scholarly journals Predicting trait phenotypes from knowledge of the topology of gene networks

2021 ◽  
Author(s):  
Andy Beatty ◽  
Christopher R Winkler ◽  
Thomas Hagen ◽  
Mark Cooper
Keyword(s):  
Gene Networks ◽  
Boolean Networks ◽  
Boolean Logic ◽  
Node Degree ◽  
Gene Interactions ◽  
Random Networks ◽  
Metabolic Reaction ◽  
Network Nodes ◽  
Reaction Data ◽  
Average Node Degree

In many fields there is interest in manipulating genes and gene networks to realize improved trait phenotypes. The practicality of doing so, however, requires accepted theory on the properties of gene networks that is well-tested by empirical results. The extension of quantitative genetics to include models that incorporate properties of gene networks expands the long tradition of studying epistasis resulting from gene-gene interactions. Here we consider NK models of gene networks by applying concepts from graph theory and Boolean logic theory, motivated by a desire to model the parameters that influence predictive skill for trait phenotypes under the control of gene networks; N defines the number of graph nodes, the number of genes in the network, and K defines the number of edges per node in the graph, representing the gene-gene interactions. We define and consider the attractor period of an NK network as an emergent trait phenotype for our purposes. A long-standing theoretical treatment of the dynamical properties of random Boolean networks suggests a transition from long to short attractor periods as a function of the average node degree K and the bias probability P in the applied Boolean rules. In this paper we investigate the appropriateness of this theory for predicting trait phenotypes on random and real microorganism networks through numerical simulation. We show that: (i) the transition zone between long and short attractor periods depends on the number of network nodes for random networks; (ii) networks derived from metabolic reaction data on microorganisms also show a transition from long to short attractor periods, but at higher values of the bias probability than in random networks with similar numbers of network nodes and average node degree; (iii) the distribution of phenotypes measured on microorganism networks shows more variation than random networks when the bias probability in the Boolean rules is above 0.75; and (iv) the topological structure of networks built from metabolic reaction data is not random, being best approximated, in a statistical sense, by a lognormal distribution. The implications of these results for predicting trait phenotypes where the genetic architecture of a trait is a gene network are discussed.

Utilizing the average node degree to assess the temporal growth rate of Twitter

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Despoina Antonakaki ◽  
Sotiris Ioannidis ◽  
Paraskevi Fragopoulou
Keyword(s):  
Growth Rate ◽  
Node Degree ◽  
Average Node Degree ◽  
Temporal Growth Rate

scholarly journals DReSS: A difference measurement based on reachability between state spaces of Boolean networks

2020 ◽  
Author(s):  
Ziqiao Yin ◽  
Binghui Guo ◽  
Shuangge Steven Ma ◽  
Yifan Sun ◽  
Zhilong Mi ◽  
...  
Keyword(s):  
State Space ◽  
Regulatory Network ◽  
Biological Systems ◽  
Boolean Networks ◽  
Random Networks ◽  
Biological Factors ◽  
Related Factors ◽  
State Spaces ◽  
Structural Perturbations ◽  
Dynamical Features

AbstractResearches on dynamical features of biological systems are mostly based on fixed network structure. However, both biological factors and data factors can cause structural perturbations to biological regulatory networks. There are researches focus on the influence of such structural perturbations to the systems’ dynamical features. Reachability is one of the most important dynamical features, which describe whether a state can automatically evolve into another state. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. DReSS, Difference based on Reachability between State Spaces, is proposed in this research to solve this problem. First, basic properties of DReSS such as non-negativity, symmetry and subadditivity are proved based on the definition. And two more indexes, diagDReSS and iDReSS are proposed based on the definition of DReSS. Second, typical examples like DReSS = 0 or 1 are shown to explain the meaning of DReSS family, and the differences between DReSS and traditional graph distance are shown based on the calculation steps of DReSS. Finally, differences of DReSS distribution between real biological regulatory network and random networks are compared. Multiple interaction positions in real biological regulatory network show significant different DReSS value with those in random networks while none of them show significant different diagDReSS value, which illustrates that the structural perturbations tend to affect reachability inside and between attractor basins rather than to affect attractor set itself.Author summaryBoolean network is a kind of networks which is widely used to model biological regulatory systems. There are structural perturbations in biological systems based on both biological factors and data-related factors. We propose a measurement called DReSS to describe the difference between state spaces of Boolean networks, which can be used to evaluate the influence of specific structural perturbations of a network to its state space quantitively. We can use DReSS to detect the sensitive interactions in a regulatory network, where structural perturbations can influence its state space significantly. We proved properties of DReSS including non-negativity, symmetry and subadditivity, and gave examples to explain the meaning of some special DReSS values. Finally, we present an example of using DReSS to detect sensitive vertexes in yeast cell cycle regulatory network. DReSS can provide a new perspective on how different interactions affect the state space of a specific regulatory network differently.

Implications of Gene Networks for Understanding Resilience and Vulnerability in the Kidney Branching Program

Physiology ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 339-347 ◽  
Author(s):  
Rosemary V. Sampogna ◽  
Sanjay K. Nigam
Keyword(s):  
Network Structure ◽  
Microarray Data ◽  
Gene Networks ◽  
Branching Morphogenesis ◽  
In Vitro Studies ◽  
Gene Interactions

Branching morphogenesis in the kidney is tightly regulated. Whereas disruption of certain pathways produces catastrophic effects, numerous instances exist in which mutation of ostensibly key molecules has minimal apparent phenotypic consequence. We suggest how the network structure of gene interactions in the branching program might explain these findings as well as apparant discrepancies between in vivo and in vitro studies. Emerging genetic, cell-biological, and microarray data should help test and/or clarify these ideas.

scholarly journals A Network Topology Control Algorithm Based on Mobile Nodes

2016 ◽  
Vol 12 (10) ◽  
pp. 76
Author(s):  
Huarui Wu ◽  
Li Zhu
Keyword(s):  
Energy Consumption ◽  
Network Topology ◽  
Topology Control ◽  
Control Algorithm ◽  
Network Performance ◽  
Network Connectivity ◽  
Node Degree ◽  
Mobile Nodes ◽  
Network Nodes ◽  
Local Topology

<p style="margin: 0in 0in 10pt;"><span style="font-family: Times New Roman; font-size: small;">Topology control is of great significance to reduce energy consumption of wireless sensor network nodes and prolong network lifetime. Different tasks taken by nodes may lead to node failures and fractures of data transmission links, hence undermining the overall network performance. In response to such problems, this paper presents a network topology control algorithm based on mobile nodes that fully considers node energy, node degree and network connectivity. Furthermore, a topology control model is established to analyze weak network topology areas and carry out local topology refactoring. Finally, a simulation experiment demonstrates that the presented algorithm is advantageous in balanced network energy consumption and network connectivity.</span></p>

scholarly journals Digital clocks: simple Boolean models can quantitatively describe circadian systems

2012 ◽  
Vol 9 (74) ◽  
pp. 2365-2382 ◽  
Author(s):  
Ozgur E. Akman ◽  
Steven Watterson ◽  
Andrew Parton ◽  
Nigel Binns ◽  
Andrew J. Millar ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Computational Models ◽  
Molecular Data ◽  
Biochemical Networks ◽  
Boolean Logic ◽  
Adaptive Behaviour ◽  
Boolean Models ◽  
Endogenous Rhythms ◽  
Logic Models

The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day–night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.

scholarly journals Evaluating the Cascade Effect in Interdependent Networks via Algebraic Connectivity

2015 ◽  
Vol 1 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Sotharith Tauch ◽  
William Liu ◽  
Russel Pears
Keyword(s):  
Network Structure ◽  
Topological Structure ◽  
Laplacian Matrix ◽  
Network Robustness ◽  
Node Degree ◽  
Algebraic Connectivity ◽  
Interdependent Networks ◽  
Cascade Effect ◽  
Underlying Network ◽  
Average Node Degree

Understanding how the underlying network structure and interconnectivity impact on the robustness of the interdependent networks is a major challenge in complex networks studies. There are some existing metrics that can be used to measure network robustness. However, different metrics such as the average node degree interprets different characteristic of network topological structure, especially less metrics have been identified to effectively evaluate the cascade performance in interdependent networks. In this paper, we propose to use a combined Laplacian matrix to model the interdependent networks and their interconnectivity, and then use its algebraic connectivity metric as a measure to evaluate its cascading behavior. Moreover, we have conducted extensive comparative studies among different metrics such as the average node degree, and the proposed algebraic connectivity. We have found that the algebraic connectivity metric can describe more accurate and finer characteristics on topological structure of the interdependent networks than other metrics widely adapted by the existing research studies for evaluating the cascading performance in interdependent networks.

scholarly journals PEAK Predicts Gene Regulatory Network Linkages during Sea Urchin Development with High Sensitivity from Gene Expression Data Alone

2021 ◽  
Author(s):  
Jingyi Zhang ◽  
Farhan Ibrahim ◽  
Doaa Altarawy ◽  
Lenwood S Heath ◽  
Sarah Tulin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Sea Urchin ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Gene Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Expression Data

Abstract BackgroundGene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to predict the entire landscape of gene-to-gene interactions with the potential to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development -- representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes -- is one of the most challenging arenas for GRN prediction. ResultsIn this work, we show that successful GRN predictions for developmental systems from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic net. We test our GRN prediction methodology using two gene expression data sets for the purple sea urchin (S. purpuratus) and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results found a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 76.32%). We also generated 838 novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. ConclusionsGRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

scholarly journals Gene networks under circadian control exhibit diurnal organization in primate organs

2021 ◽  
Author(s):  
Jie Li ◽  
Pengxing Nie ◽  
Christoph Turck ◽  
Guang-Zhong Wang
Keyword(s):  
Gene Networks ◽  
Target Genes ◽  
Expression Patterns ◽  
The Body ◽  
Gene Interactions ◽  
Molecular Clocks ◽  
Circadian Control ◽  
Genes Encoding ◽  
Basic Network ◽  
Significant Disease

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and off-target genes across time. While interactions between these molecular clocks are likely necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8,120) of baboon genes are rhythmically expressed body-wide. In addition, >30% of gene-gene interactions exhibit periodic co-expression patterns, with core circadian genes more cyclically co-expressed than others. Moreover, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, we found that 216 of 313 genes encoding products that interact with SARS-CoV-2 are rhythmically expressed throughout the body. Importantly, more than 80% of SARS-CoV-2 related genes enriched modules are rhythmically expressed, and have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

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