and perform the community effect in embryonic development.

systems biology↗

bioRxiv2021

Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns

BackgroundA central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cells gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. MethodsIn this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. ResultsOut of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly. Author summarySystems biology is a fast growing field largely powered by advances in high-performance computing and sophisticated mathematical modeling of biological systems. Based on these advances, we are now in a position to mechanistically understand and accurately predict the behavior of complex biological processes, including cell differentiation and spatial pattern formation during embryogenesis. In this article, we use an in silico approach to probe the design space of multi-input, three-node Gene Regulatory Networks (GRNs) capable of generating a striped gene expression pattern in the context of a simplified 1-D morphogenetic field.

systems biology↗