An understanding of protein folding/unfolding processes has important implications for all biological processes, in- eluding protein degradation, protein translocation, aging, and diseases. All-atom molecular dynamics...An understanding of protein folding/unfolding processes has important implications for all biological processes, in- eluding protein degradation, protein translocation, aging, and diseases. All-atom molecular dynamics （MD） simulations are uniquely suitable for it because of their atomic level resolution and accuracy. However, limited by computational ca- pabilities, nowadays even for small and fast-folding proteins, all-atom MD simulations of protein folding still presents a great challenge. An alternative way is to study unfolding process using MD simulations at high temperature. High temper- ature provides more energy to overcome energetic barriers to unfolding, and information obtained from studying unfolding can shed light on the mechanism of folding. In the present study, a 1000-ns MD simulation at high temperature （500 K） was performed to investigate the unfolding process of a small protein, chicken villin headpiece （HP-35）. To infer the folding mechanism, a Markov state model was also built from our simulation, which maps out six macrostates during the folding/unfolding process as well as critical transitions between them, revealing the folding mechanism unambiguously.展开更多
In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Develo...In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over;seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.展开更多
The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilist...The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.展开更多
Based on suitable choice of states, this paper studies the stability of the equilibrium state of the EZ model by regarding the evolution of the EZ model as a Markov chain and by showing that the Markov chain is ergodi...Based on suitable choice of states, this paper studies the stability of the equilibrium state of the EZ model by regarding the evolution of the EZ model as a Markov chain and by showing that the Markov chain is ergodic. The Markov analysis is applied to the EZ model with small number of agents, the exact equilibrium state for N = 5 and numerical results for N = 18 are obtained.展开更多
Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built fr...Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11175068 and 11474117)the Self-determined Research Funds of CCNU from the Colleges Basic Research and Operation of MOE,China(Grant No.230-20205170054)
文摘An understanding of protein folding/unfolding processes has important implications for all biological processes, in- eluding protein degradation, protein translocation, aging, and diseases. All-atom molecular dynamics （MD） simulations are uniquely suitable for it because of their atomic level resolution and accuracy. However, limited by computational ca- pabilities, nowadays even for small and fast-folding proteins, all-atom MD simulations of protein folding still presents a great challenge. An alternative way is to study unfolding process using MD simulations at high temperature. High temper- ature provides more energy to overcome energetic barriers to unfolding, and information obtained from studying unfolding can shed light on the mechanism of folding. In the present study, a 1000-ns MD simulation at high temperature （500 K） was performed to investigate the unfolding process of a small protein, chicken villin headpiece （HP-35）. To infer the folding mechanism, a Markov state model was also built from our simulation, which maps out six macrostates during the folding/unfolding process as well as critical transitions between them, revealing the folding mechanism unambiguously.
文摘In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over;seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.
文摘The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60534080, 60774085, and 70771012)
文摘Based on suitable choice of states, this paper studies the stability of the equilibrium state of the EZ model by regarding the evolution of the EZ model as a Markov chain and by showing that the Markov chain is ergodic. The Markov analysis is applied to the EZ model with small number of agents, the exact equilibrium state for N = 5 and numerical results for N = 18 are obtained.
文摘Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.
