The Experts below are selected from a list of 48528 Experts worldwide ranked by ideXlab platform
Jinde Cao - One of the best experts on this subject based on the ideXlab platform.
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mittag leffler stability and Generalized mittag leffler stability of fractional order Gene Regulatory Networks
Neurocomputing, 2015Co-Authors: Fengli Ren, Feng Cao, Jinde CaoAbstract:Gene Regulatory Networks have attracted much attention, and the Networks with integer-order have been well studied. Fractional-order Gene Regulatory Networks have been shown that it is more suitable to model Gene regulation mechanism, but has rarely been researched. In this paper, a class of fractional-order Gene Regulatory Networks is studied. Some criteria on the Mittag-Leffler stability and Generalized Mittag-Leffler stability are established by using the fractional Lyapunov method for these Networks. The existence of the equilibrium point is also considered. One illustrative example is provided to show the effectives of the obtained results.
Eric H. Davidson - One of the best experts on this subject based on the ideXlab platform.
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assessing Regulatory information in developmental Gene Regulatory Networks
Proceedings of the National Academy of Sciences of the United States of America, 2017Co-Authors: Isabelle S. Peter, Eric H. DavidsonAbstract:Gene Regulatory Networks (GRNs) provide a transformation function between the static genomic sequence and the primary spatial specification processes operating development. The Regulatory information encompassed in developmental GRNs thus goes far beyond the control of individual Genes. We here address Regulatory information at different levels of network organization, from single node to subcircuit to large-scale GRNs and discuss how Regulatory design features such as network architecture, hierarchical organization, and cis-Regulatory logic contribute to the developmental function of network circuits. Using specific subcircuits from the sea urchin endomesoderm GRN, for which both circuit design and biological function have been described, we evaluate by Boolean modeling and in silico perturbations the import of given circuit features on developmental function. The examples include subcircuits encoding positive feedback, mutual repression, and coherent feedforward, as well as signaling interaction circuitry. Within the hierarchy of the endomesoderm GRN, these subcircuits are organized in an intertwined and overlapping manner. Thus, we begin to see how Regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process.
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Implications of Developmental Gene Regulatory Networks Inside and Outside Developmental Biology
Current topics in developmental biology, 2016Co-Authors: Isabelle S. Peter, Eric H. DavidsonAbstract:The insight that the genomic control of developmental process is encoded in the form of Gene Regulatory Networks has profound impacts on many areas of modern bioscience. Most importantly, it affects developmental biology itself, as it means that a causal understanding of development requires knowledge of the architecture of Regulatory network interactions. Furthermore, it follows that functional changes in developmental Gene Regulatory Networks have to be considered as a primary mechanism for evolutionary process. We here discuss some of the recent advances in Gene Regulatory network biology and how they have affected our current understanding of development, evolution, and Regulatory genomics.
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evolution of Gene Regulatory Networks controlling body plan development
Cell, 2011Co-Authors: Isabelle S. Peter, Eric H. DavidsonAbstract:Evolutionary change in animal morphology results from alteration of the functional organization of the Gene Regulatory Networks (GRNs) that control development of the body plan. A major mechanism of evolutionary change in GRN structure is alteration of cis-Regulatory modules that determine Regulatory Gene expression. Here we consider the causes and consequences of GRN evolution. Although some GRN subcircuits are of great antiquity, other aspects are highly flexible and thus in any given genome more recent. This mosaic view of the evolution of GRN structure explains major aspects of evolutionary process, such as hierarchical phylogeny and discontinuities of paleontological change.
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emerging properties of animal Gene Regulatory Networks
Nature, 2010Co-Authors: Eric H. DavidsonAbstract:Gene Regulatory Networks (GRNs) provide system level explanations of developmental and physiological functions in the terms of the genomic Regulatory code. Depending on their developmental functions, GRNs differ in their degree of hierarchy, and also in the types of modular sub-circuit of which they are composed, although there is a commonly employed sub-circuit repertoire. Mathematical modelling of some types of GRN sub-circuit has deepened biological understanding of the functions they mediate. The structural organization of various kinds of GRN reflects their roles in the life process, and causally illuminates both developmental and evolutionary process.
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the evolution of hierarchical Gene Regulatory Networks
Nature Reviews Genetics, 2009Co-Authors: Douglas H. Erwin, Eric H. DavidsonAbstract:Comparative developmental evidence indicates that reorganizations in developmental Gene Regulatory Networks (GRNs) underlie evolutionary changes in animal morphology, including body plans. We argue here that the nature of the evolutionary alterations that arise from Regulatory changes depends on the hierarchical position of the change within a GRN. This concept cannot be accomodated by microevolutionary nor macroevolutionary theory. It will soon be possible to investigate these ideas experimentally, by assessing the effects of GRN changes on morphological evolution.
Fengli Ren - One of the best experts on this subject based on the ideXlab platform.
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mittag leffler stability and Generalized mittag leffler stability of fractional order Gene Regulatory Networks
Neurocomputing, 2015Co-Authors: Fengli Ren, Feng Cao, Jinde CaoAbstract:Gene Regulatory Networks have attracted much attention, and the Networks with integer-order have been well studied. Fractional-order Gene Regulatory Networks have been shown that it is more suitable to model Gene regulation mechanism, but has rarely been researched. In this paper, a class of fractional-order Gene Regulatory Networks is studied. Some criteria on the Mittag-Leffler stability and Generalized Mittag-Leffler stability are established by using the fractional Lyapunov method for these Networks. The existence of the equilibrium point is also considered. One illustrative example is provided to show the effectives of the obtained results.
Edward R. Dougherty - One of the best experts on this subject based on the ideXlab platform.
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Efficient experimental design for uncertainty reduction in Gene Regulatory Networks
BMC Bioinformatics, 2015Co-Authors: Roozbeh Dehghannasiri, Byung-jun Yoon, Edward R. DoughertyAbstract:Background An accurate understanding of interactions among Genes plays a major role in developing therapeutic intervention methods. Gene Regulatory Networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of Gene Regulatory Networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling Gene Regulatory Networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every Gene Regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced Networks. Conclusions Simulation results based on synthetic and real Gene Regulatory Networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/ .
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Efficient experimental design for uncertainty reduction in Gene Regulatory Networks
BMC Bioinformatics, 2015Co-Authors: Roozbeh Dehghannasiri, Byung-jun Yoon, Edward R. DoughertyAbstract:Background An accurate understanding of interactions among Genes plays a major role in developing therapeutic intervention methods. Gene Regulatory Networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of Gene Regulatory Networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.
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optimal experimental design for Gene Regulatory Networks in the presence of uncertainty
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2015Co-Authors: Roozbeh Dehghannasiri, Byung-jun Yoon, Edward R. DoughertyAbstract:Of major interest to translational genomics is the intervention in Gene Regulatory Networks (GRNs) to affect cell behavior; in particular, to alter pathological phenotypes. Owing to the complexity of GRNs, accurate network inference is practically challenging and GRN models often contain considerable amounts of uncertainty. Considering the cost and time required for conducting biological experiments, it is desirable to have a systematic method for prioritizing potential experiments so that an experiment can be chosen to optimally reduce network uncertainty. Moreover, from a translational perspective it is crucial that GRN uncertainty be quantified and reduced in a manner that pertains to the operational cost that it induces, such as the cost of network intervention. In this work, we utilize the concept of mean objective cost of uncertainty (MOCU) to propose a novel framework for optimal experimental design. In the proposed framework, potential experiments are prioritized based on the MOCU expected to remain after conducting the experiment. Based on this prioritization, one can select an optimal experiment with the largest potential to reduce the pertinent uncertainty present in the current network model. We demonstrate the effectiveness of the proposed method via extensive simulations based on synthetic and real Gene Regulatory Networks.
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Bayesian Robustness in the Control of Gene Regulatory Networks
2007 IEEE SP 14th Workshop on Statistical Signal Processing, 2007Co-Authors: Ranadip Pal, Aniruddha Datta, Edward R. DoughertyAbstract:The presence of noise and the availability of a limited number of samples prevent the transition probabilities of a Gene Regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in General are the min-max (worst case) approach and the Bayesian approach. The min-max control approach is at times conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we focus on the Bayesian approach for the control of Gene Regulatory Networks.
Yufeng Wang - One of the best experts on this subject based on the ideXlab platform.
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Reverse engineering Gene Regulatory Networks
IEEE signal processing magazine, 2009Co-Authors: Yufei Huang, Isabel María Tienda-luna, Yufeng WangAbstract:Statistical models for reverse engineering Gene Regulatory Networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the framework, we review many existing models for many aspects of Gene regulation; the pros and cons of each model are discussed. In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among the algorithms are provided. This survey has the potential to elucidate the development and future of reverse engineering Gene Regulatory Networks (GRNs) and bring statistical signal processing closer to the core of this research.
