scholarly journals Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

2018 ◽  
Author(s):  
Adrian I. Campos-González ◽  
Julio A. Freyre-González
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Clustering Coefficient ◽  
Valuable Insight ◽  
Large Set ◽  
Final Size ◽  
Scale Free ◽  
Daunting Task ◽  
Complete Networks

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network is currently complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random. Therefore, attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.

Generalized Boolean Networks

2010 ◽  
pp. 429-449 ◽  
Author(s):  
Christian Darabos ◽  
Mario Giacobini ◽  
Marco Tomassini
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Real Life ◽  
Network Models ◽  
Cell Types ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Point Of View ◽  
Scale Free

Random Boolean Networks (RBN) have been introduced by Kauffman more than thirty years ago as a highly simplified model of genetic regulatory networks. This extremely simple and abstract model has been studied in detail and has been shown capable of extremely interesting dynamical behavior. First of all, as some parameters are varied such as the network’s connectivity, or the probability of expressing a gene, the RBN can go through a phase transition, going from an ordered regime to a chaotic one. Kauffman’s suggestion is that cell types correspond to attractors in the RBN phase space, and only those attractors that are short and stable under perturbations will be of biological interest. Thus, according to Kauffman, RBN lying at the edge between the ordered phase and the chaotic phase can be seen as abstract models of genetic regulatory networks. The original view of Kauffman, namely that these models may be useful for understanding real-life cell regulatory networks, is still valid, provided that the model is updated to take into account present knowledge about the topology of real gene regulatory networks, and the timing of events, without loosing its attractive simplicity. According to present data, many biological networks, including genetic regulatory networks, seem, in fact, to be of the scale-free type. From the point of view of the timing of events, standard RBN update their state synchronously. This assumption is open to discussion when dealing with biologically plausible networks. In particular, for genetic regulatory networks, this is certainly not the case: genes seem to be expressed in different parts of the network at different times, according to a strict sequence, which depends on the particular network under study. The expression of a gene depends on several transcription factors, the synthesis of which appear to be neither fully synchronous nor instantaneous. Therefore, we have recently proposed a new, more biologically plausible model. It assumes a scale-free topology of the networks and we define a suitable semi-synchronous dynamics that better captures the presence of an activation sequence of genes linked to the topological properties of the network. By simulating statistical ensembles of networks, we discuss the attractors of the dynamics, showing that they are compatible with theoretical biological network models. Moreover, the model demonstrates interesting scaling abilities as the size of the networks is increased.

scholarly journals Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization

2020 ◽  
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Regulatory Networks ◽  
Sampling Methods ◽  
Streptomyces Coelicolor ◽  
System Level ◽  
Valuable Insight ◽  
Regulatory Interactions ◽  
Network Analyses ◽  
Daunting Task ◽  
Novel Model ◽  
Different Sources

AbstractSome organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species (Mycobacterium tuberculosis, Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pyogenes, Streptococcus pneumoniae, and Streptomyces coelicolor), containing 8,459 regulons and 4,335 modules.Database URLhttps://abasy.ccg.unam.mx/

scholarly journals Evolution in the Debian GNU/Linux software network: analogies and differences with gene regulatory networks

2020 ◽  
Vol 17 (163) ◽  
pp. 20190845
Author(s):  
Pablo Villegas ◽  
Miguel A. Muñoz ◽  
Juan A. Bonachela
Keyword(s):  
Information Processing ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Degree Distribution ◽  
Regulatory Networks ◽  
Average Distance ◽  
Scale Free ◽  
Software Network ◽  
Gene Regulatory ◽  
Over Time

Biological networks exhibit intricate architectures deemed to be crucial for their functionality. In particular, gene regulatory networks, which play a key role in information processing in the cell, display non-trivial architectural features such as scale-free degree distributions, high modularity and low average distance between connected genes. Such networks result from complex evolutionary and adaptive processes difficult to track down empirically. On the other hand, there exists detailed information on the developmental (or evolutionary) stages of open-software networks that result from self-organized growth across versions. Here, we study the evolution of the Debian GNU/Linux software network, focusing on the changes of key structural and statistical features over time. Our results show that evolution has led to a network structure in which the out-degree distribution is scale-free and the in-degree distribution is a stretched exponential. In addition, while modularity, directionality of information flow, and average distance between elements grew, vulnerability decreased over time. These features resemble closely those currently shown by gene regulatory networks, suggesting the existence of common adaptive pathways for the architectural design of information-processing networks. Differences in other hierarchical aspects point to system-specific solutions to similar evolutionary challenges.

DReSS: a method to quantitatively describe the influence of structural perturbations on state spaces of genetic regulatory networks

2020 ◽  
Author(s):  
Ziqiao Yin ◽  
Binghui Guo ◽  
Shuangge Ma ◽  
Yifan Sun ◽  
Zhilong Mi ◽  
...  
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Random Networks ◽  
Graph Distance ◽  
State Spaces ◽  
Biological Regulatory Networks ◽  
Structural Perturbations ◽  
The Relationship ◽  
Index Family

Abstract Structures of genetic regulatory networks are not fixed. These structural perturbations can cause changes to the reachability of systems’ state spaces. As system structures are related to genotypes and state spaces are related to phenotypes, it is important to study the relationship between structures and state spaces. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. Therefore, Difference in Reachability between State Spaces (DReSS) is proposed. DReSS index family can quantitively describe differences of reachability, attractor sets between two state spaces and can help find the key structure in a system, which may influence system’s state space significantly. First, basic properties of DReSS including non-negativity, symmetry and subadditivity are proved. Then, typical examples are shown to explain the meaning of DReSS and the differences between DReSS and traditional graph distance. Finally, differences of DReSS distribution between real biological regulatory networks and random networks are compared. Results show most structural perturbations in biological networks tend to affect reachability inside and between attractor basins rather than to affect attractor set itself when compared with random networks, which illustrates that most genotype differences tend to influence the proportion of different phenotypes and only a few ones can create new phenotypes. DReSS can provide researchers with a new insight to study the relation between genotypes and phenotypes.

scholarly journals Hierarchical Coordinate Systems for Understanding Complexity and its Evolution, with Applications to Genetic Regulatory Networks

2008 ◽  
Vol 14 (3) ◽  
pp. 299-312 ◽  
Author(s):  
Attila Egri-Nagy ◽  
Chrystopher L. Nehaniv
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Complexity Analysis ◽  
Structural Complexity ◽  
Evolutionary Process ◽  
Genetic Regulatory Networks ◽  
Lac Operon ◽  
Complexity Measures ◽  
Evolution Of Complexity ◽  
Hierarchical Complexity

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

scholarly journals The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks

2010 ◽  
Vol 267 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Sam F. Greenbury ◽  
Iain G. Johnston ◽  
Matthew A. Smith ◽  
Jonathan P.K. Doye ◽  
Ard A. Louis
Keyword(s):  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Scale Free

scholarly journals The Age of Analog Networks

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 63 ◽  
Author(s):  
Claudio Mattiussi ◽  
Daniel Marbach ◽  
Peter Dürr ◽  
Dario Floreano
Keyword(s):  
Reverse Engineering ◽  
Control Systems ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Metabolic Networks ◽  
Temporal Dynamics ◽  
Genetic Regulatory Networks ◽  
Nonlinear Feedback ◽  
Electronic Circuits ◽  
Genetic Encoding

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems which include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and the reverse engineering of biological networks.

scholarly journals Rank-based edge reconstruction for scale-free genetic regulatory networks

2008 ◽  
Vol 9 (1) ◽  
pp. 75 ◽  
Author(s):  
Guanrao Chen ◽  
Peter Larsen ◽  
Eyad Almasri ◽  
Yang Dai
Keyword(s):  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Scale Free ◽  
Edge Reconstruction

scholarly journals On the inherent competition between valid and spurious inductive inferences in Boolean data

2017 ◽  
Vol 28 (12) ◽  
pp. 1750146
Author(s):  
M. Andrecut
Keyword(s):  
Normal Form ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Inductive Inference ◽  
Null Model ◽  
Genetic Regulatory Networks ◽  
Observation Data ◽  
Inference Rules ◽  
Data Set ◽  
Response Variable

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

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