Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specif...Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.展开更多
Study of network dynamics is very active area in biological and social sciences. However, the relationship between the network structure and the attractors of the dynamics has not been fully understood yet. In this st...Study of network dynamics is very active area in biological and social sciences. However, the relationship between the network structure and the attractors of the dynamics has not been fully understood yet. In this study, we numerically investigated the role of degenerate self-loops on the attractors and its basin size using the budding yeast cell-cycle network model. In the network, all self-loops negatively suppress the node (self-inhibition loops) and the attractors are only fixed points, i.e. point attractors. It is found that there is a simple division rule of the state space by removing the self-loops when the attractors consist only of point attractors. The point attractor with largest basin size is robust against the change of the self-inhibition loop. Furthermore, some limit cycles of period 2 appear as new attractor when a self-activation loop is added to the original network. It is also shown that even in that case, the point attractor with largest basin size is robust.展开更多
This paper presents a design method of H<sub>2</sub> and H<sub>∞</sub>-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology ...This paper presents a design method of H<sub>2</sub> and H<sub>∞</sub>-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.展开更多
This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution pro...This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution procedure to solve nonlinear partial differential equation, the Hamilton-Jacobi-Isaacs equation with Successive Galrkin’s Approximation is sketched and validity is proved. According to Lyapunov’s theory, with solutions of the nonlinear PDEs, robust feedback control is designed. To confirm the performance and robustness of the designed controller, numerical and Monte-Carlo simulation results by Simulink software on MATLAB are provided.展开更多
Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a H...Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.展开更多
Cancer is a fetal and complex disease.Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments.Pathological differences are reflected...Cancer is a fetal and complex disease.Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments.Pathological differences are reflected in tissues,cells and gene levels etc.The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis.It is a huge challenge to understand all of these mechanistically and quantitatively.Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications.With the rapidly growing and available computing powers,researchers begin to integrate huge data sets,multi-dimensional data types and information.The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators.For example,the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle.Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks,which are essential for cancer study when the information and gene regulations are clear and available.In this review,we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks(ANNs),Decision Trees(DTs),Support Vector Machine(SVM)and naive Bayes.Then we describe a few typical models for building up gene regulatory networks such as Correlation,Regression and Bayes methods based on available data.These methods can help on cancer diagnosis such as susceptibility,recurrence,survival etc.At last,we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks.These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.展开更多
基金supported by grants from National Natural Science Foundation of China(Nos.61502198,61572226,61472161,61876069)。
文摘Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.
文摘Study of network dynamics is very active area in biological and social sciences. However, the relationship between the network structure and the attractors of the dynamics has not been fully understood yet. In this study, we numerically investigated the role of degenerate self-loops on the attractors and its basin size using the budding yeast cell-cycle network model. In the network, all self-loops negatively suppress the node (self-inhibition loops) and the attractors are only fixed points, i.e. point attractors. It is found that there is a simple division rule of the state space by removing the self-loops when the attractors consist only of point attractors. The point attractor with largest basin size is robust against the change of the self-inhibition loop. Furthermore, some limit cycles of period 2 appear as new attractor when a self-activation loop is added to the original network. It is also shown that even in that case, the point attractor with largest basin size is robust.
文摘This paper presents a design method of H<sub>2</sub> and H<sub>∞</sub>-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.
文摘This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution procedure to solve nonlinear partial differential equation, the Hamilton-Jacobi-Isaacs equation with Successive Galrkin’s Approximation is sketched and validity is proved. According to Lyapunov’s theory, with solutions of the nonlinear PDEs, robust feedback control is designed. To confirm the performance and robustness of the designed controller, numerical and Monte-Carlo simulation results by Simulink software on MATLAB are provided.
文摘Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.
基金supported by grants from The State Key Laboratory of Proteomics(SKLP-O200811)The National Natural Science Foundation of China (60603054, 30800200, 30621063)+2 种基金National Basic Research Program of China (2006CB910803, 2006CB910706, 2010CB912700)Hi-Tech Research and Development Program of China (2006AA02A312)National S&T Major Project (2008ZX10002-016, 2009ZX09301-002)~~
基金supported by National Nature Science Foundation of China Grants No.21721003.
文摘Cancer is a fetal and complex disease.Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments.Pathological differences are reflected in tissues,cells and gene levels etc.The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis.It is a huge challenge to understand all of these mechanistically and quantitatively.Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications.With the rapidly growing and available computing powers,researchers begin to integrate huge data sets,multi-dimensional data types and information.The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators.For example,the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle.Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks,which are essential for cancer study when the information and gene regulations are clear and available.In this review,we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks(ANNs),Decision Trees(DTs),Support Vector Machine(SVM)and naive Bayes.Then we describe a few typical models for building up gene regulatory networks such as Correlation,Regression and Bayes methods based on available data.These methods can help on cancer diagnosis such as susceptibility,recurrence,survival etc.At last,we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks.These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.
