Bioinformatics Advance Access originally published online on January 31, 2007
Bioinformatics 2007 23(6):777-779; doi:10.1093/bioinformatics/btm004
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SGN Sim, a Stochastic Genetic Networks Simulator
1Institute for Biocomplexity and Informatics, University of Calgary, Canada and 2Centre for Computational Physics, University of Coimbra, P-3004-516 Coimbra, Portugal
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Summary: We present SGNSim, ‘Stochastic Gene Networks Simulator’, a tool to model gene regulatory networks (GRN) where transcription and translation are modeled as multiple time delayed events and its dynamics is driven by a stochastic simulation algorithm (SSA) able to deal with multiple time delayed events. The delays can be drawn from several distributions and the reaction rates from complex functions or from physical parameters. SGNSim can generate ensembles of GRNs, within a set of user-defined parameters, such as topology. It can also be used to model specific GRNs and systems of chemical reactions. Perturbations, e.g. gene deletion, over-expression, copy and mutation, can be modeled as well. As examples, we present a model of a toggle switch without cooperative binding subject to perturbations, a system of reactions within a compartmentalized environment where membrane crossing is controlled by a negative feedback mechanism and a simulation based on the yeast transcriptional network.
Availability: SGNSim program, instructions and examples available at http://www.cs.tut.fi/~sanchesr/SGN/SGNSim.html.
Contact: andre.sanchesribeiro{at}tut.fi
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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One of the goals of systems biology is to understand how genes and proteins interact by developing computer models of biological phenomena, data or patterns, such as complex biochemical reactions or gene networks. Recent studies on gene expression (Raser and O'shea, 2003) and small synthetic genetic networks (Elowitz and Leibler, 2005; Gardner et al., 2000) provided experimental data on the phenotypic variability and stochastic nature of gene expression. Corresponding theoretical investigations incorporating the stochastic nature of chemical reactions have given reasonable explanations for these observations. While non-delay reactions correctly model equilibrium states of simple synthetic gene systems, the dynamics of real gene regulatory networks (GRNs) are so complex [e.g. due to many feedback loops (Monk, 2003)] that time delays (Gibson, et al., 2003), namely for transcription and translation (Gaffney and Monk, 2006), should be included (Bratsun et al., 2005). In many cases the behavior of GRNs is characterized by transients, rather than just steady states. For example protein production delays were shown to significantly alter transients between attractors, even in simple GRNs (Ribeiro et al., 2006). Here, we use a generalized delay algorithm that handles multiple distinct delayed output events for each input event (Roussel and Zhu, 2006). We model GRNs using a set of reactions (Ribeiro et al., 2006), including time-delayed transcription and translation. Genes interact via activation and repression reactions and the dynamics is driven by the Stochastic stimulation algorithm (SSA) (Gillespie, 1976). We present SGNSim which models a wide range of systems of chemically interacting elements. The reaction rates may be constant or a complex function of any element's concentration. Multiple time delays, generated from distributions, are also supported. It can incorporate diffusion and membrane transport as time delayed events, perturbations (e.g. gene deletion, knockout, over-expression, copy, mutation) and importantly it can generate random GRNs with some average features and apply the ensemble approach (Kauffman, 2004) to study large networks.
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2 METHODS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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SGNSim consists of a GRN or chemical system generator and a dynamics simulator. SGNSim is command line driven, receiving as input a .g file where reactions and elements initial quantities are specified. The output is a tab-delimited text file with a time series of all elements' quantities (see Availability section). GRNs are generated from the following reactions: for gene i =1, ... , N, basal transcription of promoter Proi by RNA Polymerase (RNAp) (1), activated transcription where operators sites j of gene i, Proi,j, are occupied by one or more activators (2), translation of RNA by ribosomes (Rib) into proteins (pi) (3), repression/unrepression of a gene at operator site j (4) and (5), decay of ribosome binding sites (RBS) and proteins (pi's) (6), and proteins polymerization (here, limited to dimers for simplicity) (7), that can act as indirect repression or activation. Unless time delays,
's, are explicitly represented in the products, all events, reactants depletion and products appearance, occur instantaneously at t:
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| (1) |
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| (2) |
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| (3) |
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| (4) |
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| (5) |
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| (6) |
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| (7) |
In Equations 1–3
, 's superscripts distinguish the delays between products. Subscripts distinguish the delays of products of reactions associated to different genes. For example if reaction of Equation 1 occurs for gene i, at time t, Proi and one RNAp are removed from the system and placed in a waiting list of events. At 
, Proi and RBS and at 
RNAp are released (unchanged), becoming available for reactions. We do not explicitly represent complete RNA molecules since RBS's appearance determines when translation can start. Transcription and translation could also be modeled in a single step (Roussel and Zhu, 2006). For simplicity, proteins and RBS degradation are assumed to occur at constant rate and modeled as unimolecular reactions. Since genes have multiple operator sites, a promoter `state' is defined by the combined states of its operator sites, i.e. if they have their respective transcription factor (TFs) bound to it or not. We adopted the following notation: an array is used as the index of a promoter, e.g. Proi,op(i) where op(i) = (0,pw,0). In this case, only pw is bound to operator site j = 2 of gene i, while the other operator sites are free.
SGNSim creates a GRN by generating a graph with the desired topology, imposing in-degree and out-degree distributions. Inputs are monomers or combined into multimers and set as direct or indirect. After, each direct input is assigned to an operator site (different TFs can be allowed, or not, to compete for the same operator site), while indirect inputs are given a target. Lastly, a function is assigned to each gene, defining the gene's response to a combination of TFs (promoter state). The functions can be unate, or a transcription rate can be randomly assigned to each combination of promoter states, since the combined effect of TFs on transcription rates is not necessarily unate.
The information defining the set of reactions that the resulting GRN consists of is stored in an adjacency matrix. A site aij of the adjacency matrix contains the set of values (x1, x2,x3, x4). x1 defines the type of interaction from j to i: 0 (no interaction), 1 (monomer) or 2 (dimer). In general, this value can grow to include higher-order multimers. x2 defines if it is a direct interaction (+1), i.e. if pj binds to Proi, or indirect (–1), as in (5). x3 is not null in the case of a dimer interaction and equal to the number identifying the protein that binds to pj and forms the heterodimer. If higher-order multimers are allowed, this field becomes a list of all participating monomers. Finally, x4 identifies which operator site j of Proi the complex binds to, and is 0 in the case of an indirect interaction.
Given the formalism described earlier, SGNSim can generate ensembles of random networks within user-defined parameters, allowing exploration of a large phase space. The user can, for example, select the topology from several provided topologies, specify the distributions from which the initial concentrations of chemical species and the reaction rates are generated, set the ratio of activation/inhibition interactions and the fraction of housekeeping genes (those with basal level of expression), etc.
Because genes have different lengths, resulting in RNA and proteins with different sizes, their transcription and translation delays differ. Also, RNAp's do not transcribe at a constant speed due to molecular noise, so we allow delays to vary from the mean delay at runtime. The resulting network can be simulated for a specified amount of time and sampled at set regular intervals.
SGNSim can also simulate specific systems of reactions such as chemical pathways or small GRNs. In addition, these systems can be modeled as if in compartmentalized spaces. Here, two processes are important: membrane crossing and diffusion. The crossing of a molecule A from compartment 1, of volume V1, to compartment 2, can be modeled by a single step reaction (A1 
A2) with a propensity given by (8):
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| (8) |
2) is the probability of molecule A crossing when colliding with the membrane. When relevant to the system's dynamics, diffusion can be modeled by a time delayed version of the membrane crossing reaction, where the time delay should follow a normal distribution. In some cases, as the one just described, it might be useful to set the physical parameters from which the propensity can be computed. For that purpose, SGNSim also provides an interface that calculates rate constants from physical parameters if these are provided. Whether using the ensemble approach or a user defined system of reactions, SGN Sim can test the system in many ways. For instance, in GRNs, it can do ‘mock’ perturbation experiments like gene deletion, over-expression, copy and mutation. For example to model a gene over-expression t seconds after the experiment started, a specific inducer is introduced at that time in the simulation.
Importantly, reaction rates can be some complex functions, such as hill functions, that depend on concentrations at each moment. This allows, for example, modeling active transport via a membrane.
For models of gene expression, SGNSim can include alternative splicing where a pre-mRNA transcribed from a gene can lead to different mature mRNA molecules and, thus, different proteins. This can be achieved by introducing an extra reaction: 
.
SGNSim dynamics are simulated using the ‘delayed SSA’ (Roussel and Zhu, 2006), that, unlike the non-delayed SSA, uses a waiting list to store the delayed output events, and proceeds as follows:
- Set t
0, tstop stop time, read initial number of molecules and reactions, create empty waiting list L.
- Do an SSA step for input events to get next reacting event R1 and corresponding occurrence time t1.
- If t1 + t < tmin (the least time in L), set t t + t1. Update number of molecules by performing R1, adding delayed products into L as necessary.
- If t1 + t
tmin, set t tmin. Update number of molecules by releasing the first element in L.
- If t < tstop, go to step 2.
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3 DISCUSSION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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SGNSim can model a wide range of chemical systems since it allows compartmentalization, complex rate constants, multiple time delays and perturbations, among other features. Importantly, using an improved version of the GRN model proposed in (Ribeiro et al., 2006), it can generate ensembles of large GRNs, within a large range of user-defined parameters. As examples of applications of SGNSim we modeled a toggle switch without cooperative binding subject to perturbations after a transient, a compartmentalized environment with active membrane transport and a model of the yeast transcriptional network (see Availability section).
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ACKNOWLEDGEMENTS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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The authors thank iCORE, a funding agency of Alberta and the IBI Informatics of the U. of Calgary.
Conflict of Interest. none declared.
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FOOTNOTES |
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Associate Editor: Chris Stoeckert
Received on November 5, 2006; revised on January 4, 2007; accepted on January 10, 2007
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Bratsun D, et al. Delay-induced stochastic oscillations in gene regulation. In: PNAS (2005) 102:14593.
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Roussel MR, Zhu R. Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression, (submitted). (2006).
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