DOCSLIB.ORG

An Automatic Design Framework of Swarm Pattern Formation Based on Multi-Objective Genetic Programming

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Automatic Design Framework of Swarm Pattern Formation Based on Multi-Objective Genetic Programming JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming Zhun Fan, Senior Member, IEEE, Zhaojun Wang, Xiaomin Zhu, Bingliang Hu, Anmin Zou, and Dongwei Bao Abstract—Most existing swarm pattern formation methods predetermined trajectory and maintain a specific swarm pattern depend on a predefined gene regulatory network (GRN) structure in the execution of tasks. For example, Jin [11] proposed a that requires designers’ priori knowledge, which is difficult to hierarchical gene regulatory network for adaptive multi-robot adapt to complex and changeable environments. To dynamically adapt to the complex and changeable environments, we propose pattern formation. In this work, the swarm pattern is designed an automatic design framework of swarm pattern formation as a band of circle, which encircles targets in a dynamic based on multi-objective genetic programming. The proposed environments. In the latter case of using adaptive formation, framework does not need to define the structure of the GRN- swarm robots follow an adaptive pattern to encircle targets in based model in advance, and it applies some basic network motifs the environment. For example, Oh et al. [12] have introduced to automatically structure the GRN-based model. In addition, a multi-objective genetic programming (MOGP) combines with an evolving hierarchical gene regulatory network for morpho- NSGA-II, namely MOGP-NSGA-II, to balance the complexity genetic pattern formation in order to generate adaptive patterns and accuracy of the GRN-based model. In evolutionary process, which are adaptable to dynamic environments. an MOGP-NSGA-II and differential evolution (DE) are applied to In addition, swarm pattern formation has been widely ex- optimize the structures and parameters of the GRN-based model plored in recent years, which can be divided into four cate- in parallel. Simulation results demonstrate that the proposed framework can effectively evolve some novel GRN-based models, gories [1], namely morphogenesis, reaction-diffusion model, and these GRN-based models not only have a simpler structure chemotaxis and gene regulatory network (GRN). and a better performance, but also are robust to the complex The basic idea of morphogenesis is that the morphogen- and changeable environments. like signals can provide the information of relative locations Index Terms—Gene Regulatory Networks (GRN), Swarm for each robot in swarm robots. For example, Mamei [13] Pattern Formations, Self-organization, Multi-objective Genetic has studied swarm robots utilize morphogen diffusion to form Programming (MOGP), Differential Evolution (DE). a circular, ring or polygonal pattern formation. In addition, morphogenesis can be combined with other methods to form I. INTRODUCTION arbitrary swarm patterns. For example, Kondacs [14] combines N general, multi-robot systems (MRSs) are composed of morphology and geometry to generate two-dimensional arbi- I a large number of minimal, simple and low-cost robots, trary swarm patterns. each of which has limited functions and performance [1]. The second category is reaction-diffusion model, which In most cases, these simple robots can work together in a utilizes several morphogen in a cell to react with morphogen collaborative way to accomplish complex and changeable tasks in other neighbouring cells to generate complex patterns [15]. that single robot can not accomplish. In addition, MRSs with For example, the reaction-diffusion turing pattern [16] is a arXiv:1910.14627v2 [cs.NE] 1 Nov 2019 parallelism, scalability, stability, low-cost, strong robustness typical example of reaction-diffusion model, which utilizes and high adaptability, are widely applied into the collaborative two hormones, an activator and an inhibitor, to form complex target search and rescue [2], [3], small satellites communica- and arbitrary swarm patterns. Moreover, Kondo [17] has used tion [4], [5], cooperation and navigation planning [6], [7] and mathematical models to explain the development patterns of source localization [8], [9], et al. Turing model in biological systems. The swarm pattern formation is a typical task for MRSs Without central control or coordination, chemotaxis can and embodies pattern generation and pattern maintenance [10]. control the movement, aggregation and sorting of swarm In different tasks and dynamic environments, swarm pattern robots in pattern formation. For example, Fate and Vlassopou- formation represents the coordination and local interaction of los [18] applied chemotaxis in swarm robots, which forms multi-robots to generate and maintain a swarm pattern forma- aggregation formation in a dynamic environment. Bai and tion with a certain shape, in which the shape of pattern can Breen [19] demonstrated that chemotaxis can form complex be either predefined or adaptively formed in a self-organised, swarm patterns. coordinate and cooperate way through local interaction with The GRN model [20] is inspired by the reaction-diffusion neighbouring robots and the environments [1]. In the former model, which can control the behavior of each robot in case of using a predefined pattern, swarm robots follow a swarm robots. For example, Jin [11] applied an evolutionary algorithm to evolve parameters of a GRN subnetwork, which gets a multi-robot pattern formation. Meng [21] combined JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 2 GRN and B-spline to form complex patterns in swarm robotic automatic design framework of GRN-based model for adaptive systems. Oh [12] utilized some network motifs to evolve GRN, swarm pattern formations is proposed in Section IV. Section V which generates swarm pattern formation. Oh [22] utilized a presents numerical simulation results from moving targets and two-layer hierarchical GRN to cover a desired region for target various obstacles, which verifies the adaptability, scalability entrapment. and robustness of the proposed framework. In Section VI, A substantial limitation of most existing GRN-based model conclusions and future works are discussed. for swarm pattern formation is that the structure of the network must be predefined, resulting in the inability to adapt II. PROBLEM STATEMENT AND ASSUMPTIONS to different tasks and dynamic environments. Furthermore, The studied problem mainly focus on how to generate another limitation of swarm pattern formations is that existing adaptive swarm patterns in dynamic, complex and changeable evolutionary GRN methods are mainly to optimize the param- environments. More specifically, a swarm pattern needs to eters of the network structure, and these methods are unable to form different shapes in various restricted environments. optimize topology structures of a GRN-based model, resulting In biological morphogenesis, morphogen gradients that in inability to adapt to the complex dynamic environments. guide cells migration are either directly obtained from the In order to produce a GRN-based model adapted to the mother cells or generated by a few cells known as histi- complex dynamic environments, we adopt an idea of automatic ocytic cells [15]. Inspired by these biological studies, we design [23]. The automatic design allows modules from ele- assume that there are some mother robots (a mother robot mentary building blocks and operators to automatically gener- is equivalent to a mother cell in biological morphogenesis) ate models to satisfy predefined specifications. The automatic in swarm robots. The mother robots can detect obstacles and design process is that a modular modeling language is applied targets in the environment and they are responsible for swarm to describe elementary building blocks, and then a genetic pattern formation. When the mother robots detecting targets programming (GP) is employed to automatic optimization to (or obstacles and targets) in an environment, the mother robots get an optimal model. When automatic design is applied to can form a morphogen gradient space according to the location electromechanical systems, a modular modeling language is information of the targets (or obstacles and targets). In the bond graph. For example, Erik Goodman [24] proposed an morphogen gradient space, the concentration of morphogen approach combining GP and bond graph to use in electrome- decreases as it moves away form the targets. Points whose chanical system. Jean-Francois Dupuis [25] proposed a hybrid gradient value are higher than a certain percentage of the system evolutionary design method by combining hybrid bond maximum concentration value are selected as candidate points, graph with GP. When automatic design is applied to swarm and swarm robots move to candidate points to form a swarm robots, a modular modeling language is finite-state machine. pattern. For example, Lorenzo Garattoni [26] used finite state machine In order to apply the proposed automatic design framework to control swarm robot, which could collectively sequence of GRN-based model to the swarm pattern formation. Some tasks whose order of execution was a priori unknown. assumptions are listed as follows: To tackle limitations of most existing GRN-based model for swarm pattern formation, we propose an automatic de- 1) The swarm robots can use on-board sensors (such as sign framework of swarm pattern formation based on multi- encoders and sonar sensors) to locate themselves at any objective genetic programming (MOGP). The proposed frame- time, as reported in [27]. work does not need to define the structure
Load more

Recommended publications
  • Dynamical Gene Regulatory Networks Are Tuned by Transcriptional Autoregulation with Microrna Feedback Thomas G
    www.nature.com/scientificreports OPEN Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback Thomas G. Minchington1, Sam Grifths‑Jones2* & Nancy Papalopulu1* Concepts from dynamical systems theory, including multi‑stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, infammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identifed autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non‑autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate diferent combinations of microRNAs in a tissue‑dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identifcation of TFs which regulate the dynamical properties of human gene expression. Cell fate changes are a key feature of development, regeneration and cancer, and are ofen thought of as a “land- scape” that cells move through1,2. Cell fate changes are driven by changes in gene expression: turning genes on or of, or changing their levels above or below a threshold where a cell fate change occurs.
    [Show full text]
  • Systematic Comparison of Sea Urchin and Sea Star Developmental Gene Regulatory Networks Explains How Novelty Is Incorporated in Early Development
    ARTICLE https://doi.org/10.1038/s41467-020-20023-4 OPEN Systematic comparison of sea urchin and sea star developmental gene regulatory networks explains how novelty is incorporated in early development Gregory A. Cary 1,3,5, Brenna S. McCauley1,4,5, Olga Zueva1, Joseph Pattinato1, William Longabaugh2 & ✉ Veronica F. Hinman 1 1234567890():,; The extensive array of morphological diversity among animal taxa represents the product of millions of years of evolution. Morphology is the output of development, therefore phenotypic evolution arises from changes to the topology of the gene regulatory networks (GRNs) that control the highly coordinated process of embryogenesis. A particular challenge in under- standing the origins of animal diversity lies in determining how GRNs incorporate novelty while preserving the overall stability of the network, and hence, embryonic viability. Here we assemble a comprehensive GRN for endomesoderm specification in the sea star from zygote through gastrulation that corresponds to the GRN for sea urchin development of equivalent territories and stages. Comparison of the GRNs identifies how novelty is incorporated in early development. We show how the GRN is resilient to the introduction of a transcription factor, pmar1, the inclusion of which leads to a switch between two stable modes of Delta-Notch signaling. Signaling pathways can function in multiple modes and we propose that GRN changes that lead to switches between modes may be a common evolutionary mechanism for changes in embryogenesis. Our data additionally proposes a model in which evolutionarily conserved network motifs, or kernels, may function throughout development to stabilize these signaling transitions. 1 Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
    [Show full text]
  • Temporal Flexibility of Gene Regulatory Network Underlies a Novel Wing Pattern in Flies
    Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies Héloïse D. Dufoura,b, Shigeyuki Koshikawa (越川滋行)a,b,1,2,3, and Cédric Fineta,b,3,4 aHoward Hughes Medical Institute, University of Wisconsin, Madison, WI 53706; and bLaboratory of Molecular Biology, University of Wisconsin, Madison, WI 53706 Edited by Denis Duboule, University of Geneva, Geneva 4, Switzerland, and approved April 6, 2020 (received for review February 3, 2020) Organisms have evolved endless morphological, physiological, and Nevertheless, it does not explain how the new expression of behavioral novel traits during the course of evolution. Novel traits the coopted toolkit genes does not interfere with the develop- were proposed to evolve mainly by orchestration of preexisting ment of the tissue. Some authors have suggested that the reuse genes. Over the past two decades, biologists have shown that of toolkit genes might only happen during late development after cooption of gene regulatory networks (GRNs) indeed underlies completion of the early function of the redeployed genes (21, 22, numerous evolutionary novelties. However, very little is known 29). However, little is known about the properties of a GRN that about the actual GRN properties that allow such redeployment. allow the cooption of one or several of its components/genes Here we have investigated the generation and evolution of the without impairing the development of the tissue. complex wing pattern of the fly Samoaia leonensis. We show that In this study, we use the complex wing pigmentation pattern of the transcription factor Engrailed is recruited independently from the fly species Samoaia leonensis as a model to address how the the other players of the anterior–posterior specification network to generate a new wing pattern.
    [Show full text]
  • Gene Regulatory Networks
    Gene Regulatory Networks 02-710 Computaonal Genomics Seyoung Kim Transcrip6on Factor Binding Transcrip6on Control • Gene transcrip.on is influenced by – Transcrip.on factor binding affinity for the regulatory regions of target genes – Transcrip.on factor concentraon – Nucleosome posi.oning and chroman states – Enhancer ac.vity Gene Transcrip6onal Regulatory Network • The expression of a gene is controlled by cis and trans regulatory elements – Cis regulatory elements: DNA sequences in the regulatory region of the gene (e.g., TF binding sites) – Trans regulatory elements: RNAs and proteins that interact with the cis regulatory elements Gene Transcrip6onal Regulatory Network • Consider the following regulatory relaonships: Target gene1 Target TF gene2 Target gene3 Cis/Trans Regulatory Elements Binding site: cis Target TF regulatory element TF binding affinity gene1 can influence the TF target gene Target gene2 expression Target TF gene3 TF: trans regulatory element TF concentra6on TF can influence the target gene TF expression Gene Transcrip6onal Regulatory Network • Cis and trans regulatory elements form a complex transcrip.onal regulatory network – Each trans regulatory element (proteins/RNAs) can regulate mul.ple target genes – Cis regulatory modules (CRMs) • Mul.ple different regulators need to be recruited to ini.ate the transcrip.on of a gene • The DNA binding sites of those regulators are clustered in the regulatory region of a gene and form a CRM How Can We Learn Transcriponal Networks? • Leverage allele specific expressions – In diploid organisms, the transcript levels from the two copies of the genes may be different – RNA-seq can capture allele- specific transcript levels How Can We Learn Transcriponal Networks? • Leverage allele specific gene expressions – Teasing out cis/trans regulatory divergence between two species (WiZkopp et al.
    [Show full text]
  • A Computational Model Inspired by Gene Regulatory Networks
    Rui Miguel Lourenço Lopes A Computational Model Inspired by Gene Regulatory Networks Doctoral thesis submitted to the Doctoral Program in Information Science and Technology, supervised by Full Professor Doutor Ernesto Jorge Fernandes Costa, and presented to the Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra. January 2015 A Computational Model Inspired by Gene Regulatory Networks A thesis submitted to the University of Coimbra in partial fulfillment of the requirements for the Doctoral Program in Information Science and Technology by Rui Miguel Lourenço Lopes [email protected] Department of Informatics Engineering Faculty of Sciences and Technology University of Coimbra Coimbra, January 2015 Financial support by Fundação para a Ciência e a Tecnologia, through the PhD grant SFRH/BD/69106/2010. A Computational Model Inspired by Gene Regulatory Networks ©2015 Rui L. Lopes ISBN 978-989-20-5460-5 Cover image: Composition of a Santa Fe trail program evolved by ReNCoDe overlaid on the ancestors, with a 3D model of the DNA crystal structure (by Paul Hakimata). This dissertation was prepared under the supervision of Ernesto J. F. Costa Full Professor of the Department of Informatics Engineering of the Faculty of Sciences and Tecnology of the University of Coimbra In loving memory of my father. Acknowledgements Thank you very much to Prof. Ernesto Costa, for his vision on research and education, for all the support and contributions to my ideas, and for the many life stories that always lighten the mood. Thank you also to Nuno Lourenço for the many discussions, shared books, and his constant helpfulness.
    [Show full text]
  • Gene Regulatory Network Reconstruction Using Bayesian
    1 Gene regulatory network reconstruction using Bayesian networks, the Dantzig selector, the Lasso and their meta-analysis Matthieu Vignes1∗, Jimmy Vandel1, David Allouche1, Nidal Ramadan-Alban1, Christine Cierco-Ayrolles1, Thomas Schiex1, Brigitte Mangin1, Simon de Givry1 1 SaAB Team/BIA Unit, INRA Toulouse, Castanet-Tolosan, France ∗ E-mail: [email protected] Abstract Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth \Dialogue for Reverse Engineering Assessments and Methods" (DREAM5) challenges are aimed at assessing methods and as- sociated algorithms devoted to the inference of biological networks. Challenge 3 on \Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We in- vestigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics. Introduction Inferring gene regulatory networks. Gene regulatory networks (GRN) are simplified representa- tions of mechanisms that make up the functioning of an organism under given conditions. A node in such a network stands for a gene i.e.
    [Show full text]
  • 'Non-Genetic' Inheritance: Insights from Molecular-Evolutionary Crosstalk
    Adrian-Kalchhauser et al / Understanding NGI: Insights from Molecular-Evolutionary Crosstalk / EcoEvoRxiv 20200501 Understanding 'Non-genetic' Inheritance: Insights from Molecular-Evolutionary Crosstalk Irene Adrian-Kalchhauser1, Sonia E. Sultan2, Lisa Shama3, Helen Spence-Jones4, Stefano Tiso5, Claudia Isabelle Keller Valsecchi6, Franz J. Weissing5 Addresses 1Centre for Fish and Wildlife Health, Department for Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3012 Bern, Switzerland 2Biology Department, Wesleyan University, Middletown CT 06459 USA 3Coastal Ecology Section, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Wadden Sea Station Sylt, Hafenstrasse 43, 25992 List, Germany 4Centre for Biological Diversity, School of Biology, University of St Andrews, UK 5Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands 6Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany Corresponding author: Adrian-Kalchhauser, I. ([email protected]) Keywords epigenetics, epiallelic variation, methylation, transgenerational effects, inherited gene regulation, gene regulatory network model Abstract Understanding the evolutionary and ecological roles of 'non-genetic' inheritance is daunting due to the complexity and diversity of epigenetic mechanisms. We draw on precise insights from molecular structures and events to identify three general features of 'non-genetic' inheritance systems that are central to broader investigations: (i) they are functionally interdependent with, rather than separate from, DNA sequence; (ii) each of these mechanisms is not uniform but instead varies phylogenetically and operationally; and (iii) epigenetic elements are probabilistic, interactive regulatory factors and not deterministic 'epi-alleles' with defined genomic locations and effects. We explain each feature and offer research recommendations.
    [Show full text]
  • Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation
    Utah State University DigitalCommons@USU All Graduate Theses and Dissertations Graduate Studies 5-2015 Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation Hamid Mohamadlou Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/etd Part of the Computer Sciences Commons Recommended Citation Mohamadlou, Hamid, "Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation" (2015). All Graduate Theses and Dissertations. 4539. https://digitalcommons.usu.edu/etd/4539 This Dissertation is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. ALGORITHMIC INFORMATION THEORY APPLICATIONS IN BRIGHT FIELD MICROSCOPY AND EPITHELIAL PATTERN FORMATION by Hamid Mohamadlou A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science Approved: Dr. Nicholas Flann Dr. Gregory Podgorski Major Professor Committee Member Dr. Xiaojun Qi Dr. Minghui Jiang Committee Member Committee Member Dr. Haitao Wang Dr. Mark R. McLellan Committee Member Vice President for Research and Dean of the School of Graduate Studies UTAH STATE UNIVERSITY Logan, Utah 2015 ii Copyright c Hamid Mohamadlou 2015 All Rights Reserved iii ABSTRACT Algorithmic Information Theory applications in Bright Field Microscopy and Epithelial Pattern Formation by Hamid Mohamadlou, Doctor of Philosophy Utah State University, 2015 Major Professor: Dr. Nicholas Flann Department: Computer Science Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information.
    [Show full text]
  • Computational Representation of Developmental Genetic Regulatory Networks
    Developmental Biology 283 (2005) 1 – 16 www.elsevier.com/locate/ydbio Review Computational representation of developmental genetic regulatory networks William J.R. Longabaugha, Eric H. Davidsonb,1, Hamid Bolouria,b,* aInstitute for Systems Biology, Seattle, WA 98103-8904, USA bDivision of Biology, California Institute of Technology, Pasadena, CA 91125, USA Received for publication 12 February 2005, revised 30 March 2005, accepted 16 April 2005 Available online 23 May 2005 Abstract Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multi- layered, and organized in a nested, modular hierarchy of regulatory network kernels, function-specific building blocks, and structural gene batteries. They are also inherently multicellular and involve changing topological relationships among a growing number of cells. Reconstruction of developmental GRNs requires unique computational tools that support the above representational requirements. In addition, we argue that DNA-centered network modeling, separate descriptions of network organization and network behavior, and support for network documentation and annotation are essential requirements for computational modeling of developmental GRNs. Based on these observations, we have developed a freely available, platform-independent, open source software package (BioTapestry) which supports both the process of model construction and also model visualization, analysis, documentation, and dissemination. We provide an overview of the main features of BioTapestry. The BioTapestry software and additional documents are available from http://www.biotapestry.org.We recommend BioTapestry as the substrate for further co-development for and by the developmental biology community. D 2005 Elsevier Inc. All rights reserved. Keywords: Genetic regulatory networks; Computational modeling; Visualization; Documentation Introduction can be fully and unambiguously verified by well-established experimental procedures.
    [Show full text]
  • The Sonic Hedgehog Signaling System As a Bistable Genetic Switch
    2748 Biophysical Journal Volume 86 May 2004 2748–2757 The Sonic Hedgehog Signaling System as a Bistable Genetic Switch Karen Lai, Matthew J. Robertson, and David V. Schaffer Department of Chemical Engineering and the Helen Wills Neuroscience Institute, University of California, Berkeley, California ABSTRACT Sonic hedgehog (Shh) controls critical cellular decisions between distinct fates in many systems, particularly in stem cells. The Shh network functions as a genetic switch, and we have theoretically and computationally analyzed how its structure can endow it with the ability to switch fate choices at a threshold Shh concentration. The network is composed of a positive transcriptional feedback loop embedded within a negative signaling feedback loop. Specifically, positive feedback by the transcription factor Gli, which upregulates its own expression, leads to a switch that can adopt two distinct states as a function of Shh. However, Gli also upregulates the signaling repressor Patched, negative feedback that reins in the strong Gli autoregulatory loop. Mutations that have been associated with cancer are predicted to yield an irreversible switch to a high Gli state. Finally, stochastic simulation reveals the negative Patched feedback loop serves a critical function of dampening Gli fluctuations to reduce spontaneous state switching and preserve the network’s robust, switch-like behavior. Tightly linked positive and negative feedback loops are present in many signaling systems, and the Shh system is therefore likely representative of a large set of gene regulation networks that control stem cell fate throughout development and into adulthood. INTRODUCTION Throughout development and adulthood, cells are exposed to development by forming a concentration gradient, a phe- complex regulatory signals and must correctly interpret these nomenon best characterized in the neural tube and spinal signals to implement necessary functional decisions.
    [Show full text]
  • Analysis on Gene Modular Network Reveals Morphogen-Directed Development Robustness in Drosophila
    Zhang et al. Cell Discovery (2020) 6:43 Cell Discovery https://doi.org/10.1038/s41421-020-0173-z www.nature.com/celldisc ARTICLE Open Access Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila Shuo Zhang1,2,JuanZhao1,3, Xiangdong Lv1,2, Jialin Fan1,2,YiLu1,TaoZeng 1,3, Hailong Wu1, Luonan Chen 1,3,4,5 and Yun Zhao1,4,6 Abstract Genetic robustness is an important characteristic to tolerate genetic or nongenetic perturbations and ensure phenotypic stability. Morphogens, a type of evolutionarily conserved diffusible molecules, govern tissue patterns in a direction-dependent or concentration-dependent manner by differentially regulating downstream gene expression. However, whether the morphogen-directed gene regulatory network possesses genetic robustness remains elusive. In the present study, we collected 4217 morphogen-responsive genes along A-P axis of Drosophila wing discs from the RNA-seq data, and clustered them into 12 modules. By applying mathematical model to the measured data, we constructed a gene modular network (GMN) to decipher the module regulatory interactions and robustness in morphogen-directed development. The computational analyses on asymptotical dynamics of this GMN demonstrated that this morphogen-directed GMN is robust to tolerate a majority of genetic perturbations, which has been further validated by biological experiments. Furthermore, besides the genetic alterations, we further demonstrated that this morphogen-directed GMN can well tolerate nongenetic perturbations (Hh production changes) via computational fi 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; analyses and experimental validation. Therefore, these ndings clearly indicate that the morphogen-directed GMN is robust in response to perturbations and is important for Drosophila to ensure the proper tissue patterning in wing disc.
    [Show full text]
  • Gene Regulatory Networks (Grns) Are the On- Off Switches of a Cell Operating at the Gene Level
    CS 4491/CS 7990 SPECIAL TOPICS IN BIOINFORMATICS Mingon Kang, Ph.D Computer Science, Kennesaw State University Biological Networks Provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks Represent complex biological systems using graphs Nodes: units in the network Edges: interactions between the units Biological Networks Network analysis is increasingly recognized as a powerful approach to understanding biological organization and the function of cellular components and may help us to understand the principles deriving the evolution of living organisms. Most genes and proteins do not have a function on their own, rather their role is realized through a complex web of interactions with other proteins, genes, and biomolecules. Reference: http://homepages.ulb.ac.be/~dgonze/TEACHING/network_motifs.pdf Biological Networks Gene regulatory network: two genes are connected if the expression of one gene modulates expression of another one by either activation of inhibition Protein interaction network: proteins that are connected in physical interactions or metabolic and signaling pathways of the cell; Metabolic network: metabolic products and substrates that participate in one reaction; Reference: http://dna.cs.byu.edu/bio465/slides/regulatory-network.ppt Biological Networks These networks can be obtained from A collection of database MIPS, String -> protein interaction network A high-throughout experiments Microarray -> regulation network Large-scale
    [Show full text]