PPT-Full counting statistics of Markov chains applied to the kinetics of molecular motors

Intermediate presentation at the group seminar July 18th 2012 Maximilian Thaller 1 Contents Molecular Motors Connection between rates and cumulants Simulation Discussion of a special case. T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function Nimantha . Thushan. Baranasuriya. Girisha. . Durrel. De Silva. Rahul . Singhal. Karthik. . Yadati. Ziling. . Zhou. Outline. Random Walks. Markov Chains. Applications. 2SAT. 3SAT. Card Shuffling. C483 Spring 2013. Questions. 1. Enzymes . that join two substrates and require energy of a nucleoside triphosphate (such as ATP) to do so are called. A. ) . isomerases. .. B. ) . lyases. .. C. ) ligases.. Network. . Ben . Taskar. ,. . Carlos . Guestrin. Daphne . Koller. 2004. Topics Covered. Main Idea.. Problem Setting.. Structure in classification problems.. Markov Model.. SVM. Combining SVM and Markov Network.. First – a . Markov Model. State. . : . sunny cloudy rainy sunny ? . A Markov Model . is a chain-structured process . where . future . states . depend . only . on . the present . state, . . and Bayesian Networks. Aron. . Wolinetz. Bayesian or Belief Network. A probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).. Pratt & . Cornely. . Ch. 7. Enzyme Kinetics. How fast an enzyme catalyzed reaction goes. Why study enzyme kinetics?. Helps us understand mechanism of enzyme (how it works). Investigation of mutations in metabolic pathways. Pratt & . Cornely. . Ch. 7. Enzyme Kinetics. How fast an enzyme catalyzed reaction goes. Why study enzyme kinetics?. Helps us understand mechanism of enzyme (how it works). Investigation of mutations in metabolic pathways. Markov Models. A. AAA. : 10%. A. AAC. : 15%. A. AAG. : 40%. A. AAT. : 35%. AAA. AAC. AAG. AAT. ACA. . . .. TTG. TTT. Training. Set. Building the model. How to find foreign genes?. Markov Models. . Salehi. Marc D. Riedel. Keshab. K. Parhi. University of Minnesota, USA. . Markov Chain Computations. using . Molecular Reactions. 1. Introduction. Modeling of Molecular Systems. Mass-action Law. Stochastic . (part 1). 1. Haim Kaplan and Uri Zwick. Algorithms in Action. Tel Aviv University. Last updated: April . 15 . 2016. (Finite, Discrete time) Markov chain. 2. A sequence . of random variables.  . Each . Gordon Hazen. February 2012. Medical Markov Modeling. We think of Markov chain models as the province of operations research analysts. However …. The number of publications in medical journals . using Markov models. . CS6800. Markov Chain :. a process with a finite number of states (or outcomes, or events) in which the probability of being in a particular state at step n + 1 depends only on the state occupied at step n.. Markov processes in continuous time were discovered long before Andrey Markov's work in the early 20th . centuryin. the form of the Poisson process.. Markov was interested in studying an extension of independent random sequences, motivated by a disagreement with Pavel Nekrasov who claimed independence was necessary for the weak law of large numbers to hold..