Minimum network constraint on reverse engineering to develop biological regulatory networks

Shao, Bin; Wu, Jiayi; Tian, Binghui; Ouyang, Qi

doi:10.1016/j.jtbi.2015.05.005

Cited by 4 publications

(6 citation statements)

References 24 publications

(31 reference statements)

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“…Because genes in the same cluster show similar temporal patterns, we hypothesized that they might share same upstream regulators. So we treated each cluster as a ‘meta-gene’ and generated its activation/inhibition time sequence via a threshold model ( S5B Fig ), which allows us to reconstruct the putative interactions among the clusters by analyzing the time trajectory using a Boolean network model [ 14 , 16 ]. A wide range of parameter values was used in the threshold model to investigate the robustness of our results ( S5B Fig ).…”

Section: Resultsmentioning

confidence: 99%

“…Well-established methods include statistical methods based on correlation and mutual information [ 9 , 10 ], ordinary differential equation (ODE) model [ 11 ], Bayesian networks [ 12 ] and Boolean network models [ 13 , 14 ]. Prior knowledge about the organization of the biological network can be further incorporated into the workflow to facilitate the reverse engineering process [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing the regulatory circuit of cell fate determination in yeast mating response

et al. 2017

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Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing the regulatory circuit of cell fate determination in yeast mating response

et al. 2017

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“…Minimal networks tend to outperform the candidate networks, and mainly distribute in the upper right corner of the figure. In our previous work [ 22 ], we have proposed that least number of regulatory edges is preferred to implement biological functions. Our results here provide further evidence and suggest that minimal network constraints may also be useful in the design of functional circuits.…”

Section: Resultsmentioning

confidence: 99%

“…This captures the combinatorial mechanism of transcription regulation, in which existence of an inhibitor can block transcription of the target gene. Although some limitations persist, our method is able to recover the regulatory network from different types of data in an efficient way [ 22 ]. The level of genes in the next time step is determined by the level of genes in the current time step by the following rule: Where θ (x) is a Heaviside step function with θ (x) = 1 for x > 0 and θ (x) = 0 for x < 0.…”

Section: Methodsmentioning

confidence: 99%

“…Minimal networks have been proposed to contribute to the core motif responsible for the main functional response [ 21 ]. Moreover, our previous work indicates that biological networks may prefer to use minimal networks to fulfill their functions, providing evidence that the edge number is limited in the evolutionary process [ 22 ]. To apply minimal network constraint in our approach, we enumerate all possible regulations of each node and obtain the ones with the fewest edges.…”

Section: Methodsmentioning

confidence: 99%

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From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

et al. 2015

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Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.

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Investigation of presence of endofungal bacteria in Rhizopus spp. ısolated from the different food samples

Birol

Günyar

2021

Arch Microbiol

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Minimum network constraint on reverse engineering to develop biological regulatory networks

Cited by 4 publications

References 24 publications

Reconstructing the regulatory circuit of cell fate determination in yeast mating response

Reconstructing the regulatory circuit of cell fate determination in yeast mating response

From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

Investigation of presence of endofungal bacteria in Rhizopus spp. ısolated from the different food samples

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