Gene regulatory network - Knowledge (XXG)

209:. Edges between nodes represent interactions between the nodes, that can correspond to individual molecular reactions between DNA, mRNA, miRNA, proteins or molecular processes through which the products of one gene affect those of another, though the lack of experimentally obtained information often implies that some reactions are not modeled at such a fine level of detail. These interactions can be inductive (usually represented by arrowheads or the + sign), with an increase in the concentration of one leading to an increase in the other, inhibitory (represented with filled circles, blunt arrows or the minus sign), with an increase in one leading to a decrease in the other, or dual, when depending on the circumstances the regulator can activate or inhibit the target node. The nodes can regulate themselves directly or indirectly, creating feedback loops, which form cyclic chains of dependencies in the topological network. The network structure is an abstraction of the system's molecular or chemical dynamics, describing the manifold ways in which one substance affects all the others to which it is connected. In practice, such GRNs are inferred from the biological literature on a given system and represent a distillation of the collective knowledge about a set of related biochemical reactions. To speed up the manual curation of GRNs, some recent efforts try to use 412:

computational simulations. For example, fluctuations in the abundance of feed-forward loops in a model that simulates the evolution of gene regulatory networks by randomly rewiring nodes may suggest that the enrichment of feed-forward loops is a side-effect of evolution. In another model of gene regulator networks evolution, the ratio of the frequencies of gene duplication and gene deletion show great influence on network topology: certain ratios lead to the enrichment of feed-forward loops and create networks that show features of hierarchical scale free networks. De novo evolution of coherent type 1 feed-forward loops has been demonstrated computationally in response to selection for their hypothesized function of filtering out a short spurious signal, supporting adaptive evolution, but for non-idealized noise, a dynamics-based system of feed-forward regulation with different topology was instead favored.

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used due to their simplicity and ability to handle noisy data but lose data information by having a binary representation of the genes. Also, artificial neural networks omit using a hidden layer so that they can be interpreted, losing the ability to model higher order correlations in the data. Using a model that is not constrained to be interpretable, a more accurate model can be produced. Being able to predict gene expressions more accurately provides a way to explore how drugs affect a system of genes as well as for finding which genes are interrelated in a process. This has been encouraged by the DREAM competition which promotes a competition for the best prediction algorithms. Some other recent work has used artificial neural networks with a hidden layer.

1284:), that can model GRNs 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 time 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. Genetic perturbations such as gene deletions, gene over-expression, insertions, frame shift mutations can also be modeled as well. 232:, typically AND, OR, and NOT). These functions have been interpreted as performing a kind of information processing within the cell, which determines cellular behavior. The basic drivers within cells are concentrations of some proteins, which determine both spatial (location within the cell or tissue) and temporal (cell cycle or developmental stage) coordinates of the cell, as a kind of "cellular memory". The gene networks are only beginning to be understood, and it is a next step for biology to attempt to deduce the functions for each gene "node", to help understand 364:. Network motifs can be regarded as repetitive topological patterns when dividing a big network into small blocks. Previous analysis found several types of motifs that appeared more often in gene regulatory networks than in randomly generated networks. As an example, one such motif is called feed-forward loops, which consist of three nodes. This motif is the most abundant among all possible motifs made up of three nodes, as is shown in the gene regulatory networks of fly, nematode, and human. 1288:

input is assigned to an operator site and different transcription factors can be allowed, or not, to compete for the same operator site, while indirect inputs are given a target. Finally, a function is assigned to each gene, defining the gene's response to a combination of transcription factors (promoter state). The transfer functions (that is, how genes respond to a combination of inputs) can be assigned to each combination of promoter states as desired.

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pathway. This suggests that the Hippo signaling pathway operates as a conserved regulatory module that can be used for multiple functions depending on context. Thus, changing network topology can allow a conserved module to serve multiple functions and alter the final output of the network. The second way networks can evolve is by changing the strength of interactions between nodes, such as how strongly a transcription factor may bind to a

41: 353: 1271: 403:, the feed-forward loop acts as a fold-change detector that responses to the fold change, rather than the absolute change, in the level of β-catenin, potentially increasing the resistance to fluctuations in β-catenin levels. Following the convergent evolution hypothesis, the enrichment of feed-forward loops would be an 1287:

The GRN is created from a graph with the desired topology, imposing in-degree and out-degree distributions. Gene promoter activities are affected by other genes expression products that act as inputs, in the form of monomers or combined into multimers and set as direct or indirect. Next, each direct

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From here, a set of reactions were proposed that allow generating GRNs. These are then simulated using a modified version of the Gillespie algorithm, that can simulate multiple time delayed reactions (chemical reactions where each of the products is provided a time delay that determines when will it

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Continuous network models of GRNs are an extension of the Boolean networks described above. Nodes still represent genes and connections between them regulatory influences on gene expression. Genes in biological systems display a continuous range of activity levels and it has been argued that using a

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In multicellular animals the same principle has been put in the service of gene cascades that control body-shape. Each time a cell divides, two cells result which, although they contain the same genome in full, can differ in which genes are turned on and making proteins. Sometimes a 'self-sustaining

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In other recent work, multiscale models of gene regulatory networks have been developed that focus on synthetic biology applications. Simulations have been used that model all biomolecular interactions in transcription, translation, regulation, and induction of gene regulatory networks, guiding the

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Furthermore, there seems to be a trade-off between the noise in gene expression, the speed with which genes can switch, and the metabolic cost associated their functioning. More specifically, for any given level of metabolic cost, there is an optimal trade-off between noise and processing speed and

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The validity of the model can be tested by comparing simulation results with time series observations. A partial validation of a Boolean network model can also come from testing the predicted existence of a yet unknown regulatory connection between two particular transcription factors that each are

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for fast response and noise resistance. A recent research found that yeast grown in an environment of constant glucose developed mutations in glucose signaling pathways and growth regulation pathway, suggesting regulatory components responding to environmental changes are dispensable under constant

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In single-celled organisms, regulatory networks respond to the external environment, optimising the cell at a given time for survival in this environment. Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol. This process, which

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Experimental results have demonstrated that gene expression is a stochastic process. Thus, many authors are now using the stochastic formalism, after the work by Arkin et al. Works on single gene expression and small synthetic genetic networks, such as the genetic toggle switch of Tim Gardner and

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Other work has focused on predicting the gene expression levels in a gene regulatory network. The approaches used to model gene regulatory networks have been constrained to be interpretable and, as a result, are generally simplified versions of the network. For example, Boolean networks have been

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in the equations correspond to critical cell states in which small state or parameter perturbations could switch the system between one of several stable differentiation fates. Trajectories correspond to the unfolding of biological pathways and transients of the equations to short-term biological

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provides a good example. The Hippo signaling pathway controls both mitotic growth and post-mitotic cellular differentiation. Recently it was found that the network the Hippo signaling pathway operates in differs between these two functions which in turn changes the behavior of the Hippo signaling

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of GRNs have been developed to capture the behavior of the system being modeled, and in some cases generate predictions corresponding with experimental observations. In some other cases, models have proven to make accurate novel predictions, which can be tested experimentally, thus suggesting new

371:, suggesting they are "optimal designs" for certain regulatory purposes. For example, modeling shows that feed-forward loops are able to coordinate the change in node A (in terms of concentration and activity) and the expression dynamics of node C, creating different input-output behaviors. The 1087:

Since some processes, such as gene transcription, involve many reactions and could not be correctly modeled as an instantaneous reaction in a single step, it was proposed to model these reactions as single step multiple delayed reactions in order to account for the time it takes for the entire

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to adapt to almost every environmental niche on earth. A network of interactions among diverse types of molecules including DNA, RNA, proteins and metabolites, is utilised by the bacteria to achieve regulation of gene expression. In bacteria, the principal function of regulatory networks is to

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On the other hand, some researchers hypothesize that the enrichment of network motifs is non-adaptive. In other words, gene regulatory networks can evolve to a similar structure without the specific selection on the proposed input-output behavior. Support for this hypothesis often comes from

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signal of histone modification are more correlated with transcription factor motifs at promoters in comparison to RNA level. Hence it is proposed that time-series histone modification ChIP-seq could provide more reliable inference of gene-regulatory networks in comparison to methods based on

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There are three classes of multiple sclerosis: relapsing-remitting (RRMS), primary progressive (PPMS) and secondary progressive (SPMS). Gene regulatory network (GRN) plays a vital role to understand the disease mechanism across these three different multiple sclerosis classes.

1055:, e.g., but proteins do often control gene expression in a synergistic, i.e. non-linear, way. However, there is now a continuous network model that allows grouping of inputs to a node thus realizing another level of regulation. This model is formally closer to a higher order 33: 323:

There are primarily two ways that networks can evolve, both of which can occur simultaneously. The first is that network topology can be changed by the addition or subtraction of nodes (genes) or parts of the network (modules) may be expressed in different contexts.

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The nodes of this network can represent genes, proteins, mRNAs, protein/protein complexes or cellular processes. Nodes that are depicted as lying along vertical lines are associated with the cell/environment interfaces, while the others are free-floating and can

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to activate that sequence. The interaction can be direct or indirect (through transcribed RNA or translated protein). In general, each mRNA molecule goes on to make a specific protein (or set of proteins). In some cases this protein will be

1080:, provided additional experimental data on the phenotypic variability and the stochastic nature of gene expression. The first versions of stochastic models of gene expression involved only instantaneous reactions and were driven by the 396:

delays the activation of arabinose catabolism operon and transporters, potentially avoiding unnecessary metabolic transition due to temporary fluctuations in upstream signaling pathways. Similarly in the Wnt signaling pathway of

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control the response to environmental changes, for example nutritional status and environmental stress. A complex organization of networks permits the microorganism to coordinate and integrate multiple environmental signals.

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gradients, which in effect provide a positioning system that tells a cell where in the body it is, and hence what sort of cell to become. A gene that is turned on in one cell may make a product that leaves the cell and

189:, interacting with molecules in the environment. Still others pass through cell membranes and mediate long range signals to other cells in a multi-cellular organism. These molecules and their interactions comprise a 4026:

BIOREL is a web-based resource for quantitative estimation of the gene network bias in relation to available database information about gene activity/function/properties/associations/interactio.

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through adjacent cells, entering them and turning on genes only when it is present above a certain threshold level. These cells are thus induced into a new fate, and may even generate other

443:. Specifically, on average, the response strength of a gene was predictable from the difference between the numbers of activating and repressing input transcription factors of that gene. 439:

After this environmental change, thousands of genes change expression level. However, these changes are predictable from the topology and logic of the gene network that is reported in

108:, i.e., a micro-machine that catalyses a certain reaction, such as the breakdown of a food source or toxin. Some proteins though serve only to activate other genes, and these are the 926: 177:

At one level, biological cells can be thought of as "partially mixed bags" of biological chemicals – in the discussion of gene regulatory networks, these chemicals are mostly the

1266:{\displaystyle {\text{RNAP}}+{\text{Pro}}_{i}{\overset {k_{i,bas}}{\longrightarrow }}{\text{Pro}}_{i}(\tau _{i}^{1})+{\text{RBS}}_{i}(\tau _{i}^{1})+{\text{RNAP}}(\tau _{i}^{2})} 1095:

For example, basic transcription of a gene can be represented by the following single-step reaction (RNAP is the RNA polymerase, RBS is the RNA ribosome binding site, and Pro

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we associate with wine-making, is how the yeast cell makes its living, gaining energy to multiply, which under normal circumstances would enhance its survival prospects.

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that arise from gene expression. These mRNA and proteins interact with each other with various degrees of specificity. Some diffuse around the cell. Others are bound to

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continuous representation captures several properties of gene regulatory networks not present in the Boolean model. Formally most of these approaches are similar to an

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approaches to explore in an experiment that sometimes wouldn't be considered in the design of the protocol of an experimental laboratory. Modeling techniques include

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Roussel MR, Zhu R (December 2006). "Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression".

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Mangan S, Itzkovitz S, Zaslaver A, Alon U (March 2006). "The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli".

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Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, et al. (October 2002). "Transcriptional regulatory networks in Saccharomyces cerevisiae".

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Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, et al. (October 2002). "Transcriptional regulatory networks in Saccharomyces cerevisiae".

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region at the start of other genes they turn them on, initiating the production of another protein, and so on. Some transcription factors are inhibitory.

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Mangan S, Zaslaver A, Alon U (November 2003). "The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks".

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Leitner F, Krallinger M, Tripathi S, Kuiper M, Lægreid A, Valencia A (July 2013). "Mining cis-regulatory transcription networks from literature".

197: 1547:"The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks" 104:, and will accumulate at the cell membrane or within the cell to give it particular structural properties. In other cases the protein will be an 4574: 3058:"Evolution and Morphogenesis of Differentiated Multicellular Organisms: Autonomously Generated Diffusion Gradients for Positional Information" 3942: 2832:"Boolean modelling reveals new regulatory connections between transcription factors orchestrating the development of the ventral spinal cord" 2771: 2636: 2607: 2578: 1366: 2375:"Whole genome, whole population sequencing reveals that loss of signaling networks is the major adaptive strategy in a constant environment" 951:, one obtains (possibly several) concentration profiles of proteins and mRNAs that are theoretically sustainable (though not necessarily 213:, curated databases, network inference from massive data, model checking and other information extraction technologies for this purpose. 4539: 3032: 4112: 132:

modification may provide cellular memory by blocking or allowing transcription. A major feature of multicellular animals is the use of

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Ribeiro A, Zhu R, Kauffman SA (November 2006). "A general modeling strategy for gene regulatory networks with stochastic dynamics".

338:. Such variation in strength of network edges has been shown to underlie between species variation in vulva cell fate patterning of 320:

to more highly connected genes. Recent work has also shown that natural selection tends to favor networks with sparse connectivity.

4127: 4122: 3870:"Gene Regulatory Networks in Peripheral Mononuclear Cells Reveals Critical Regulatory Modules and Regulators of Multiple Sclerosis" 1730:

Wagner GP, Zhang J (March 2011). "The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms".

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can model a GRN together with its gene products (the outputs) and the substances from the environment that affect it (the inputs).

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For a gene, "on" corresponds to the gene being expressed; for inputs and outputs, "on" corresponds to the substance being present.

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Gillespie DT (1976). "A general method for numerically simulating the stochastic time evolution of coupled chemical reactions".

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Another widely cited characteristic of gene regulatory network is their abundance of certain repetitive sub-networks known as

297:) and many poorly connected nodes nested within a hierarchical regulatory regime. Thus gene regulatory networks approximate a 3789:"Gene expression prediction by soft integration and the elastic net-best performance of the DREAM3 gene expression challenge" 464: 428:

One example stress is when the environment suddenly becomes poor of nutrients. This triggers a complex adaptation process in

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processes, and the loss of such feedback because of a mutation can be responsible for the cell proliferation that is seen in

4518: 64:) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the 4369: 3065:

Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems

224:. The value of the node depends on a function which depends on the value of its regulators in previous time steps (in the 3027:

Knabe JF, Nehaniv CL, Schilstra MJ (2006). "Evolutionary Robustness of Differentiation in Genetic Regulatory Networks".

972: 853: 494: 388:, potentially facilitating the metabolic transition to galactose when glucose is depleted. The feed-forward loop in the 4336: 1077: 968: 3002:

Knabe JF, Nehaniv CL, Schilstra MJ, Quick T (2006). "Evolving Biological Clocks using Genetic Regulatory Networks".

4528: 4394: 4226: 3186:"Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells" 4221: 3341:

Gardner TS, Cantor CR, Collins JJ (January 2000). "Construction of a genetic toggle switch in Escherichia coli".

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Azpeitia E, Muñoz S, González-Tokman D, Martínez-Sánchez ME, Weinstein N, Naldi A, et al. (February 2017).

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in which there is an arrow from one node to another if and only if there is a causal link between the two nodes.

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feedback loop' ensures that a cell maintains its identity and passes it on. Less understood is the mechanism of

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was amongst the first biologists to use the metaphor of Boolean networks to model genetic regulatory networks.

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or any combination of two or more of these three that form a complex, such as a specific sequence of DNA and a

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BEN: a web-based resource for exploring the connections between genes, diseases, and other biomedical entities

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Tutorial: Genetic Algorithms and their Application to the Artificial Evolution of Genetic Regulatory Networks

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Barabási AL, Oltvai ZN (February 2004). "Network biology: understanding the cell's functional organization".

4462: 4399: 4257: 4177: 4020:– regularly updated, contains hundreds of links to papers from bioinformatics, statistics, machine learning. 2504:"Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise" 952: 881: 330: 313: 4447: 4346: 4331: 4300: 4201: 3036: 3007: 2939: 2628: 2599: 2570: 2288: 2243: 1876:"Quantitative variation in autocrine signaling and pathway crosstalk in the Caenorhabditis vulval network" 967:

can usually be characterized by the sign of higher derivatives at critical points, and then correspond to

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Cordero OX, Hogeweg P (October 2006). "Feed-forward loop circuits as a side effect of genome evolution".

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Elowitz MB, Leibler S (January 2000). "A synthetic oscillatory network of transcriptional regulators".

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from scratch through a series of sequential steps. They also control and maintain adult bodies through

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that signal back to the original cell. Over longer distances morphogens may use the active process of

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Kauffman SA (March 1969). "Metabolic stability and epigenesis in randomly constructed genetic nets".

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Kauffman SA (March 1969). "Metabolic stability and epigenesis in randomly constructed genetic nets".

2720: 2657:"The transcription factor network of E. coli steers global responses to shifts in RNAP concentration" 2515: 2139: 2039: 1981: 1558: 1484: 1429: 471:, describing the reaction kinetics of the constituent parts. Suppose that our regulatory network has 368: 309: 267:. Conversely, techniques have been proposed for generating models of GRNs that best explain a set of 217: 96: 4035: 2293: 2248: 4563: 3997:

Plant Transcription Factor Database and Plant Transcriptional Regulation Data and Analysis Platform

3041: 3012: 1081: 290: 146: 3773: 3571: 2944: 2706:"Models of transcription factor binding: sensitivity of activation functions to model assumptions" 4252: 3663: 3629: 3503: 3460: 3374: 3323: 3166: 3115: 3079: 2965: 2744: 2449: 2108: 2005: 1954: 1825:

Jukam D, Xie B, Rister J, Terrell D, Charlton-Perkins M, Pistillo D, et al. (October 2013).

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and proteins which, in turn, determine the function of the cell. GRN also play a central role in

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Almeida BL, Bahrudeen MN, Chauhan V, Dash S, Kandavalli V, Häkkinen A, et al. (June 2022).

1604:"Computational inference of gene regulatory networks: Approaches, limitations and opportunities" 963:

solutions to the above equation to naturally cyclic cell types. Mathematical stability of these

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Blake WJ, KAErn M, Cantor CR, Collins JJ (April 2003). "Noise in eukaryotic gene expression".

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Time is viewed as proceeding in discrete steps. At each step, the new state of a node is a

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in increasing levels of complexity, from gene to signaling pathway, cell or tissue level.

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Lynch M (October 2007). "The evolution of genetic networks by non-adaptive processes".

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for the real molecular dynamics. Such models are then studied using the mathematics of

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contains a feed-forward loop which accelerates the activation of galactose utilization

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Gene regulatory networks are generally thought to be made up of a few highly connected

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Global protein-protein interaction and gene regulation network of Arabidopsis thaliana

2187:"The incoherent feedforward loop can provide fold-change detection in gene regulation" 2162: 2127: 2026:

Boyle AP, Araya CL, Brdlik C, Cayting P, Cheng C, Cheng Y, et al. (August 2014).

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Hoyos E, Kim K, Milloz J, Barkoulas M, Pénigault JB, Munro E, Félix MA (April 2011).

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Lovrics A, Gao Y, Juhász B, Bock I, Byrne HM, Dinnyés A, Kovács KA (November 2014).

2748: 2453: 2028:"Comparative analysis of regulatory information and circuits across distant species" 1716: 4326: 4147: 4137: 4050: 3847: 3572:"Optimal parameter settings for information processing in gene regulatory networks" 3507: 3170: 2969: 2009: 1958: 1759: 1457: 993: 981: 956: 3378: 3201: 2705: 2112: 1643:

Kumar V, Muratani M, Rayan NA, Kraus P, Lufkin T, Ng HH, Prabhakar S (July 2013).

352: 3848:"Time Series Gene Expression Prediction using Neural Networks with Hidden Layers" 3813: 2856: 2391: 2341: 2202: 1620: 1603: 4262: 4242: 1340: 960: 849: 722:{\displaystyle {\frac {dS_{j}}{dt}}=f_{j}\left(S_{1},S_{2},\ldots ,S_{N}\right)} 268: 210: 125: 112:

that are the main players in regulatory networks or cascades. By binding to the

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Proceedings of the National Academy of Sciences of the United States of America

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Proceedings of the National Academy of Sciences of the United States of America

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Genes can be viewed as nodes in the network, with input being proteins such as

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Geard N, Wiles J (2005). "A gene network model for developing cell lineages".

2302: 2257: 1925:"Network motifs in the transcriptional regulation network of Escherichia coli" 1891: 617:. Then the temporal evolution of the system can be described approximately by 404: 326: 305: 260: 142: 3855:

Proceedings of the 7th Biotechnology and Bioinformatics Symposium (BIOT 2010)

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Zabet NR (September 2011). "Negative feedback and physical limits of genes".

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on the concentrations of other substances present in the cell. The functions

4457: 4425: 4192: 4098: 3258: 3103: 2985:"Modelling the Regulation of Gene Expression in Genetic Regulatory Networks" 2480: 2152: 1993: 1842: 1827:"Opposite feedbacks in the Hippo pathway for growth control and neural fate" 1497: 1441: 1346: 1330: 964: 440: 389: 372: 248: 206: 154: 138: 133: 129: 3911: 3832: 3759: 3740: 3708: 3659: 3606: 3556: 3538: 3499: 3456: 3370: 3319: 3276: 3162: 3111: 2961: 2916: 2907: 2890: 2875: 2740: 2690: 2545: 2488: 2445: 2410: 2359: 2310: 2265: 2220: 2171: 2104: 2069: 2001: 1950: 1909: 1860: 1808: 1751: 1708: 1670: 1629: 1588: 1516: 1449: 4024:

https://web.archive.org/web/20060907074456/http://mips.gsf.de/proj/biorel/

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Conant GC, Wagner A (July 2003). "Convergent evolution of gene circuits".

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topology. This is consistent with the view that most genes have limited

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Proceedings of the 7th German Workshop on Artificial Life 2006 (GWAL-7)

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increasing the metabolic cost leads to better speed-noise trade-offs.

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Each gene, each input, and each output is represented by a node in a

857: 382: 162: 105: 2437: 1743: 1700: 4045: 2987:. Biocomputation group, University of Hertfordshire. Archived from 2096: 1645:"Uniform, optimal signal processing of mapped deep-sequencing data" 3634: 1941: 1924: 1385:

Latchman DS (September 1996). "Inhibitory transcription factors".

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of kinetic equations thus correspond to potential cell types, and

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of the prior states of the nodes with arrows pointing towards it.

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constants and sensitivities, are encoded as constant parameters.

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Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms

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events. For a more mathematical discussion, see the articles on

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A bibliography on learning causal networks of gene interactions

2128:"Structure and function of the feed-forward loop network motif" 193:. A typical gene regulatory network looks something like this: 76:, the creation of body structures, which in turn is central to 4001: 3235:"Noise in gene expression: origins, consequences, and control" 1059:. The same model has also been used to mimic the evolution of 1023:

Each node in the graph can be in one of two states: on or off.

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For an example of modelling of the cell cycle with ODEs, see

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Evolving Biological Clocks using Genetic Regulatory Networks

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Goentoro L, Shoval O, Kirschner MW, Alon U (December 2009).

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The International Journal of Biochemistry & Cell Biology

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It is common to model such a network with a set of coupled

4032:– Information page with model source code and Java applet. 3935:

Computational modeling of genetic and biochemical networks

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Elowitz MB, Levine AJ, Siggia ED, Swain PS (August 2002).

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Proceedings of the Artificial Life X Conference (Alife 10)

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Gnanakkumaar P, Murugesan R, Ahmed SS (September 2019).

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Xiong K, Lancaster AK, Siegal ML, Masel J (June 2019).

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enzymatic kinetics. Hence, the functional forms of the

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that give each cell the physical properties it needs.

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be released in the system as a "finished product").

4551: 4480: 4408: 4362: 4355: 4319: 4271: 4235: 4168: 4105: 4014:– Inference of gene association networks with GGMs 3683:"SGN Sim, a stochastic genetic networks simulator" 1923:Shen-Orr SS, Milo R, Mangan S, Alon U (May 2002). 1265: 943: 920: 840: 805: 778: 751: 721: 609: 589: 570:{\displaystyle S_{1}(t),S_{2}(t),\ldots ,S_{N}(t)} 569: 483: 3846:Smith MR, Clement M, Martinez T, Snell Q (2010). 367:The enriched motifs have been proposed to follow 312:. This structure is thought to evolve due to the 817:or simple expressions derived from these e.g. 271:observations. Recently it has been shown that 4078: 3080:"Stochastic gene expression in a single cell" 2557: 2555: 8: 1367:Weighted gene co-expression network analysis 1280:A recent work proposed a simulator (SGNSim, 44:Control process of a gene regulatory network 3787:Gustafsson M, Hörnquist M (February 2010). 3570:Chu DF, Zabet NR, Hone AN (May–June 2011). 3184:Arkin A, Ross J, McAdams HH (August 1998). 3056:Knabe JF, Schilstra MJ, Nehaniv CL (2008). 2704:Chu D, Zabet NR, Mitavskiy B (April 2009). 4359: 4085: 4071: 4063: 2983:Schilstra MJ, Bolouri H (2 January 2002). 2324:Goentoro L, Kirschner MW (December 2009). 4012:Graphical Gaussian models for genome data 4007:BIB: Yeast Biological Interaction Browser 3901: 3822: 3812: 3749: 3739: 3698: 3633: 3546: 3266: 3209: 3040: 3011: 2943: 2906: 2865: 2855: 2680: 2535: 2400: 2390: 2349: 2292: 2247: 2210: 2161: 2151: 2059: 1940: 1899: 1850: 1798: 1660: 1619: 1578: 1506: 1496: 1254: 1249: 1237: 1225: 1220: 1207: 1202: 1189: 1184: 1171: 1166: 1145: 1136: 1130: 1125: 1116: 1114: 936: 895: 885: 883: 832: 826: 797: 791: 770: 764: 743: 737: 708: 689: 676: 661: 637: 627: 625: 602: 582: 552: 524: 502: 496: 476: 4002:Open source web service for GRN analysis 3681:Ribeiro AS, Lloyd-Price J (March 2007). 1004:The following example illustrates how a 3527:Journal of the Royal Society, Interface 2373:Kvitek DJ, Sherlock G (November 2013). 1377: 452:Coupled ordinary differential equations 4575:Index of evolutionary biology articles 3523:"Computational limits to binary genes" 3233:Raser JM, O'Shea EK (September 2005). 921:{\displaystyle {\frac {dS_{j}}{dt}}=0} 36:Structure of a gene regulatory network 2891:"Neural model of the genetic network" 2650: 2648: 2021: 2019: 1820: 1818: 7: 1682: 1680: 864:. System-specific information, like 577:represent the concentrations of the 3033:Akademische Verlagsgesellschaft AKA 2895:The Journal of Biological Chemistry 4609:Evolutionary developmental biology 4385:Evolutionary developmental biology 1471:Davidson E, Levin M (April 2005). 1282:Stochastic Gene Networks Simulator 813:are ultimately derived from basic 78:evolutionary developmental biology 27:Collection of molecular regulators 25: 2596:Two-Component Systems in Bacteria 2126:Mangan S, Alon U (October 2003). 597:corresponding substances at time 220:, and outputs being the level of 4342:Evolution of sexual reproduction 3599:10.1016/j.biosystems.2011.01.006 3480:Journal of Computational Biology 1602:Banf M, Rhee SY (January 2017). 848:are usually chosen as low-order 2625:Stress Response in Microbiology 2594:Gross R, Beier D, eds. (2012). 2469:Molecular Biology and Evolution 1532:Proceedings of BioLINK SIG 2013 1102:is the promoter region of gene 815:principles of chemical kinetics 465:ordinary differential equations 200:Example of a regulatory network 4113:Genotype–phenotype distinction 3954:Journal of Theoretical Biology 3937:. Cambridge, Mass: MIT Press. 3724:"Models for synthetic biology" 3722:Kaznessis YN (November 2007). 3622:Journal of Theoretical Biology 3521:Zabet NR, Chu DF (June 2010). 2889:Vohradsky J (September 2001). 2789:Journal of Theoretical Biology 2713:Journal of Theoretical Biology 1260: 1242: 1231: 1213: 1195: 1177: 1138: 971:of the concentration profile. 564: 558: 536: 530: 514: 508: 308:and operate within regulatory 1: 4370:Regulation of gene expression 3700:10.1093/bioinformatics/btm004 3006:. MIT Press. pp. 15–21. 2567:Bacterial Regulatory Networks 1292:design of synthetic systems. 416:Bacterial regulatory networks 4540:Endless Forms Most Beautiful 4320:Evolution of genetic systems 4128:Gene–environment correlation 4123:Gene–environment interaction 3974:10.1016/0022-5193(69)90015-0 3814:10.1371/journal.pone.0009134 3414:10.1016/0021-9991(76)90041-3 2857:10.1371/journal.pone.0111430 2809:10.1016/0022-5193(69)90015-0 2392:10.1371/journal.pgen.1003972 2342:10.1016/j.molcel.2009.11.017 2281:Journal of Molecular Biology 2236:Journal of Molecular Biology 2203:10.1016/j.molcel.2009.11.018 1621:10.1016/j.bbagrm.2016.09.003 1428:(5594). Young Lab: 799–804. 1399:10.1016/1357-2725(96)00039-8 4519:Christiane Nüsslein-Volhard 3202:10.1093/genetics/149.4.1633 2766:. Oxford University Press. 149:. Such signalling controls 4625: 4395:Hedgehog signaling pathway 4272:Developmental architecture 3894:10.1038/s41598-019-49124-x 3652:10.1016/j.jtbi.2011.06.021 2733:10.1016/j.jtbi.2008.11.026 2528:10.1038/s41467-019-10388-6 1773:Leclerc RD (August 2008). 1473:"Gene regulatory networks" 1313: 759:express the dependence of 455: 420:Regulatory networks allow 247:(ODEs), Boolean networks, 234:the behavior of the system 228:described below these are 4572: 4222:Transgressive segregation 3449:10.1088/1478-3975/3/4/005 2303:10.1016/j.jmb.2003.09.049 2258:10.1016/j.jmb.2005.12.003 1892:10.1016/j.cub.2011.02.040 1779:Molecular Systems Biology 1049:artificial neural network 4036:Engineered Gene Networks 3492:10.1089/cmb.2006.13.1630 2954:10.1162/1064546054407202 2623:Requena JM, ed. (2012). 2426:Nature Reviews. Genetics 1732:Nature Reviews. Genetics 1689:Nature Reviews. Genetics 1088:process to be complete. 1071:Stochastic gene networks 1061:cellular differentiation 1057:recurrent neural network 4400:Notch signaling pathway 4375:Gene regulatory network 4258:Dual inheritance theory 3259:10.1126/science.1105891 3104:10.1126/science.1070919 2153:10.1073/pnas.2133841100 1994:10.1126/science.1075090 1843:10.1126/science.1238016 1498:10.1073/pnas.0502024102 1442:10.1126/science.1075090 1063:and even multicellular 392:utilization systems of 331:Hippo signaling pathway 314:preferential attachment 280:Structure and evolution 257:Gaussian network models 191:gene regulatory network 4448:cis-regulatory element 4356:Control of development 4236:Non-genetic influences 4202:evolutionary landscape 3741:10.1186/1752-0509-1-47 3539:10.1098/rsif.2009.0474 2908:10.1074/jbc.M104391200 2661:Nucleic Acids Research 2629:Caister Academic Press 2600:Caister Academic Press 2571:Caister Academic Press 1267: 945: 922: 842: 807: 780: 753: 723: 611: 591: 571: 485: 375:utilization system of 357: 336:cis-regulatory element 245:differential equations 201: 45: 37: 4559:Nature versus nurture 4463:Cell surface receptor 4380:Evo-devo gene toolkit 4279:Developmental biology 4217:Polygenic inheritance 4143:Quantitative genetics 4056:16 March 2016 at the 2508:Nature Communications 2481:10.1093/molbev/msl060 1336:Cis-regulatory module 1268: 969:biochemical stability 946: 923: 843: 841:{\displaystyle f_{j}} 808: 806:{\displaystyle f_{j}} 781: 779:{\displaystyle S_{j}} 754: 752:{\displaystyle f_{j}} 724: 612: 592: 572: 486: 355: 218:transcription factors 199: 110:transcription factors 83:The regulator can be 43: 35: 4468:Transcription factor 4183:Genetic assimilation 4170:Genetic architecture 2764:The Origins of Order 2762:Kauffman SA (1993). 1649:Nature Biotechnology 1113: 1038:nodes of the model. 935: 882: 825: 790: 763: 736: 732:where the functions 624: 601: 581: 495: 475: 369:convergent evolution 153:, the building of a 97:transcription factor 4564:Morphogenetic field 4481:Influential figures 3966:1969JThBi..22..437K 3886:2019NatSR...912732G 3805:2010PLoSO...5.9134G 3774:"The DREAM Project" 3728:BMC Systems Biology 3644:2011JThBi.284...82Z 3591:2011BiSys.104...99C 3441:2006PhBio...3..274R 3406:1976JCoPh..22..403G 3355:2000Natur.403..339G 3304:2000Natur.403..335E 3251:2005Sci...309.2010R 3245:(5743): 2010–2013. 3155:10.1038/nature01546 3147:2003Natur.422..633B 3096:2002Sci...297.1183E 3090:(5584): 1183–1186. 2991:on 13 October 2007. 2901:(39): 36168–36173. 2848:2014PLoSO...9k1430L 2801:1969JThBi..22..437K 2725:2009JThBi.257..419C 2673:10.1093/nar/gkac540 2520:2019NatCo..10.2418X 2144:2003PNAS..10011980M 2138:(21): 11980–11985. 2052:10.1038/nature13668 2044:2014Natur.512..453B 1986:2002Sci...298..799L 1791:10.1038/msb.2008.52 1563:2017NatSR...742023A 1489:2005PNAS..102.4935D 1434:2002Sci...298..799L 1259: 1230: 1194: 1082:Gillespie algorithm 1042:Continuous networks 871:By solving for the 276:expression levels. 240:Mathematical models 167:structural proteins 147:signal transduction 4253:Genomic imprinting 3874:Scientific Reports 3035:. pp. 75–84. 1551:Scientific Reports 1316:Multiple sclerosis 1310:Multiple sclerosis 1263: 1245: 1216: 1180: 990:bifurcation theory 941: 918: 862:nonlinear dynamics 838: 803: 776: 749: 719: 607: 587: 567: 481: 358: 302:scale free network 202: 58:regulatory network 46: 38: 4581: 4580: 4514:Eric F. Wieschaus 4476: 4475: 4294:Pattern formation 4198:Fitness landscape 3944:978-0-262-02481-5 3857:. pp. 67–69. 3349:(6767): 339–342. 3298:(6767): 335–338. 3141:(6932): 633–637. 2773:978-0-19-505811-6 2667:(12): 6801–6819. 2638:978-1-908230-04-1 2609:978-1-908230-08-9 2580:978-1-908230-03-4 2475:(10): 1931–1936. 2038:(7515): 453–456. 1980:(5594): 799–804. 1837:(6155): 1238016. 1571:10.1038/srep42023 1240: 1205: 1169: 1163: 1128: 1119: 986:dynamical systems 944:{\displaystyle j} 910: 856:that serve as an 652: 610:{\displaystyle t} 590:{\displaystyle N} 484:{\displaystyle N} 356:Feed-forward loop 253:Bayesian networks 230:Boolean functions 16:(Redirected from 4616: 4524:William McGinnis 4493:Richard Lewontin 4488:C. H. Waddington 4360: 4337:Neutral networks 4087: 4080: 4073: 4064: 3985: 3948: 3916: 3915: 3905: 3865: 3859: 3858: 3852: 3843: 3837: 3836: 3826: 3816: 3784: 3778: 3777: 3770: 3764: 3763: 3753: 3743: 3719: 3713: 3712: 3702: 3678: 3672: 3671: 3637: 3617: 3611: 3610: 3576: 3567: 3561: 3560: 3550: 3518: 3512: 3511: 3486:(9): 1630–1639. 3475: 3469: 3468: 3429:Physical Biology 3424: 3418: 3417: 3389: 3383: 3382: 3363:10.1038/35002131 3338: 3332: 3331: 3312:10.1038/35002125 3287: 3281: 3280: 3270: 3230: 3224: 3223: 3213: 3196:(4): 1633–1648. 3181: 3175: 3174: 3130: 3124: 3123: 3075: 3069: 3068: 3062: 3053: 3047: 3046: 3044: 3024: 3018: 3017: 3015: 2999: 2993: 2992: 2980: 2974: 2973: 2947: 2927: 2921: 2920: 2910: 2886: 2880: 2879: 2869: 2859: 2827: 2821: 2820: 2784: 2778: 2777: 2759: 2753: 2752: 2710: 2701: 2695: 2694: 2684: 2652: 2643: 2642: 2620: 2614: 2613: 2591: 2585: 2584: 2559: 2550: 2549: 2539: 2499: 2493: 2492: 2464: 2458: 2457: 2421: 2415: 2414: 2404: 2394: 2385:(11): e1003972. 2370: 2364: 2363: 2353: 2321: 2315: 2314: 2296: 2276: 2270: 2269: 2251: 2242:(5): 1073–1081. 2231: 2225: 2224: 2214: 2182: 2176: 2175: 2165: 2155: 2123: 2117: 2116: 2080: 2074: 2073: 2063: 2023: 2014: 2013: 1969: 1963: 1962: 1944: 1920: 1914: 1913: 1903: 1871: 1865: 1864: 1854: 1822: 1813: 1812: 1802: 1770: 1764: 1763: 1727: 1721: 1720: 1684: 1675: 1674: 1664: 1662:10.1038/nbt.2596 1640: 1634: 1633: 1623: 1599: 1593: 1592: 1582: 1542: 1536: 1535: 1527: 1521: 1520: 1510: 1500: 1468: 1462: 1461: 1417: 1411: 1410: 1382: 1272: 1270: 1269: 1264: 1258: 1253: 1241: 1238: 1229: 1224: 1212: 1211: 1206: 1203: 1193: 1188: 1176: 1175: 1170: 1167: 1164: 1162: 1161: 1137: 1135: 1134: 1129: 1126: 1120: 1117: 1053:sigmoid function 1031:Boolean function 950: 948: 947: 942: 927: 925: 924: 919: 911: 909: 901: 900: 899: 886: 847: 845: 844: 839: 837: 836: 819:Michaelis–Menten 812: 810: 809: 804: 802: 801: 785: 783: 782: 777: 775: 774: 758: 756: 755: 750: 748: 747: 728: 726: 725: 720: 718: 714: 713: 712: 694: 693: 681: 680: 666: 665: 653: 651: 643: 642: 641: 628: 616: 614: 613: 608: 596: 594: 593: 588: 576: 574: 573: 568: 557: 556: 529: 528: 507: 506: 490: 488: 487: 482: 318:duplicated genes 21: 18:Genetic networks 4624: 4623: 4619: 4618: 4617: 4615: 4614: 4613: 4604:Systems biology 4594:Gene expression 4584: 4583: 4582: 4577: 4568: 4547: 4534:Sean B. Carroll 4472: 4404: 4351: 4315: 4267: 4248:Maternal effect 4231: 4164: 4101: 4091: 4058:Wayback Machine 3993: 3988: 3951: 3945: 3928: 3924: 3922:Further reading 3919: 3867: 3866: 3862: 3850: 3845: 3844: 3840: 3786: 3785: 3781: 3772: 3771: 3767: 3721: 3720: 3716: 3680: 3679: 3675: 3619: 3618: 3614: 3585:(2–3): 99–108. 3574: 3569: 3568: 3564: 3533:(47): 945–954. 3520: 3519: 3515: 3477: 3476: 3472: 3426: 3425: 3421: 3394:J. Comput. Phys 3391: 3390: 3386: 3340: 3339: 3335: 3289: 3288: 3284: 3232: 3231: 3227: 3183: 3182: 3178: 3132: 3131: 3127: 3077: 3076: 3072: 3060: 3055: 3054: 3050: 3026: 3025: 3021: 3001: 3000: 2996: 2982: 2981: 2977: 2932:Artificial Life 2929: 2928: 2924: 2888: 2887: 2883: 2842:(11): e111430. 2829: 2828: 2824: 2786: 2785: 2781: 2774: 2761: 2760: 2756: 2708: 2703: 2702: 2698: 2654: 2653: 2646: 2639: 2622: 2621: 2617: 2610: 2593: 2592: 2588: 2581: 2561: 2560: 2553: 2501: 2500: 2496: 2466: 2465: 2461: 2438:10.1038/nrg2192 2432:(10): 803–813. 2423: 2422: 2418: 2372: 2371: 2367: 2323: 2322: 2318: 2294:10.1.1.110.4629 2278: 2277: 2273: 2249:10.1.1.184.8360 2233: 2232: 2228: 2184: 2183: 2179: 2125: 2124: 2120: 2085:Nature Genetics 2082: 2081: 2077: 2025: 2024: 2017: 1971: 1970: 1966: 1929:Nature Genetics 1922: 1921: 1917: 1880:Current Biology 1873: 1872: 1868: 1824: 1823: 1816: 1772: 1771: 1767: 1744:10.1038/nrg2949 1729: 1728: 1724: 1701:10.1038/nrg1272 1686: 1685: 1678: 1642: 1641: 1637: 1601: 1600: 1596: 1544: 1543: 1539: 1529: 1528: 1524: 1470: 1469: 1465: 1419: 1418: 1414: 1384: 1383: 1379: 1375: 1362:Systems biology 1327: 1318: 1312: 1307: 1298: 1201: 1165: 1141: 1124: 1111: 1110: 1101: 1073: 1044: 1010:Stuart Kauffman 1006:Boolean network 1002: 1000:Boolean network 973:Critical points 933: 932: 902: 891: 887: 880: 879: 875:of the system: 828: 823: 822: 793: 788: 787: 766: 761: 760: 739: 734: 733: 704: 685: 672: 671: 667: 657: 644: 633: 629: 622: 621: 599: 598: 579: 578: 548: 520: 498: 493: 492: 491:nodes, and let 473: 472: 461: 454: 449: 418: 350: 287: 282: 265:Process Calculi 226:Boolean network 222:gene expression 175: 66:gene expression 28: 23: 22: 15: 12: 11: 5: 4622: 4620: 4612: 4611: 4606: 4601: 4596: 4586: 4585: 4579: 4578: 4573: 4570: 4569: 4567: 4566: 4561: 4555: 4553: 4549: 4548: 4546: 4545: 4544: 4543: 4531: 4526: 4521: 4516: 4511: 4510: 4509: 4498:François Jacob 4495: 4490: 4484: 4482: 4478: 4477: 4474: 4473: 4471: 4470: 4465: 4460: 4455: 4450: 4445: 4440: 4435: 4434: 4433: 4423: 4418: 4412: 4410: 4406: 4405: 4403: 4402: 4397: 4392: 4387: 4382: 4377: 4372: 4366: 4364: 4357: 4353: 4352: 4350: 4349: 4344: 4339: 4334: 4329: 4323: 4321: 4317: 4316: 4314: 4313: 4308: 4303: 4298: 4297: 4296: 4291: 4281: 4275: 4273: 4269: 4268: 4266: 4265: 4260: 4255: 4250: 4245: 4239: 4237: 4233: 4232: 4230: 4229: 4227:Sequence space 4224: 4219: 4214: 4209: 4204: 4195: 4190: 4185: 4180: 4174: 4172: 4166: 4165: 4163: 4162: 4157: 4156: 4155: 4145: 4140: 4135: 4130: 4125: 4120: 4115: 4109: 4107: 4103: 4102: 4092: 4090: 4089: 4082: 4075: 4067: 4061: 4060: 4048: 4043: 4038: 4033: 4027: 4021: 4015: 4009: 4004: 3999: 3992: 3991:External links 3989: 3987: 3986: 3960:(3): 437–467. 3949: 3943: 3925: 3923: 3920: 3918: 3917: 3860: 3838: 3779: 3765: 3714: 3693:(6): 777–779. 3687:Bioinformatics 3673: 3612: 3562: 3513: 3470: 3435:(4): 274–284. 3419: 3384: 3333: 3282: 3225: 3176: 3125: 3070: 3048: 3042:10.1.1.71.8768 3019: 3013:10.1.1.72.5016 2994: 2975: 2938:(3): 249–267. 2922: 2881: 2822: 2795:(3): 437–467. 2779: 2772: 2754: 2719:(3): 419–429. 2696: 2644: 2637: 2615: 2608: 2586: 2579: 2565:, ed. (2012). 2551: 2494: 2459: 2416: 2365: 2336:(5): 872–884. 2330:Molecular Cell 2316: 2287:(2): 197–204. 2271: 2226: 2197:(5): 894–899. 2191:Molecular Cell 2177: 2118: 2097:10.1038/ng1181 2091:(3): 264–266. 2075: 2015: 1964: 1915: 1886:(7): 527–538. 1866: 1814: 1765: 1738:(3): 204–213. 1722: 1695:(2): 101–113. 1676: 1655:(7): 615–622. 1635: 1594: 1537: 1522: 1463: 1412: 1393:(9): 965–974. 1376: 1374: 1371: 1370: 1369: 1364: 1359: 1354: 1349: 1344: 1338: 1333: 1326: 1323: 1314:Main article: 1311: 1308: 1306: 1303: 1297: 1294: 1274: 1273: 1262: 1257: 1252: 1248: 1244: 1236: 1233: 1228: 1223: 1219: 1215: 1210: 1200: 1197: 1192: 1187: 1183: 1179: 1174: 1160: 1157: 1154: 1151: 1148: 1144: 1140: 1133: 1123: 1096: 1072: 1069: 1043: 1040: 1035: 1034: 1027: 1024: 1021: 1018:directed graph 1001: 998: 940: 929: 928: 917: 914: 908: 905: 898: 894: 890: 854:Hill functions 835: 831: 800: 796: 773: 769: 746: 742: 730: 729: 717: 711: 707: 703: 700: 697: 692: 688: 684: 679: 675: 670: 664: 660: 656: 650: 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Berlin: 2563:Filloux AA 1785:(1): 213. 1373:References 1343:(database) 1296:Prediction 965:attractors 467:(ODEs) or 432:, such as 405:adaptation 327:Drosophila 306:pleiotropy 261:Stochastic 249:Petri nets 143:morphogens 102:structural 68:levels of 4458:Morphogen 4443:Engrailed 4426:Pax genes 4347:Tinkering 4193:Epistasis 4188:Dominance 4099:phenotype 3635:1408.1869 3037:CiteSeerX 3008:CiteSeerX 2940:CiteSeerX 2289:CiteSeerX 2244:CiteSeerX 1557:: 42023. 1347:Morphogen 1331:Body plan 1247:τ 1218:τ 1182:τ 1139:⟶ 699:… 543:… 447:Modelling 441:RegulonDB 390:arabinose 373:galactose 155:body plan 134:morphogen 130:chromatin 128:by which 4599:Networks 4421:Hox gene 4409:Elements 4390:Homeobox 4054:Archived 3933:(2001). 3931:Bower JM 3912:31484947 3833:20169069 3793:PLOS ONE 3760:17986347 3709:17267430 3668:14274912 3660:21723295 3607:21256918 3557:20007173 3500:17147485 3465:21456299 3457:17200603 3371:10659857 3328:41632754 3320:10659856 3277:16179466 3190:Genetics 3163:12687005 3120:10845628 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