The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jie Liang - One of the best experts on this subject based on the ideXlab platform.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
arXiv: Molecular Networks, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter ACME algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 10^5-10^6 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells towards better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
Frontiers in Genetics, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady-state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 105-106 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
Youfang Cao - One of the best experts on this subject based on the ideXlab platform.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
arXiv: Molecular Networks, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter ACME algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 10^5-10^6 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells towards better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
Frontiers in Genetics, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady-state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 105-106 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
David B Saakian - One of the best experts on this subject based on the ideXlab platform.
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Exact Probability distribution functions for parrondo s games
Physical Review E, 2016Co-Authors: Rubina Zadourian, David B Saakian, A KlumperAbstract:We study the discrete time dynamics of Brownian ratchet models and Parrondo's games. Using the Fourier transform, we calculate the Exact Probability distribution functions for both the capital dependent and history dependent Parrondo's games. In certain cases we find strong oscillations near the maximum of the Probability distribution with two limiting distributions for odd and even number of rounds of the game. Indications of such oscillations first appeared in the analysis of real financial data, but now we have found this phenomenon in model systems and a theoretical understanding of the phenomenon. The method of our work can be applied to Brownian ratchets, molecular motors, and portfolio optimization.
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Exact Probability distribution function for multifractal random walk models of stocks
EPL, 2011Co-Authors: David B Saakian, Araks Martirosyan, Zbigniew R StruzikAbstract:We investigate the multifractal random walk (MRW) model, popular in the modelling of stock fluctuations in the financial market. The Exact Probability distribution function (PDF) is derived by employing methods proposed in the derivation of correlation functions in string theory, including the analytical extension of Selberg integrals. We show that the recent results by Y. V. Fyodorov, P. Le Doussal and A. Rosso obtained with the logarithmic Random Energy Model (REM) model are sufficient to derive Exact formulas for the PDF of the log returns in the MRW model.
Anna Terebus - One of the best experts on this subject based on the ideXlab platform.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
arXiv: Molecular Networks, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter ACME algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 10^5-10^6 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells towards better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
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Exact Probability landscapes of stochastic phenotype switching in feed forward loops phase diagrams of multimodality
Frontiers in Genetics, 2021Co-Authors: Anna Terebus, Farid Manuchehrfar, Youfang Cao, Jie LiangAbstract:Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the Exact steady-state Probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the Exact topological features of their high-dimensional Probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 Probability landscapes, where each landscape resides over 105-106 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
N C Beaulieu - One of the best experts on this subject based on the ideXlab platform.
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accurate error rate performance analysis of ofdm on frequency selective nakagami m fading channels
IEEE Transactions on Communications, 2006Co-Authors: Julian Cheng, N C BeaulieuAbstract:Error rates of orthogonal frequency-division multiplexing (OFDM) signals in multipath slow fading Nakagami-m fading channels are considered. The Exact Probability density function of a sum of Nakagami-m random phase vectors is used to derive a closed-form expression for the error rates of OFDM signals. The precise error-rate analysis is extended to a system using multichannel reception with maximal ratio combining. An asymptotic error-rate analysis is also provided. For a two-tap channel with finite values of Nakagami-m fading parameters, our analysis and numerical results show that the asymptotic error-rate performance of an OFDM signal is similar to that of a single carrier signal transmitted over a Rayleigh fading channel. On the other hand, our analysis further shows that a frequency-selective channel that can be represented by two constant taps has similar asymptotic error-rate performance to that of a one-sided Gaussian fading channel. It is observed that, depending on the number of channel taps, the error-rate performance does not necessarily improve with increasing Nakagami-m fading parameters.
