GeNESiS: gene network evolution simulation software
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1. AkKrishnan created the main algorithm and wrote the paper AKratz designed and developed the software and also helped in the writing of the paper MT was in charge of the overall project All authors read and approved the final manuscript References Siegal ML Bergman A Waddington s canalization revisited Developmental stability and evolution Proc Natl Acad Sci USA 2002 99 16 10528 10532 2 Bergman A Siegal ML Evolutionary capacitance as a general feature of complex gene networks Nature 2003 424 549 552 3 Gavrilets S Hastings A A quantitative genetic model for selec tion on developmental noise Evolution 1994 48 5 1478 1486 4 Wagner A Does evolutionary plasticity evolve Evolution 1996 50 3 1008 1023 5 Wagner GP Booth G Bagheri Chaichian H A population genetic theory of canalization Evolution 1997 51 2 329 347 6 Ancel LW Fontana W Plasticity evolvability and modularity in rna J Exp Zool 2000 288 242 283 7 Rice SH The evolution of canalization and the breaking of von baer s laws Modeling the evolution of development with epistasis Evolution 1998 52 647 656 8 Eshel Matessi C Canalization genetic assimilation and prea daptation A quantitative genetic model Genetics 1998 149 2119 2133 9 Krishnan A Giuliani A Tomita M Evolution of gene regulatory networks Robustness as an emergent property of evolution Physica A 2008 387 2170 2186 Page 6 of 7 page number not for c2. A molecules Initial of Proteins Inital of RNA P Inital of Inputs OF INPUT TYPE m STEP_TOGETHER Default Cancel Figure 4 The parameters panes Simulation Parameters Parallel Computing o of CPUs m Path of GeNESiS parallel version GeNESiS Browse Path of GeNESiS single CPU version GeNESiS_nonParallel Browse Path of runmodek runmodel Browse Path of MakeDotFile MakeDotFile Browse Default Cancel Figure 5 The parameters panes Other Parameters Conclusion Our earlier study 9 on GRN evolution using GeNESiS enabled us to conclude that it can serve as an important tool for analyzing the evolution of GRNs The ability to study GRN evolution under different selective pressures and starting conditions is an inherent strength of the GeN ESiS framework We foresee GeNESiS being used for large scale simulation of GRN evolution Future enhancements to GeNESiS would include the ability to make use of the grid framework to launch massively parallel simulations with hundreds of genes Availability and requirements e Availability The software is freely downloadable from http genomics iab keio ac jp genesis html e Programming Language The core algorithm is written in C while the GUI is written in Java Dependencies GNU Scientific Library PGAPACK Graph Viz OpenMPI MPICH e Platforms Linux Unix based Authors contributions
3. arallel software package GeNESiS for the modeling and simulation of the evolution of gene regulatory networks GRNs The software models the process of gene regulation through a combination of finite state and stochastic models The evolution of GRNs is then simulated by means of a genetic algorithm with the network connections represented as binary strings The software allows users to simulate the evolution under varying selective pressures and starting conditions We believe that the software provides a way for researchers to understand the evolutionary behavior of populations of GRNs Conclusion We believe that GeNESiS will serve as a useful tool for scientists interested in understanding the evolution of gene regulatory networks under a range of different conditions and selective pressures Such modeling efforts can lead to a greater understanding of the network characteristics of GRNs Background While a lot of interest has been focused on the modeling and simulation of Gene Regulatory Networks GRNs in recent years the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown There have been efforts at understanding particular aspects of evolution such as the correlation between development evolution and robustness or canalization of the network 1 2 Studies on the evolution of GRNs have tended to focus on certain a priori assumptions about the nature of the evolutionary force such as stabilizing s
4. d immediately upon acceptance e cited in PubMed and archived on PubMed Central e yours you keep the copyright Submit your manuscript here BioMedcentral http www biomedcentral com info publishing_adv asp Page 7 of 7 page number not for citation purposes
5. e BioMed Central BMC Bioinformatics Software GeNESiS gene network evolution simulation software Anton Kratz Masaru Tomita and Arun Krishnan Address Institute for Advanced Biosciences Keio University 14 1 Baba Cho Tsuruoka Yamagata ken 997 0035 Japan Email Anton Kratz anton kratz gmail com Masaru Tomita mt sfc keio ac jp Arun Krishnan drarunkrishnan gmail com Corresponding author Published 16 December 2008 BMC Bioinformatics 2008 9 541 Received 21 May 2008 doi 10 1186 1471 2105 9 541 Accepted lp December2098 This article is available from http www biomedcentral com 147 1 2105 9 541 2008 Kratz et al licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons org licenses by 2 0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited Abstract Background There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks GRNs However the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown In an earlier work we developed a framework to analyze the effect of objective functions input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs Results In this work we present a p
6. election 3 6 or the use of more abstract 7 and analytical 8 mod els Siegal et al 1 showed that the developmental process constrains the genetic system to produce robustness even in the absence of a selection towards optimum In an earlier work 9 we developed a framework to ana lyze the effect of objective functions input types and start ing populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs We observed that robustness evolves along with the networks as an emergent property even in the absence of specific selective pressure During this optimization process towards a more robust system multiple genotypes evolve which give rise to the same phenotype this is in accord ance with the theoretical view that natural selection oper Page 1 of 7 page number not for citation purposes BMC Bioinformatics 2008 9 541 ates on phenotypes thereby accommodating variation in the genotype by fixing those changes that are phenotype neutral In this work we introduce a parallel software package GeNESiS Gene Network Evolution SImulation Software that implements the framework developed in 9 for stud ying the evolution of GRNs Software The software GeNESiS which stands for GEne Network Evolution SImulation Software is composed essentially of two parts The front end graphical user interface GUI written in Java and the backend algorithm written in C The algorithm itsel
7. er nal inputs and those which do Robustness tries to explic itly maximize the robustness of the system by picking the solution which undergoes the least change to all point mutations of the bitstring Total tries to maximize the sum of all the proteins in the system and Biomass_plus_min_links tries to maximize the biomass as before while at the same time minimizing the total number of connections in the network The Evolution Parameters tab also has options to set the numbers of genes proteins inputs and RNAP cofactor complexes in the system and these are linked to their counterparts on the Evolve pane There are also options http www biomedcentral com 1471 2105 9 541 Ea Genesis Parameters a Evolution Parameters Model Parameters Other Other Maximum Generations Convergence Generations Log Generations Seed Type ALL Population Size Population Replacement Size Fitness Function Crossover Function Mutation Probability Crossover Probability of Genes of Proteins of Inputs of RNAP RESTART_FLAG RESTART_FREQ 50H OBJ_TYPE NO_SELF_REGULATION o PRINT_FINAL POPULATION v7 PRINT_GEN_POPULATION Default Cancel Figure 3 The parameters panes Evolution Parameters here to print the final population as well as the popula tion every generation Model Parameters This tab contains all the parameters required to simulate the model of a give
8. f is parallel in nature and has been built using the GNU Scientific Library GSL 10 and the par allel genetic algorithm package PGAPACK 11 A slight change was made in the data structure of PGAPACK in order to pass parameters from one subroutine to another Moreover since PGAPACK uses the Message Passing Inter face MPI 12 in order to run parallely across multiple processors the algorithm requires the presence of some MPI implementation and has been tested using both MPICH 13 and OpenMPI 14 Algorithm At the basic level the model consists of a finite state aspect since the state of the network depends on the bind ing unbinding of proteins to the different binding sites in the promoter regions of the different genes Each protein has binding domains for none or more genes The effect of a protein binding to the promoter region of a gene can be activation or repression In addition a protein can also undergo an activating or a repressing post translational modification PTM A similar abstraction can also be made for the RNAP cofactor complexes Each RNAP con factor complex can bind to none or more genes in order to transcribe them The RNAP cofactor complexes also evolve by either gaining or losing the ability to bind to and transcribe specific genes The gene activity in our model is governed by the number of molecules of the active gene that is one with pro moter proteins bound to their promoter regions as a resul
9. he Simulate pane to the left This pane contains options that control the dis play of the different simulation curves A zoom function ality has also been provided This panel also allows the user to mutate the network by either mutating one of the connections or the PTM or RNAP cofactor complex bind ing ability However at any given time only one muta tion can be carried out for the base network Parameters GeNESiS has a number of parameters that can be set both for the evolution of the networks as well as for simulating a given network The three different tabs on the Parameters pane corresponding to the Evolution Model and Other parameters are shown in Figures 3 4 and 5 respectively Evolution Parameters The Evolution Parameters tab contains parameters that are typically used by genetic algorithms such as the maximum number of generations the maximum number of genera tions to converge to a solution that is the maximum number of generations for a solution to not change in order to assume convergence the size of the population the population replacement size and the mutation and crossover probabilities The users are directed to 11 for more details about the parameters used in the genetic algorithm There are four different objective functions that can be used for evolution called Biomass Robustness Total and Biomass_plus_min_links Biomass tends to maximize the difference between those proteins which don t have ext
10. ingle canvas on the right when it is first launched This canvas is linked to the tab Evolve on the left When the program is launched the panels as well as the canvas are greyed out The user can either open a saved project or start a new project Starting a new project essentially results in the cre ation of a new directory that will hold the files resulting from one or more runs of the network evolution For each run a subdirectory is created within the main project directory simrun1 simrun2 etc Inside each subdirec tory are a number of different files that are generated dur ing the evolution run There are four main types of files generated as given below e GPIR This file contains the number of genes G pro teins P inputs I and RNAP cofactor complexes R used during the run in a single column e LOG This contains the logged output from the run The log interval can be set using the Parameters tab see Section on parameters below Each line contains the generation number the best solution for that particular generation the fitness function score for that solution and the values of the protein levels e LOG pop This file contains for each generation the list of all individuals in the population and their fitness func tion values e dot These files contain the network realization in dot format For each generation three different dot files are Page 3 of 7 page number not for citation purpose
11. itation purposes BMC Bioinformatics 2008 9 541 http Awww biomedcentral com 1471 2105 9 541 10 Pierce R The gnu scientific software library 1996 http www gnu org software gsl Il Levine D Users guide to the pgapack parallel genetic algo rithm library 1996 ftp info mcs anl gov pub pgapack pga pack tar Z 12 Mpi Message passing interface http www unix mcs anl gov mpi 13 Gropp W Lusk E Doss N Skjellum A A high performance port able implementation of the MPI message passing interface standard Parallel Computing 1996 22 789 828 14 Gabriel E Fagg GE Bosilca G Angskun T Dongarra J et al Open mpi Goals concept and design of a next generation mpi implementation th European PVM MPI Users Group Meeting Budapest Hungary 2004 15 Brazma A Schlitt T Reverse engineering of gene regulatory networks a finite state linear model Genome Biology 2003 4 6 P5 16 Gansner ER Koutsofios E North SC Vo KP A technique for drawing directed graphs Software Engineering 1993 19 214 230 http www graphviz org Publish with BioMed Central and every scientist can read your work free of charge BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime Sir Paul Nurse Cancer Research UK Your research papers will be available free of charge to the entire biomedical community e peer reviewed and publishe
12. ks Evolution proceeds under certain pre defined selective pressures such as maximizing the biomass or through minimizing the number of inter actions between proteins or a combination of both selec tive pressures Evolution stops when stable networks are obtained The reader is referred to 9 for more details on the algorithm Graphical User Interface The GUI for GeNESiS has been written in Java and con tains two main canvases as shown in Figures 1 and 2 The Evolve tab is used for the evolution of a given network while the Simulate tab is used to simulate a particular GRN with the desired parameters The two essential can Page 2 of 7 page number not for citation purposes BMC Bioinformatics 2008 9 541 E Genesis Gene Network Evolution SImulation Software a File Options Help Graph Evolve Simulate http www biomedcentral com 1471 2105 9 541 sjsj ioii Seed Type Seed Type Numbers of Genes lt H of Proteins of Inputs of RNAP CHECHEN L GENERATIONS MAX GEN CONY GEN VIZ GEN CHENE Info Generation 8 Best Score 1 840000e 02 of Runs Evolve Other Settings Show All v Delay 5SH Simulate pause advance Figure Evolution Window vases and their associated functionalities will be discussed in more detail below GRN Evolution The main window has two panels on the left and a s
13. n gene regulatory network It contains the kinetic parameters in effect these are actually the reac tion probabilities for all the reactions as well as general parameters such as the simulation time the reaction step time and the sampling time This tab also contains the ini tial numbers of the DNA molecule proteins RNAP cofac tor complexes as well as the inputs The input step size as well as the input step time can be set here These inputs are basically proteins which are external to the network but still have a role in the activation of transcription or in the repression of some of the proteins of the network Other Parameters This tab contains miscellaneous parameters such as the paths for the different executables as well as a checkbox for the use of multiple CPUs The software package con tains a detailed user manual that describes the software in greater detail along with examples Page 5 of 7 page number not for citation purposes BMC Bioinformatics 2008 9 541 El cenesis Parameters OOOO O Evolution Parameters Model Parameters Other L http www biomedcentral com 1471 2105 9 541 _ cenesis Parameters OOOO O O er 1 Evolution Parameters Model Parameters Other Kinetic Parameters ki ki k2 k2 k3 k4 k5 k6 General Parameters Simulation Time Reaction Step Time _ Sampling Time Input Step Size Input Step Time L Initial of DN
14. rget gene in the absence of any other transcription factors The protein molecule can also undergo post translational modifications PTM which can be both enabling and dis abling modifications Enabling modifications turn the activity of a protein molecule on while an inactivating modification turns the activity of the molecule off Network Evolution The evolution of the networks is studied by means of a genetic algorithm GA A bitstring representation of the different RNAP cofactor complexes and the protein mole cules is concatenated together to form a representation of the entire network Each such representation of a network is used as an individual chromosome in the genetic algo rithm At the start a population of solutions is initialized using networks with random connectivities or ones in which all proteins have broad specificities These corre spond to two scenarios random connectivities corre sponding to specificity of DNA protein interactions while those in which all proteins are connected to each other correspond to the situation whereby any protein can acti vate any other protein leading to very broad specificities Once the initial population has been seeded evolution is allowed to proceed In each generation two individuals in the population are chosen at random to mate in order to produce offspring Individual networks are also subject to mutations while unfit individuals die out only to be replaced by newer networ
15. s BMC Bioinformatics 2008 9 541 http Awww biomedcentral com 1471 2105 9 541 E Genesis Gene Network Evolution Simulation Sofware TST File Options Help Simulate Graph Sim Run 1 Gen 13 Evolve 140 130 i F f 120 Simulation Time MAX SIM TIME 3 00H 110 Graph Visualization 100 Inputs DNA Y 30 Q 80 Q_bar L Q_star o 70 R w M 7 60 P Y 50 Refresh 40 amp Zoom Out Close graph 30 Allele E izo i j AN a 16 ek A Ley Ae AP a 0 Tar E oo Jma E aa _ 30 60 90 120 150 180 210 240 270 DNAG ONAL DNA DNAS RO RI R2 R3 MO Ml M2 MS PO Pi P2 P3 Simulation Runi w 44 first 4i back pause gt play I gt advance b gt last Figure 2 Simulation Window created one each for the case where all the species are shown only proteins are shown and only RNAP cofactor complexes along with the genes are shown Opening an aleady existing project simply implies open ing the main project directory containing the many simu lation runs The Evolve tab contains some of the main parameters required for the evolution of the network such as the number of genes proteins inputs and RNAP cofactor complexes the number of generations to evolve the net work as well as the minimum number of generations for the fitness function to be unchanged before converging The panel also has a pull down button to change
16. t of which the model stays closer to reality where a basal level of gene activity is present and genes are seldom seen to exhibit purely binary state behavior Additionally in contrast to the work by Brazma et al 15 time in our case is discrete Moreover the state affects the number of molecules of each species in the system Additionally we also model the effect of reversible PIMs We describe the model in more detail in the following section Model Our model of gene regulatory networks has been dis cussed in detail in 9 here we give only a brief description http www biomedcentral com 1471 2105 9 541 of the model Our model attempts to describe the process of gene regulation from the binding of the transcription factor RNA Polymerase complex to the DNA molecule to the translation of mRNA into the protein product Every gene is represented by a DNA molecule that is assumed to have one or more sites for the binding of transcription fac tors and other cofactors The RNA Polymerase molecule can then bind to the transcription factor cofactor complex which then breaks down on completion of the reading to form the mRNA molecule which is in turn translated to form the associated protein molecule The protein mole cule can cause both positive and negative regulation of its target gene However negative regulation is not inde pendent of the binding order which implies that the mol ecule can only bind to the promoter region of its ta
17. the seed type for the network as well as buttons to start and stop the evolution There are also graphing options available that allow the user to select the species that will be dis played GeNESiS uses the GraphViz 16 package to draw the networks and there is a further option to select the par ticular GraphViz program to use namely dot circo or twopi which result in different network layouts When the EVOLVE button is pressed the network evolu tion algorithm executes in the background and the value of the fitness function for the best network for each gener ation along with the generation number is shown in the panel In addition the best network for each generation is automatically drawn on the graphing canvas on the right using one of the three different GraphViz programs as set in the panel Once the evolution has converged or has been manually stopped a user can follow the evolution Page 4 of 7 page number not for citation purposes BMC Bioinformatics 2008 9 541 ary path by making use of the movie buttons at the bot tom of the canvas A number of different runs can be carried out and the results viewed at any given point in time GRN Simulation GeNESiS also allows the user to simulate a given GRN The network that is simulated is the one currently appear ing on the graphing canvas The results from the simula tion of one such example network are shown in Figure 2 The simulation canvas is controlled by t
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