Modeling Gene Regulation Networks Using Ordinary Differential Equations

Cao, Jiguo; Qi, Xin; Zhao, Hongyu

doi:10.1007/978-1-61779-400-1_12

Cited by 47 publications

(28 citation statements)

References 16 publications

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“…A positive value for means initiates the expression of and a negative value for means inhibits the expression of . Every gene regulatory network characterizes a dynamical system which can be represented by a set of differential equations [26], [27]. Let be the set of all attractors of the dynamical system of gene regulatory network .…”

Section: Description Of the Modelmentioning

confidence: 99%

Natural Biased Coin Encoded in the Genome Determines Cell Strategy

et al. 2014

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Decision making at a cellular level determines different fates for isogenic cells. However, it is not yet clear how rational decisions are encoded in the genome, how they are transmitted to their offspring, and whether they evolve and become optimized throughout generations. In this paper, we use a game theoretic approach to explain how rational decisions are made in the presence of cooperators and competitors. Our results suggest the existence of an internal switch that operates as a biased coin. The biased coin is, in fact, a biochemical bistable network of interacting genes that can flip to one of its stable states in response to different environmental stimuli. We present a framework to describe how the positions of attractors in such a gene regulatory network correspond to the behavior of a rational player in a competing environment. We evaluate our model by considering lysis/lysogeny decision making of bacteriophage lambda in E. coli.

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Section: Description Of the Modelmentioning

confidence: 99%

Natural Biased Coin Encoded in the Genome Determines Cell Strategy

et al. 2014

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“…Reconstruction methods use various approaches including Bayesian network inference [7], [8] and ordinary differential equations [9]. Because of the large amount of gene expression data that is available, many methods attempt to use transcript levels to reverse engineer a regulatory network [10].…”

Section: Introductionmentioning

confidence: 99%

Passing Messages between Biological Networks to Refine Predicted Interactions

et al. 2013

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Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

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“…These regulatory relations are manifested in the expression of the genes that can be considered as continuous real variables in an interval bounded by min/max expression values of the respective gene. This kind of formalism is used in differential equation related analysis [6,7]. However differential equation methods suffer heavily from the need to a great number of kinetic parameters that usually are not known to a satisfying degree of precision, especially for regulatory and signaling networks.…”

Section: Introductionmentioning

confidence: 99%

Dysregulated Signal Propagation in a MYC-associated Boolean Gene Network in B-cell Lymphoma

Çakır¹,

Wirth²,

Arakelyan³

et al. 2017

Biol Eng Med

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By the modern molecular biological approaches that exploit the availability of high quality gene expression data, it is made clear that flexible and robust responses of cellular programs are encoded in the relations between gene expression values. These relations naturally define a network where they stand for edges between the nodes that stand for the genes. The wiring of these networks often found to be dysregulated in cancer. Different system biological approaches that rely on correlations, differential equations and logical analysis are used to probe these relations in gene expression data especially. In our work we investigated selected biological functions in aggressive germinal center B-cell lymphoma in terms of a logical analysis of gene-regulation in Boolean space and a signal propagation algorithm considering network topology based on gene expression data. We especially aimed at studying the activity of the MYC gene as a key player. It is shown that the functional output of a gene network is affected by the states of the genes and also by the wirings between them. Our results support the key function of MYC in lymphoma biology. In addition, we showed that genes can alter functional output of the network by alternative mechanisms like reducing the variance in propagating signal and locking it to a certain level.

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Modeling Gene Regulation Networks Using Ordinary Differential Equations

Cited by 47 publications

References 16 publications

Natural Biased Coin Encoded in the Genome Determines Cell Strategy

Natural Biased Coin Encoded in the Genome Determines Cell Strategy

Passing Messages between Biological Networks to Refine Predicted Interactions

Dysregulated Signal Propagation in a MYC-associated Boolean Gene Network in B-cell Lymphoma

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