最近,公司北京国际数学研究中心的葛颢副教授与哈佛大学化学与生物化学系的谢晓亮教授和西雅图华盛顿大学应用数学系的钱纮教授合作,提出了细菌单细胞表型间的跃迁速率新理论,定量的刻画了基因在活跃程度不同的状态间切换的快慢是如何影响单细胞在不同表型间跃迁的速率。论文于今年的2月20日发表在《物理评论快报》(Physical Review Letters)上,通讯作者是葛颢副教授和谢晓亮教授。葛颢副教授和谢晓亮教授同时也都是北京老员工物动态光学成像中心的研究员。
单个细胞常常会具有多个表型,这对于细胞应对不可预知的环境变化是非常重要的,因此人们希望研究表型的稳定性及表型间的跃迁速率。过去十多年中单细胞实验技术的飞速发展,使得人们发现至少在细菌中,基因在活跃程度不同的状态间的切换既不像前人的某些模型里假设的那样,要比转录和翻译的速率还要快得多,也不像前人的另一些模型里假设的那样,比细胞分裂的周期还要慢得多。
因此,葛颢等着重研究了在这样一类最符合实际情况的中间情形中单细胞的动力学行为。他们首先提出了比完整的化学主方程模型更加简化的速率涨落模型,其中仅仅保留了基因在活跃程度不同的状态间切换的随机性;然后在该简化模型下得到了非平衡态的景观(landscape)函数,这类似于平衡态统计物理中用来刻画涨落的能量函数,定量刻画了每个细胞表型的内部涨落和不同细胞表型间跃迁速率的最高阶近似(leading order)。该表型间跃迁速率理论类似于化学反应速率中著名的Kramers理论,而且比以往的类似工作更为符合活细胞的实际且更加一般。
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Ly1PKfs/iiBv4bq2aP/gSNuH6M35VB4h41fw6f+ogw/wDIEtWNe/cx2d70+y3KMx/2W+Rv0bP4VcN7dyKm1+xqUtJWP4tnvrfwtqDaTbyz3zRGOBIhltzfKD+Gc/hUFlPwx4o03xNqmrNpsrOLZkjfcuM43DI9QcHmulry3wr4e13wt4209ZYreezaxFlcNZRsBHjLI8mTySQRkV6lTYkFZk02tCZxBZWDRAnYz3bqxHuPLOPzrTrA1DxZpqW1yllf2kt3G4gVfNGBK3ABPTjqfTFCV3ZA3ZXKiza1qmr+Z9i09odPYqFN2+1piOTny+doJHTqT6Vqefr3/Phpv/ga/wD8ao0670rTrGK2j1K1YIOWM65ZjyWPPUkk/jVn+2dN/wCghaf9/wBf8acnd6bCgrK73Mi5sNZupxM9pZiVSSrLfMCoKlcD910+Yn6mn2Fpq+mo6wWFh8+3cWvX7KFHAix0ArU/tnTf+ghaf9/1/wAaP7Z03/oIWn/f9f8AGpKMK3n1rTNXktvsFj5d8WmiX7Y21XAG8A+X3+9jH96tHRLnXJ7y+XV7K2gtkfFs8cpZnHfIx09Dxn070avc2V9ZFYNQtFuYmEsDGZfldenfoeh9iataTrFrq9pHNbzRl2XLRhwWQjgg49DxVy1XMRHR8px1x8QzpWv6vHJpE0lja3kUE90k6ZRmUKuIzyenavQK8j1jwBq2qeJ9dmtbSx824uopbfUHuWWS0xg5CgcnivTNQvm06ziRB9ovJcRwxngyPjqfQDqT2FTZt2KbUVcr33/E01SPTl5t7crNdehPVI/xI3H2A9a16qaXYf2faeWz+ZM7GSaUjmRz1P8AQDsABVynJ9FsiYJ7vdhRRRUlhRRRQAUUUUAFFFFABRRWP4omEWj+WAhluJooIg6BxuZwPunrgZP4UAbFFc9aavp9pp9rd2emvEl9KYUWNUBZhu255xhtpwenI6Vfhtp5L2edlkgSaEKP35Zg3+7gqCOmQT+NAHNXOdSs9f1kC2ZXlSztftWPL8qKQBjk8cyF/wAlrofETFbWzKywx/6db5Mq5BHmDgcH5j0B9e4qpq2lxab4K/s6zt0nigSKNY5n2hwHXlmyOe/Xk1b8RA/YrbaLYkXtv/x8Ebf9avTP8Xp74oA1qKq3GoW9reWlrKxE12zLEoBOdq7iT6AAdfcetULF5dQ1vUJZJSba0lWGBUJX5guXJ5+b74HP92gDZoorMtdOuY7q8mmuMif/AFYVn/ddiBk9OAfqT2oA06zJtSuE1qOzjtg0DFQ0pyMEq5OOMcBR3/iqcoLO0dri8ZEU7jKTjA+rZpYUFxEJIb6WSNujKUIP44oAt0VHFGYwQ0jyZ7tjj8gKkoAKKKKACiiigAooooAKKKKACiiigAooooAwvEfGo+Hj6al/7QlrT1O0F/plzan/AJbRsgPoSOD+dZfibi70A/8AUUX/ANFS1vU07O6E1dWZT0m7N9pNrcH70kYLezdx+eauVlaP/o91qFiekU5lQf7Enzf+hbx+FatOatJkwd4q5Rt/+Qte/wC7F/JqvVRt/wDkLXv+7F/JqvUS3KQVzNjoemXfiG9vItPtVhhYx8RDEkxHzvj2GF+u6tfWL2Szsf8ARwDdTMIYFPd26H6Dkn2BqbT7JNPsYraMkrGuNx6se5PuTk/jTWkb9yH70rdhn9kad/z4Wn/flf8ACsLxYLHSbK08uGztnuLuOIymGPCpnc/3hj7isPqRXU1ka54bs/EGwXxkKpDLEqrjCmQAFxkfeAHB7ZNQaE1tp2n3ECStpUMJYZ2SQIGH1xmpf7I07/nwtP8Avyv+FZN79vi1OJYDetbwCMOwBJkAJZz6E8KOnc4x20tLe7eS8a8V0PnYjU/dC7F+6ccjOfxzQA/+xdM/6B1n/wB+F/wrOfT00HUZNQsbNDbTgC5jgiG9COjqByRwMr7Ajvnepk0qQQySyHCRqWY+w5NOLsTKNzAt9fge/vRYwz3kjlPLWOMgH5ccseFAPXNaGnadKlw19qDrJeyLt+X7kK/3E/qep/IVjeHPEb32rTRzwuiXzefa5k37FEMLbSOxxIG44yT+PV1TmvsolQe8mFFFFQaBRRRQAUUUUAFFFFABRRRQAVDPaQ3LwvMgZoWLRkn7pIK5/IkfjU1FAGc2g6c1rBbG3/cQKixJvbCbSCuOeCMdetaAGAAOgpaKAMfxVD5/h25j+yvd5aP9yjFS3zr3Hp1/CneI0aTT4NsEc5F5bttd9oGJV+bORyOoHcjoelM8XIknhq6WSKeZS0eUgOHP7xenB/l0qPxk7w+G5riOxkvntpYp1gjbDMUkVhj6YzjvigCKyX+2PFWp3hLeRZR/2fAytj5zh5mHofuLn1U1uW1tHaQiKIEKCSSTksSckk9yTVLw9pr6TodtbTNvuMGSd/78rEs5/Fia06ACiiigCC7tIr2AwzgmMsrFfXBBwfbis5dN1G0hEFjeIsSnCiRQxx3ycdTzWxRQBHbrKkCLO4eUD5mHc+tSUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYHik4n0E/wDUUj/9Akrfrn/Ff39DPpqsP8mFdBQBk33+h67ZXfSOcG0lPufmQ/mCP+BVrVU1OyGoadNbbtjOPkf+4w5VvwIBpuk3x1DT0lddkykxzJ/ckXhh+f6Yq3rFPsQtJNdxLb/kLXv+7H/I1eqjbf8AIWvf92P+RqLWLqXEdhZttu7vKqw/5ZIPvSfgDx7kUW5nYOblVyKz/wCJprEt8eba03QW/ozf8tH/AE2j6N61sVDaWsVlaxW8C7YolCqPYVNSk7vQcFZa7hRRRUlBRRRQAUhAIIIyD2NLUaTxSSvEkiNJHjegYErnpkdqAOeTwpFY22rfZIlMlwjJarvP7pDGi7Rk4HzJnjsAOwrW0q2mtkuBKNqvKXReOBgdhwOQen86v0ySVIULyuqKMDcxwOeBQA+ioPtlubw2nnx/aAu8xbvm2+uKnoAKKKKACiiigAooooAKKKKACiiigDC8QanLATZw74mkRczKrEqGbaSMdNoyST7etXNHvZ76GR51RSjmPaqMDkE889iMEfWprtngjlna5EcMalm/d5wAOaLR3nAlW6WWPuAgGD6H0PtQBbrM8QXEtvpTGBZjIzKB5P3gM5Yj/gINLp9jfW1y0lzemeMqQE54OasmK4JOLofTyxQAyyvknKws4acRiRtsbKpB7jParlUE0+RLmS4WcCaQAM2zqB04zipJbe6eILHemNw2S4iByPTBoAt0Vi63HemxtLa1up1vJJ1VZ41wBjJZnA4xtB4PBOBUuiWd5aiUXjyuOPKMk5kO0jOD7g55/pQBq0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGB4s+7ox9NVt/5kVv1geLf9TpJ9NVtv8A0Ot+gArIu4J9Ov31CyiaaOUAXVun3mxwHX/aA4I7jHcVr1DeXAtLKe4KM4hjaTavVsDOB78VUZWJlHmMGHxBAdRvGt0e5kkEYihjU72IByGBA2Yz1NaemWEkDS3d6yvfXGPMK/dRR0RfYZ/E5Nc/4T1m4uNVninSJlvy12rRE/uyI4PlOeoxIMHjkGuvVldQysGU9CDkU3JbJEqDveTHUUUVBoYHjLUI7DR4xK8qCa4RC0WdwUHe5GOfuq3Pati0e4a2RrxIopj95I33BfbOBmmXenWt+QbqFZdqOg3f3WGGH4jiqN3pM7C1itDDHb2siNHEScMF9eM9cY/GgDVjljlBMbq4HBKnNYFh4q+2ao9k0EQYOAGS4BwD/eUgNn0KhlP96ruiaVJpkcglZGZlRRt9FHPPHUljj3rRNvCZUlMUfmRgqj7RlR6A9qAOem1OR/G5tLX5pIrYKUdmWPkhnOehYDy8Dr856CtCw01dMuZ7ie4jYvkBiu3ALs3JzyctjPsOKnbR7Frhbg2yGZZTOHOc7yoUn8gB+ArOudGvrm6eZ5IGMgi3AkjbsZm2g46Z289eDQBNJrcsuqSWdhbicQSRxzuSQF3AMeenCkH3JAA71ZvNL+12zQGYmN87hLls8EeoxjOfwFRad4fsrFopzBGbxY1R5VBG4gYzjpn364rVoAxYtA8i+W7e4eYooYxhApeTklt3oSTx+uOKsWp1JrwG5CrCN24Lgg8nbjvwMVpUUAFFFFABRRRQAUUUUAFFZmo6/Z6ZqNnY3Bk867WR12LkIka7mZvQdB9TWZa/ELQL+yu7ixuZLg2lu1y8SwssjRjqyhgNw+lAHTUVDaXUV9Zw3Vu4eGeNZI2HdSMg/kamoAytX0iTVJY8XCpEFKsjJuzllJxyMEgFc9s8VZ0ux/s+yEJZXcu8jMq7dxZixOPxq5RQAVg3OkzWt/Lq0AFzcguyxYxnIVVAPoAGJ9Sfat6igDEtdU1ea7jjn0kwwkjdJvzjOc8fl+ftW3RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVPUr99PhR47G7vCzbdlsFLL7ncw4q5RQBxHibX55rbT86Dq8ezUbZ8ukXOJBwMSHk9BWz/wk83/AELut/8AfqL/AOOVc1vTZNTt7aOJ1Qw3cNwS3cI4Yj6kCtKgDA/4SiYdfDuuf9+Y/wD4uj/hKZP+he1z/vwn/wAXW/RQBxNpeLpa6g1h4d1aKa7kLqws0URjaBtGG6DBP1NWdL1yXTopYDoetNB5haHFquVU8kEBvXOMdq62igDn/wDhLD/0Add/8BP/ALKj/hLB30PXB/25n/GugooAwP8AhLF/6Amuf+AR/wAaP+EtT/oDa5/4ANW/RQBgf8JbF30fXB/3D3NH/CXQ/wDQI1z/AMF0n+Fb9FAGD/wlsH/QL1v/AMFsv+FH/CW2/fTNbH/cNm/+JreooAwf+Ettv+gdrX/gtm/+Jo/4S6z/AOfHWP8AwWT/APxNb1FAGF/wl9l3s9YH10yf/wCIo/4S+x/59dX/APBXcf8AxFbtFAGF/wAJhp3/ADw1T/wWXH/xFZ+sfEXTtIihma01B4nlEblrSWMrnoRvUBjntnP1rrawtW1/SrHVYbS+glkvEjNxbqtq0rMM7SY8A8jPOOQPagDZgmW5t45owwSRQwDqVOD6g8j6GpKKKACiiigDhvEUTj4i2ZbkXmjXVtbr6yghiB7lf5Vy/h7RNW0KJr/U7O8htLPRZYLpr+SNwOCQsAUkhc9c1668MUjo8kaM8Z3IzKCVOMZHpxSywx3ETRTRpJGwwyOuQR7g0AYfgW2mtPA2iw3ORKtpHkHqMjIH4AgVm+JvEN9Za3dW9ldx29vY6VJfXDvEHG/OI1/HDcd8cV2GMcdqoPoOlyXcl1JYwPPIys7sgJYr93Prjt6UAc9/wn/kWDyXWk3S3EKxCWFCpO+QRFVUZzyZcD/dNOf4h2i2Mc6WFy8sjMBCGXdgTCEHOcHc54A6gE9q2LXw/aQNNPeBbu6lmSeWeVFBLIMIcDgbQOPz61zcOq+CbrUrexGm22Im2QStbL5andn5T1Hzc5xVxpynflV7ETqQhbmdrk8nxKtUuJohpd6/lu6q6bCsm2YQ5HPQsePXBFWtP8Xi6Eot7a6vGDPKQPLHlxea0akcgEEoxHchcntnY/4R7SPLEf8AZtrsARQvlDACMWUfgxJHuTTR4a0YIqDS7QKsflACIAbMk4+mSfzqCzMfxYl14O/tmCGeLznWKFEKNIXZwgwDxkMcEH0NSaJ4xh1vWZdPhs7iPy1lcTsVKOEk8vIwc4JBx9DWpcadpkeniK4trcWkD+eFdBtRgd2765yc+tUtP/sq1uoWg0w2TPGIIZWgCBlyWCA9RySQDjqaaTYnJLc5i4+Id1/aFjMlswsmkn3RRFXaaMSLDE2TjbukLEY6gV1WneI4tV1W5tbW2uDBb71a7ZcRF1baVB785/75PtmWPw3o0UYSPS7RUG07REMfKxZfyYk/U1Zg0uytvP8AItYo/tBJl2rjfnJOfqSfzNIZylp8TbO6aEHTL2NZTFtdtu3bI7KG69MIW9wCaln+I9nFZSXX9n3nleWZoWcBPOjVQSwz7sij1LgeuN8eHtIAQf2ba4QoVHlDgou1PyUkD2NRyeFtDmgigl0mzeKLIjRoQQucdP8AvkfkKAKGn+M4tQ8Q/wBkrYXKP5ksZmJUoDGqluhzwXC/Ws+78WzReJJllmaDTra5+z/uwjeZshMszvkZCqNo+XnP1rpV0Sxg+eztYLa4RJFimSJd0e87mI+rc+5qvZ+F9Ot7VI7iCO8lDSyNNOisztKcyHpgbs4x0wAO1AHP3PjmWTULOSO3urWzjWWaeOSIb7hMIkQUdi0kgUdOVPbmpIfE183hHX9akbASaWLT0UK2CuI1wQMNmTOPbFdA/hrRpAA+mWjAII/miB+XduA/PmpRoemLpxsFsLYWZbf5AjATdndnHrnmgDkj4/8A7HtXF7b3d/LGZld4xGq/udiOwAx8rSMQM85yPSn3/wAQgvmPaWd0ZYDMnkOUUSssiRLk8kZd8DH91s9K6dPDukRghNMtFBxkCIdn3j/x/wCb61la7beGNCtjc3+m2mZX4VYFLyNu3/8AoXOfWnGLk7LcUpKKu9h/iPxgnhiO2F3ZTXEstvLPIsBGIxGFznJHBLAD3qnJ8RbKOO5Y2V0HtjKXRtoOyPaC3XqXcIF67sjjFaGmXmh+LkkuUto5ZUVY5FmjG9V3BwD7blB+orQTQdKSYTLp9qJN5k3CIZ3FtxP13c/XmiUXF2ktRRkpLmi9Dl7rx2ra/ALZpF06BJDPhAzXDeYIYwo6jMm7B4yFJ6VpaBrl7rfiC6JjltbK3tYg1tMg3iZyzcnr9wIcf7YrR/4RfRA0jDSrMNJy7CIAk7t3X/e5+tW7XTbKxmlltbWGGSbHmMiAF8DAz+AApFFqiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKwrnwrFd6tBqUuoX/2m3meSFhIoCIww0QGPunA9+OtbtFABRRRQAUUUUAFFFFABSEZBHrS0lAGUfD1p5ry77kyPE0WWnduDn1PvXA23w21UaqkczRC0VwTOH5Kg9l65rf1aw1g6rfy6el4Fco5lDAMAGT5Y/mwQQCcEDkdeaS4XxDcec8kN4qRXCyQhfKMgADg7egIwU4Pqa7aVSdNPlktTgrQhUa5ovQ6SfRkuL43Ru7xCU2bI5yqj6AUf2Mn/P3f/wDgS1c8sXiaGWEqhTzphJcFArZbZGDkZ+7w/THP4VueII7kx2M9rFLN9mullkjiYBmTawOMkA9RxWDTTS5jpUk03yj7nSm/s2eGCaWWR9rL9olLjKkEDnoDjBqO4nm1NI7ZLO4hPmI8ryqAIwrBuD/EeMDFYlta+JbS0tRG8qiNQGjOx/veYWJJOSV+TjOKiUeJkVpnS/MkttGu1XjIVg5DE5HBxg4A7nJ4qlDzRm53+yzpzpEZJP2q+5Pa5f8Axo/seP8A5+r7/wACn/xrmJLPxBNJOjwzf6VCrTj5BGW8pRwc5Dbx06Yrb0FtZOoX39q7hFu/dLtXaPmONpHJG3b170pRcVfmLjNSduVlwaPGP+Xq+/8AAp/8aP7GiP8Ay8X3/gU/+NaNFZc0u5ryozv7Gh/5+L7/AMCn/wAaP7Gh/wCfi+/8Cn/xrRoo5pdw5V2M/wDsaHvPen/t6k/xpP7Gg/573v8A4FSf41o0Ucz7hyozX0O3eNkM97hgR/x9Sf41z/izwdNqWlWcWmyM8lnuCrNISXU4z8x78Cuk1qF7jR7qKMzh3jIBgx5n4ZIGfxrkoYfEdrE5SC7QPBGkcULLtjw7ZOGLENjBxz1PPArajKSfMparuYV4xa5HHR9iXwl4KmsLK9GrHa12oTy4pDlVHPLDvXSJoVpHGqq10AoA4uZB/WuZls/EE73EbwzYuYlaYfIELeWg4OchtwPHTFbegHWTfX39rbhHu/dKVXaPmP3SOo27eveqrOUm5uSJoqMUoKL+Zc/sWDtNej/t6k/xqza2a2gYJJO4b/nrKXx9M1Yormcmzq5UgorlfiBdzRaXp1nbzSQHUtSt7R5I2KsqM2WwRyMhcfjXKahrGt2kPijXIdcnQaRqJhis5FQwSJ8vyHjOTu6g0hnqtFcj4Uv528WeJNPleVokeC7iWRixi82PLJz0AI6dOa66gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqndapDZy7JUn2gZaRYmZE+pA/wA96ALlFRW1zHdwiWFi0bfdbGM+49qx/GniB/DHhS91SKNZJYgqxq33dzMFBPsCc0AVvEGt3enahJHbvHHGkEWWlXKqZJdm8+ygHuOtZ8HiPVbjVLazikhmjE0yvcRQgrMibPmALDA+YgkE8jin+D9Rh1hr24vtWg1O6toxHIBYvbNCjDcVZG5ZTjIOOx9a6eOW3jRBHbyKqjChbdgAPQcVM05NWdjejVhTg1KN3r+X6HPeH9b1S8/smW+ZSt+JflEQQABQysMEkjqMnHXpXW1nP9jWeO6a3lDwIyo3kthFOM4GMdhVgXsZAIjnwf8Api3+FV0SMpy5pyklZN6LsWaKr/bE/wCec/8A35b/AApFv4nQMqTlWGQfJbkflQSWaKr/AGxP+ec//flv8Kal/FIoZEnKnofJb/CgC1RVf7Yn/POf/vy3+FIt/E4yqTkZI/1LduD2oAs0VX+2J/zzn/78t/hTVv4nztSc7Tg/uW4P5UAWqKr/AGxP+ec//flv8KQX8TFgEnJU4P7luD19PegCxXHXfiK+S8lhF5a2cZnuAstwmVURBQE6jliS3rjpXVfbE/55z/8Aflv8KprHp/mzj7K7NJIJpFaBmG7GA2CMA4Hb0oesWiqclGalJXRzdv4t1S4vbaKaBII7uSCNGCcxsyKzqc+zZH0NdPoF7LqGhWdzP/rZIwXIGAT0J/HGakkkglVlaGYhup8ls9MZzjr70lrNbWkMdrbwzIkKBVQQt8q9B29qUVZNN3Lr1I1JJwjy6F6iq/2xP+ec/wD35b/CnRXUcshjUOrAbsOhXj8aZkZvifQj4g0pbeKcW9xDPHcQSldwSRGBBI7jqPxqvJ4G8PS6o2oy6akl08vnsXkdlMn97YTtz+FdBRQBj6LoJ0zVNY1CaYTXGpXAkJC42RqoVF684Gefetiq99ewabYz3l2+yCBDJIwBOAOTwOTWFa/EDQbyyvrmO4mUWO3z4nt3WRdxAXCYyckgcUAdLRWRoPifTPElvPNpszN9nfy5kkjZHjb3UjNXI9QWWNXSC52sMjMRH6GgC3RVb7YP+eFx/wB+6R79Y0Z3huAqjJPlmgC1RVb7YP8Anhcf9+6Ptg/54XH/AH7oAs0VUe/WNdzQ3AGQP9We/FO+2D/nhcf9+6ALNFVvtg/54XH/AH7pGv1TG6G4GSAP3Z6mgC1RVb7YP+eFx/37o+2D/nhcf9+6ALNFVWv1UqDDcAscD92eTjP9KX7YP+eFx/37oAs0VW+2D/nhcf8Afumm/RXVTDcbmzgeWeaALdFVvtg/54XH/fuj7YP+eFx/37oAs0VV+3qHCeTcbiCQPLPQf/rpftg/54XH/fugCzRVb7YP+eFx/wB+6WC8SeZ4gkqOgBIdCuQfQ96ALFFFFABXBa9repX6XJtLprGG01i308eWD5j7njDsxzgAh+BjtnPNd7WHrXhOx1pxIxktpjLHK8sB2tIYyCm7scYHPX0IoAk0LVZ7251Oyu1jM2nXAhMsYIWQFFcHBzg4bBGTyPepfEehQeJNCudMuneNJwMSJ95GBBVh9CBWfq2mXFnaRQ6JEY0KzMwiOCZyv7t2PU/N1J9s10S52jd170Ac1pPhdtFXVdQvdQk1HUr2ILNcPGsY2opCgKOB7+tdJH/q1+gqK9/48Lj/AK5t/KnwhhCm8gtgZIGKAHkZqC+v7XTLR7q9njggjGWkkbAFQaxq0Oj2Xnyq0kjsI4YU5eZz0VR6n9OSeBVCw0Oa7uk1PXyk14pzDbqd0NoPRR/E3q5/DArWEFbnnt+L9P8AMVyL+2tZ1UZ0TSligP3brUWMYYeqxj5iPrtpRZeKyN39r6UD/cFg+36Z8zNdFRVe2t8MUvlf8/8AgBY5z+0/EOmc6npUN9DnmXTXJce5ifk/gxPtWrper2OsW3nafcJKgO1gOGQ+jKeVPsavVjar4eS7uPt+ny/YdUQYW5Rc7x/dkXo6/XkdiKOanPSS5X3W3zX+X3MNUbNJjFZGj62buZ7DUIha6pCMyQ5yrr/z0jP8Sn8x0NbFZTg4O0h3uFJjFLRUgFJilooAKTHOaWigApMc5paKACq3/MT/AO2P/s1WaqAN/apyQV8ngAcjmgC3RRRQBU1W7ksNLubqG1lu5YkLJBEMtIewFedeHb7VNPs9d1y50DV7jxHdhXZHtSkXBCpGnOSFzknrge1eoUUAcP8ADu3eDTdSkvbLUodTupPtF5NeW/lLK7Z4jGT8q9Px96662uYEjtoGmjEzxBljLDcwA5IHU1PN/qX/AN0158/hvxBF45tvEUUVrewRwyiNBKUdU8oBIueBlsnI7sSe1AHfm5hW4W3M0YnZSyxlhuI9QOuKrwwmdpmeWYYkYAByBgVx9n4a1/8A4WRb+ILxbQ28kcqviQl4EKqEjx0OCDyODlj3Fak1tey6jdfYdHt3PnM32u6uSqk/IeEUEnBReuOlVGLk7IDoPsa/89p/+/poFko/5bXB+sprnk8LajsUG/sIcbflisWONu3GC0h/uL27Uf8ACG3BUK2svgDGFtYwB06cH0Her9nH+dfj/kK50P2Jf+e0/wD39NAslH/La4P1lNc+fB1yzFjrUpJ/6dYvQj09GP50h8GTkODrMuJM7v8ARouc5z2/2j+dHs4/zr8f8gudD9jX/ntP/wB/TQLJQP8AXXB+sprn38G3EgIbWZSG3Z/0WLnduz2772/OhvB1xIxMmtTnd1It4gf4u+3/AG2/Oj2cf51+P+QXOg+xr/z2n/7+mlsifs+GZmId1yxycBiBXP8A/CE8sf7a1DLZz8sXOSSf4PUmtrRrf7JpcVv5ry+UWQO+MkBiOcYFTKKjtJP7/wBUBdIzS0UVAwpMUtFABRRTXdY0Z3YKqjJJOABQAuOc0tctpd8viDxXJqFmJvsFpCYBN0SaTPIHrj1rqaqUeWw9Gk0wqsP+Qm3/AFxH8zVmqw/5Cbf9cR/M1IizRRRQAUUUUAFU9Vu7ix02a4s7KS+nQDZbxsFLkkDqeB1yT6CrlZniFdVfQ7lNBMI1F12xNMcKmTy3Q8gZI96AMrQfFZ8SWmsW8+nyWF5pzNDPC0gkAJBxhhwehroJ7qGxsXubqRYoYo97ux4UAcmuV8HaJqGg+Hr2z1GytYmZWke4juDM9zIQd7OSo56VbuEGv6wmlxqBpenFHu8D5ZZuGSL6Lwzf8BHrWlOHM9dluJsn0W1n1S9/t3Uo2jZlK2Ns45t4j/ER/fbAJ9BgetdBRRSnNzdwSsFFFFQMKKKKAM3WtHTVreMpIbe8t28y2uVGWhf+oPQr0IqPQ9YfUEmtryNYNStG2XMKk49nXPVGAyD9R1BrWrF17S55Hi1TSwo1S0UhATgXEZ5aJvY9j2OD61tCSkvZy+T7f8B/hv3un3NqiqelanBrGnQ3ltuCSDlWGGRhwysOxBBBHtVysmnF2e4wooopAFFFFABRRRQAVW/5if8A2x/9mqzWck0Z8RSwKgEi2qyM3qCxA/8AQTTSbA0aKKKQBRRRQAyb/Uv/ALpqOybfYwHDD92vDDB6VJN/qX/3TTLT/jzg/wCua/yoAmqtZZ/f5/57NirNVrLOJ84/1zYoAs0UUUAFFFFABRRRQAVWsc/ZjuIJ8yToMfxmrNVrHP2Y7gAfMk6f75oAs0UUUAFFFZepa7b6Ve20FyGVJw7eaThUCjJzTUXJ2Q0m9v6saE00dvE8szqkaDLMxwAK5FnuvHNwUhZ7fw8jYeQHD3hHZfRPU/l7OjiufHEyzXAeDQEbMcRyHvMdCfRP5/TmutjjSGNY40VEQbVVRgAegFX8HqYq89XsMtbWGyto7e2iSKGJQqIgwFHoKmoorPc1CqqNnVZBhhtiXkjg8npVqqw/5Cbf9cR/M0AWaKKKACiiigAooqpqmp2mjadNf38vlW0IBd9pbGTgcDk5JAoAkvf+PG4/65t/KksoIoLcCGBYA5MjKoAyx5JOO5NZmneJdL8S6TezaTc+csKskilGRkbB4KsARWzH/q1+gouA6iiigAooooAKKKKACiiigDmrz/imdaGoJ8umajKEvF7QzHAWX2DcK3vtPrXSVFdWsN7ay21zGskMqFHRujKeCKxvDdzLavPod9Iz3ViAYpH6z254R/cjBU+4z3rd/vIc3Vb+nf5bfcLZm/RRRWAwooooAKKKKACue0+Vp/HmsH+CC1t4e33vnc/o4roa5vw4fN1vW7j/AJ6Xjp+CKif0Na09ITfl+qEzpK5/VfEclh4gtLSOJHs12/bpj1h8w7IcfVgc+gxXQVUuLG0eG68y3hYTjM29AQ+BgbvXAArIZborifCGs61rWmaoj6hY3F9EqiCRFBiUsCQTjDEdDggfjVfw3r3ia/vfEGmrPp+oyae0ccF6YzFEZD99WC5zt56dx15oA7ub/Uv/ALprzuXxXqFp8QbOC8N1aaNFBKpDW5EcoSIOZt+OQCSOOgUetdB4G1m917wXDfak6Pcu0yuyLtB2uyjj6Ct+zKyWEBKnBjHDLg9PSgDldP18z/E2exXV1ubOXTknhgG0BHLZwMck7fm55w1dTZZ/f8Y/fNU4RQchQD9KqQzeQ0yvFNkysQVjJBB9xQBdoqv9tT/nlP8A9+W/wpq3yMP9TcDkjmJqALVFV/tqf88p/wDvy3+FNW+Vgf3NwOccwtQBaoqv9tT/AJ5T/wDflv8ACmrfIwz5NwPrE1AFqq1gSbYll2nzJMjOcfOabJqUEK7pRKi5A3NGQMk4Az9aZZ3dusD75o0KzMrKzAFWZiQCOxIIOPegC9RVVtSskkWNru3DvL5KqZBkvjO3HrgdKj1bV7XRrJrm8kCoOFUcs57ADuaFqJtJXZJqGoW2mWcl1eTLFDGMszVzlvp914tuY77V4mg0uNt1tZMOZvR5Pb0X8/eex0i61u9j1TX02Ih3WtgTlY/Rn9W9u316dLVXtsZ8rm7y27f5gAAAAMAUtFFSahRRRQAVWH/ITb/riP5mrNVUbOqyDDfLEvJHB5PSgC1RRRQAUUUUAFcxrF5r+iRahqLrFqdqin7LY21q3nFyw25bJyB3IHvXT0UAcB4AiYaRrVxeW9/Hqt6TcXklzaNAhZg2Fjz1C8/n71teLtfu/DWiW9xaxxyyO/ltJOGKJiNmBIX1KhR7sPpW9e/8eNx/1zb+VSQsrwoysGUgYIORQBx934wvjPafYhZiN7SS4ullVs2pQAFS24AneQuOOjc8VreHdeudY8PaffTWqrc3SsWjQlVXaSOM/T39iRzW5VafP261wf7+fyoAXzrn/n1/8iCk8+5yB9k69/MFWqKAK3nXP/Pr/wCRBSGe5yB9k69/MFWqKAK3nXP/AD6/+RBSGe5yB9k69/MHFWqKAK3nXP8Az6/+RBWJ4iiu91pqVpaH7fZMzRqrAmZMfvIvxUcf7QWukqpdOBfWSlwCXbCnq3ymrpzcJcyBq4+wvoNSsYLy1cPBMgdGHcGrFc5Y/wDEg8Ryac3FjqTPcWh7Ry9ZI/x++P8AgXpXRU6kFF6bPVf1+AkxaKKKzGFFFFACVzPg0tJZrcszH7U08+D23TMR+mK2tYvBp2jXt2ekEDyceyk1R8PWv2Cz0+yYjzbewjSRc5IbjP65rZaUn5tfhf8AzQupt1Rj1EXlldS2cEsjws8ao6mPzGXI+UnjGRjPSr1eeXXh6zs7zVrpNd1PTr2K482Q2bvtfzmzHmJsqxJO35eCR2rEZY8J6Pfy6HqdjdQ3Gl3k0SI12ItjtkHp8zL8uSMrjr0FXdB8E3vh3SZdPsvENwsJTbDi1iBhbcCX6fMTyPmz1rpdOgmttNtoLmYzzxxKkkp6yMBgt+J5qzQBzfhbwvL4U0qey/tSa9tjlokkjVfKJLFsEcnJPet60/484P8Armv8qfN/qX/3TTLT/jzg/wCua/yoAmooooAr38K3FhPFJPJbo8ZVpY32Mgx1Ddj71x3ws+zw6Vq9rbzB1h1a5CAyb22AgAk9T06967W4t4ru3kt7iNJYZVKPG4yrKeCCPSqOm+HdI0aV5dM0y0tJHXazQxBCR1wcUAaVFef2ura54fludY8TSXf9mLbvJ5AEbbHefEcYxgllQDvj5varUHxY0G4ZUjjvfNIYmNo1VlCoX5y3oDj3HuKAOk8QpYPoF4NXlMVh5ZM7hymF78jnnpxXIfCuyjj/ALXv7L/R9MvZleysTLvaKMAjeRklS3p7fSuvt5LHxRoMcktuk9jdoG8qYKwZc5GQCR2B/wDr0umeH9J0aSR9L020tHkADmCIIWHvigCTV7KTUtLntI5EiMy7C7xiQBT1+U8E4zjPGex6Vz934MkGgxaZYzWxWG9S7ikuYyzjawflgcsxIILdcHuea62svWdaTTFSGJDcX0/ywWyH5nPr7AdzTSuJtJanDf8ACOWXhDULW61OFdSu5JVFn9nUo6srO248/MzGTB9lFdbpei3FxerquuFXvRnyYFOY7Ye3q3qfyqXR9EeG4bUtTkE+pyjBYcpCP7ieg9T3rbptq1kFk0m1qFFFFSMKKKKACiiigAqsP+Qm3/XEfzNWarD/AJCbf9cR/M0AWaKKKACiiigAoorz74jazrA8K61bppNxaW6Kqi+85CsiGRQ3yqdwBUnqOmaAO2nuIbiwuTDLHIBGwJRgcce1WYxiNcegrz7wPcxRv4g0yCx0iMWsUbfadKB8qbchIBJJyw9c966TxL4l/wCEZ0m3uWtRNJMxRUMoRRiNnOWI9EOOOTigDfqpdDN7a4OG+fbz3x+tYVz4xkS4sUtLBblLy1e6GLgK8SqoJ3gjA5Kr16npxWhpeprrNlo+orEYxdwecEzu2blBxn26UAXtNuWu9Ot5pMb3Qb8f3uh/XNWqz9J+SO5hxjybmRR9Cd4/Rq0KqStJgFFFFSAUUVFcXEVpA807hI0GWJoAbeXcdlbmaXJAOFVRlnY9FA7k1nQ2rjULa8vdv2yVmAUNkRJtPyL69snufbFTWlvLd3Ivr1ChAP2eBv8AlkD/ABH/AGz+g49c2Lg/6XaDafvtzjp8pq37qsgK+u6UNY0t4FfyrhSJbeYdYpV5VvwPX1GRSaBq39saYs0ieVcxsYbmEnmKVeGX8+R6gg1p1zmpE+H9fTVgMWN6Ut77H/LN+kcv052H22ntWlP34+z67r9V8/z9RPTU6OikpawGFFFFAGD4y/eeHzaD715PDbD6PIoP6ZrUx/xM/wDtj/WsrWP9M8S6JY4ysTyXsg9Ai7F/8ekH5VqAt/apBUBRDwc9ea2npTivV/p+gluW65DxF9nTxro001veOkY/evAoaMEtiLzRnIVWJIIzg9a6+vP/AIjLD9s0+6uhZJbxKypPc28jozk8xtJGcx5AyDgjI9RWIzrtE1+w8Q20lxpsrSRI5Qs0bJn3GQMg9iOK0qxfC2mW+n6NA1qbkRTRo8cU8m/yE2jbGvH3R781tUAMm/1L/wC6aisW32MDYYfIOGGD0qWb/Uv/ALpplp/x5wf9c1/lQBNRRRQAUUUUAMkjSVdsiK6+jDIqI2FoWDG1gLA5B8sZqxRQA1I0iQJGqqq8BVGAKdSVz+uatdPeRabobB7/AHhpm2hkhT1f0z6daqEXJ2QK10r7lrWNaazkWysIvtOpTDMcIPCj++57KKXR9EFg0l1dS/atRn/11ww/8dUfwqPT86k0jRotKiZt7T3c3M9zJ9+U/wBAOw7VpUNrZERi780twoooqSwooooAKKKKACiiigAqoj7tVlXDDbEvJHB5PSrdVh/yE2/64j+ZoAs0UUUAFFFFABTJYo54nimRZI3BVkYZDA9QRT6KAM9dOs9L0m4g0+0gtYdjN5cMYRc464FWJrS3vbZYrqCKePg7JUDDI6HBpb3/AI8bj/rm38qfCoSFFBJwByTk/nQBB/Zdh+8/0K2/egq/7pfnBOSDxzk8/WmC3itLizhtYY4YV34SNQqrx2A461eqrOM31ryeN/8AKgCC3bydcu4jkefGky++Plb8sL+daNZ+pxMrW95EMvbPlgOpjIw4/kf+AiryOrorIQysMgg8EVUtbMB1FFVb3UIbEIJNzyycRwxjLyH2H9eg70km3ZAS3NzFaQPNO4SNByT/AJ5PtVG3t5dQnS8vkKRod1vbN/D/ALb/AO16D+H60ttYzXFwl5qW0yLzFApykPv/ALTe/bt6nSqr8ui3AKrXG77XaYA272zzz901ZqtcA/a7Q5OA7cevymoAs1Dd2kN9aTWtzGJIJkKSIejKRgipqKE2tUBg+Hbue2lm0PUJWku7JQ0UrHm4gJwr/Ufdb3Ge9b1Y/iDS57uOG904quqWRL27Ho+fvRt/ssOPY4Parek6pDq+nR3cG5d2VeNxhonHDIw7EHitqiUl7RfP1/4P+aEuxdooqnquoxaTpdzfTnEcEZcjuT2A9ycD8ayScnZDMzS/9O8Xave9Y7ZI7GNh6jLv+rqPwrW/5if/AGx/9mql4ZsJdO0C2iuv+PpwZrgn/nq5Lv8AqxH4VbCAasWy2Wh6E8dewrSs05tLZafdp+IlsW65e58A6aZpp9NudQ0u4mZnkezumAdicksjZU8n0rqKKyGQ2cDWtlBbtM8zRRqhlk+85AxuPuetTVXv1umsJ1sHiS6KERNKCUVuxIHUV5m3jvX9MtPEUc1zaak1jNDa216kPloZZDhlIBwdvP4j3oA9Rm/1L/7pplp/x5wf9c1/lXLeGdY1STWte0LWrmG7n05YnS5ih8resiZwVyQMV09i4exgYBh8g4YYPSgCxRRRQAUUUUAFJVPVdUg0i0+03W/ygwU7Rk5JxWTJd3PiaRrfTpHg0xTtmvF4ab1WP+rflVKDtd7CneKTtvsPvNVudUupNO0NwCh23F6RlIfVV/vP/KtPS9KttItRBbKcE7ndzl5G7sx7mprOzt9PtUt7WJYokGFVf88n3qehy6LYmMbavcKKKKksKxpr0wKrSHUZC7Pj7PCXAAYjHA4rZrOSW8jgX7LbRTAvJuLzFMfOf9k5pppbiZTGqoSB5etDJxzaN/8AE0611GK4nuIpbi5tjC2AZmRfMGSNy8dOP1FWxcapkZ0+2x3/ANKP/wARWdNcg3Uy65pysgyYGjiMnyZP3sZ56frQ5RfwqwJ3I9V8Q22l8mS+mQY3Sx7dgBzzuxzyMYHrUemeJYNT1NLKNrxHYE7i8ZHG7090b8Np6MKtX0uoRW6ppOn24hCq8RlUsMkEkFRyuPXnnjHNV9I1LVrjVYo7rSFt4CvMnklSDg98/wCyn/fWOqmkM3vsjf8AP1P+Y/wo+yN/z9T/AJj/AAqzRQBWa1YKT9qn4Hqv+FRWjtJcI7nLNbISffJq4/3G+lUNPYNMi4b5bWPkjg9enrQBo0UUUAFFFFAGNdeIbKXVJtCtb5YdYMZ8tWgdgh27gTwFOBzjNcEfEWtaX4B8Qu2pTXN9BrL2Ed3Ko3Iu5FyB0HU4HYmvUbmBbq1lgZnRZUKFo2KsARjII6H3rmLf4b6JBZX1oTfTW98D58c107gtkNv5/jyo+brQBR8O3F9Za/4n0G61C61C3s4IpYZrohpF3oSwJAGeeldBrfiS30DTYLmSGe4MzbI44gAzEIzn7xAGFRjUWmeGLHw5p+oG1e4mnuVLT3FzKZJZMKQMsewHStK+0qx1iyS31G1iuYQQwSRcjOOv6n86AMDUfiJpenzpH5VzMXtUuhsUdHBKKATyxCk47Dn1rQ07W7XWrLTtViV44JPMAD9VIJUjjI6g0+fwxoy+ZcLpVu84R8ELhjnJwD2+8fpk4rjNM8a39p4aCRadbWjrLEkQS1lEcMbKpkJTqSjvtJyMk59aAPQ/t9r/AM9lrNMn2SZvsF7H5bknyJlJVT32sOVHtyPTFUfCfiW+1q/u7bUYIoHit7eZY0RwVZ1JkVi3UqcDjpmuppp2Axjd3U4KveWtsp7xIXb8C2B+hqW0/s6xcskpeeT780hLO+PVvT26elZvjbxBqPh+ztpNMtUmeVnBLxu4yELKmF5y7AKD2zWLqfjzVItUuo7G1gNtFAzq0kMhORGTuyMAjzB5eByTzmm5PYDt/wC0LX/nstIdRtQQPOGT7GsDw/4oudW10Wcwg8ltOhulKRuGEjffQk8ccHHuK6mpArf2hbf89lqJ54bi9tTGwdlZjxngbTWH4n8S6jo2uafa2toklvOA0jtG7Fv3iqyqRwpVGZ8nPC1hXnxB1eKbUHgtLc20Ljyi8EudvmhVz6l0JcY6BTmgD0eiud8O6/caxrGsQM0D2lpIqQOsTRu3XdkMTkDgBhjJB4xXRUAFYOpaZd2N++raGiPPIALqzY7VugOjA9FkA4B6EcHsRvUVcJuDugaMCHxvoTqwub+OymT/AFkF5+5kQ+hVuv4ZqCOSTxbqFvOkbpolpIJkaRSpvJR90gHkRqecn7xx2HPRSW8MpUyRI5XoWUHFPrT2kI6wVn5u9vTRf156is+otVA6tqxUH5lh5496t1W/5if/AGx/9mrAZZooooAqarZSajpdzaRXUlq8yFBPF95M9x71yFr8Mlh8OT6HNrNxLYsoMKCCNDDIGDCQEDJORzk85ruqKAOe0HwwdDOpXl3fy6jqN/tM9zIipkKuFUKOAAK27T/jzg/65r/Knzf6l/8AdNMtP+POD/rmv8qAJqKKKACorm5hs7d57iRY4oxlnY4AFQ6jqVvpdsZrliATtVVGWduyqO5rNt9MuNWuEvdaULGh3QWOcrH6M/8Aeb26CqS6shyd+WO5Wa3n8WlWuo2g0cEMkLDElz6Fv7q+nc10cUSQxrHEioijCqowAPQCnUtJu44x5QooopFBRRRQAVmrBdzQKbW8FuA8m4GIPn5z71pVQhs4bmAGUOSrvja7L/GfQ0WvuHM46x/r8xgs9TBGdUUjPT7MP8aqxpqdlc3DQONQSVi215QvlHJ+UD05/SrNpBpt75jWsjSiJyjFZ3OGHUdazZ47bStTlisdQS0nuCryiSMuGZmO07ugJJPGabjyuw5Sm3aaS/r0RPew6zcQeYt7HZsSuIRt4IPIL89R6VkGx8Slww1eHZxx9oHT67fQx8/7LH+Pie8tNH1ZJFvNWtpZggNw25WQoCe3QdOSOePes8aH4clk3/28pKjqzR4O3d3I5HzN+Sj+EUhHerkKNxyccmlqhJrWmWu1Jb6BSdoUGQEnOMfnkUsmt6ZEqNJqFqgkBKFpVAYA4OOfXigC4/3G+lUbH/WRf9eqf1pDrmmPMbdb6Bpm2qEVwSS3TFLY/wCsi/69U/rQBoUUUUAFFFFABRRRQBHPGZreSMHBdSufTIqFReBQP9H4H+1WD4/1m80fQ7YadL5Nxe3sVms20MYg5OWAPBOBUPgrUtQk1bX9H1K8e9OmXCLFcSIqsyuu7B2gDjFAHSML0qdptwexw1KPtuOTb5/4FVmobyf7LZT3GN3lRs+PXAzQAz/TP+nf/wAepFF8FAY25bHJAYZrznw14j11tS8L3l/qbXUHiD7R5lqYlVICnK7CBnHbkmtb4lXmqaVZpfabrslpMdkNrYRQI7XMxbnk5J4I4HTHvQB2P+mf9O//AI9SKL4KNxtyfUBhT7JrhrC3N4qrcmNTKF6B8fNj2zmp6AK3+mf9O/8A49SKL4D5jbk+wYV554q8Q62+r+JjpupvZReH7eGVIliVhOzjcd5IJxjjiuo13xSdM8HW+pIEW8vkjjtUkIVfOkHy5J4AHJOewNAG5/pn/Tv/AO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(A) 速率涨落模型 (B) 非平衡态景观函数 (C) 速率公式 (D) 数值模拟验证
该新理论强调了单个DNA分子的随机行为对于单细胞的多个表型及其功能具有十分重要的影响。审稿人评价该文章为"excellent paper","a solid research and the scientific level of the manuscript is high"。
本研究得到国家自然科学基金委和教育部优秀博士论文基金的资助。
2014年7月,葛颢已经和谢晓亮课题组合作,利用随机数学模型和单分子酶动力学实验技术地结合,成功揭示了活细胞内的一个与DNA力学性质紧密相关的重要且普适的机制,即细菌内转录随机爆发现象(Transcriptional bursting)的分子机制。这种随机性是很多细胞和组织中细胞与细胞间基因表达量不同的主要根源之一。论文于2014年的7月17日发表在《细胞》(Cell)杂志上,且得到了Cell的同期评述。该研究表明,细菌里的转录爆发机制,就是来源于旋转酶分子与DNA分子的不断随机结合与解离,从而导致该DNA片段的超螺旋情况发生改变。
葛颢副教授2004年本科毕业于williamhill官网概率论与数理统计专业,2008 年于同一专业获得英国威廉希尔公司理学博士学位,师从钱敏教授。2010-2011年访问美国哈佛大学化学与化员工物学系。曾获得 2009 年中国数学会“钟家庆数学奖”( 优秀博士论文奖 ) 和 2010 年教育部全国优秀博士论文奖励。葛颢课题组致力于研究非平衡态统计物理的随机数学理论及其在具体生物化学系统中的应用,迄今已经发表国际期刊论文30多篇。
原文检索:
Hao Ge, Hong Qian and X. Sunney Xie. Stochastic Phenotype Transition of a Single Cell in an Intermediate Region of Gene State Switching. Physical Review Letters, 114, 078101 (2015)
DOI: 10.1103/PhysRevLett.114.078101
http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.078101