open documents

google photos album

roam

sessions

• evolution of multicelluarity and cancer evolution

  • Trigos, A. S., Pearson, R. B., Papenfuss, A. T., & Goode, D. L. (2017). Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors. Proceedings of the National Academy of Sciences of the United States of America, __114__(24), 6406–6411. https://doi.org/10.1073/pnas.1617743114
  • Trigos, A. S., Pearson, R. B., Papenfuss, A. T., & Goode, D. L. (2019). Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer. ELife, __8__(e40947). https://doi.org/10.7554/eLife.40947
  • Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, __144__(5), 646–674. https://doi.org/10.1016/j.cell.2011.02.013
  • Trigos, A. S., Pearson, R. B., Papenfuss, A. T., & Goode, D. L. (2018). How the evolution of multicellularity set the stage for cancer. British Journal of Cancer, __118__(2), 145–152. https://doi.org/10.1038/bjc.2017.398
  • Fibration symmetries uncover the building blocks of biological networks by Flaviano Morone and Hernan Makse in 2020
  • Circuits with broken fibration symmetries perform core logic computations in biological networks by Ian Leifer, Flaviano Morone, and Hernan Makse in 2020

• stochastic processes for cancer evolution

  • historical

    • Armitage, P., & Doll, R. (1954). The Age Distribution of Cancer and a Multi-stage Theory of Carcinogenesis. British Journal of Cancer, __8__(1), 1–12. https://doi.org/10.1038/bjc.1954.1

  • tutorials on solving master equations with generating functions

    • A kinetic view of statistical physics by Pavel Krapivsky, Sidney Redner, and Eli Ben-Naim in 2010
    • Walczak, A. M., Mugler, A., & Wiggins, C. H. (2012). Analytic Methods for Modeling Stochastic Regulatory Networks. In X. Liu & M. D. Betterton (Eds.), Computational Modeling of Signaling Networks (Vol. 880). Totowa, NJ: Humana Press. https://doi.org/10.1007/978-1-61779-833-7

  • branching processes

    • Armitage, P., & Doll, R. (1954). The Age Distribution of Cancer and a Multi-stage Theory of Carcinogenesis. British Journal of Cancer, __8__(1), 1–12. https://doi.org/10.1038/bjc.1954.1
    • Karlin, S., & McGregor, J. (1957). The Classification of Birth and Death Processes. Transactions of the American Mathematical Society, __86__(2), 366. https://doi.org/10.2307/1993021
    • Exact solution of a two-type branching process clone size distribution in cell division kinetics by Antal and Krapivsky in 2010
    • Branching process models of cancer by Durrett in 2015
    • Cheek, D., & Antal, T. (2018). Mutation frequencies in a birth–death branching process. The Annals of Applied Probability, __28__(6), 3922–3947. https://doi.org/10.1214/18-AAP1413
    • Bozic, I., Paterson, C., & Waclaw, B. (2019). On measuring selection in cancer from subclonal mutation frequencies. PLOS Computational Biology, __15__(9), e1007368. https://doi.org/10.1371/journal.pcbi.1007368

    • applied to real data

      • Williams, M. J., Zapata, L., Werner, B., Barnes, C. P., Sottoriva, A., & Graham, T. A. (2020). Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios. ELife, 9, 661264. https://doi.org/10.7554/eLife.48714
      • The evolutionary dynamics and fitness landscape of clonal hematopoiesis by Caroline Watson, Daniel Fisher, Jamie Blundell, et al in 2020
      • Cheek, D., & Antal, T. (2020). Genetic composition of an exponentially growing cell population. Stochastic Processes and Their Applications, (xxxx). https://doi.org/10.1016/j.spa.2020.06.003

      • Subclonal reconstruction of tumors by using machine learning and population genetics by Giulio Caravagna, Marc Williams, Trevor Graham, and Andrea Sottoriva in 2020

        • Kessler, D. A., & Levine, H. (2013). Large population solution of the stochastic Luria-Delbruck evolution model. Proceedings of the National Academy of Sciences, __110__(29), 11682–11687. https://doi.org/10.1073/pnas.1309667110

  • Erwin Frey

    • Evolutionary game theory theoretical concepts and applications to microbial communities by Erwin Frey in 2010
    • Wienand, K., Frey, E., & Mobilia, M. (2018). Eco-evolutionary dynamics of a population with randomly switching carrying capacity. Journal of The Royal Society Interface, __15__(145), 20180343. https://doi.org/10.1098/rsif.2018.0343
    • Wienand, K., Frey, E., & Mobilia, M. (2017). Evolution of a Fluctuating Population in a Randomly Switching Environment. Physical Review Letters, __119__(15), 1–6. https://doi.org/10.1103/PhysRevLett.119.158301

  • diffusion approximation

    • Fate of a mutation in a fluctuating environment by Cvijovic, Good, and Desai in 2015

      • Ewens, W. J. (2004). Mathematical Population Genetics 1: Theoretical Introduction (2nd ed., Vol. 27). Springer.
    • The frequency-dependent Wright–Fisher model diffusive and non-diffusive approximations by Fabio Chalub and Max Souza in 2014

  • statistical physics and related mathematics

    • Population Genetics and Evolution by Joachim Krug in 2018
    • Wilf, H. S. (2005). Generatingfunctionology (3rd ed.).
    • Intrinsic and extrinsic thermodynamics for stochastic population processes with multi-level large-deviation structure by Eric Smith in 2020
    • Manzano, D. (2019). A short introduction to the Lindblad Master Equation, 1–28. https://doi.org/10.1063/1.5115323
    • Fisher, D. S. (2013). Asexual evolution waves: fluctuations and universality. Journal of Statistical Mechanics: Theory and Experiment, __2013__(01), P01011. https://doi.org/10.1088/1742-5468/2013/01/P01011

• cancer evolution

  • The evolutionary dynamics and fitness landscape of clonal hematopoiesis by Caroline Watson, Daniel Fisher, Jamie Blundell, et al in 2020
  • Subclonal reconstruction of tumors by using machine learning and population genetics by Giulio Caravagna, Marc Williams, Trevor Graham, and Andrea Sottoriva in 2020
  • Growth dynamics in naturally progressing chronic lymphocytic leukaemia by Michaela Gruber, Gad Getz, and Catherine Wu in 2020
  • Measuring Clonal Evolution in Cancer with Genomics by Williams, Sottoriva, and Graham in 2019
  • ~Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases by Zheng Hu, Christina Curtis, et al in 2020~
  • Between-region genetic divergence reflects the mode and tempo of tumor evolution by Ruping Sun, Zheng Hu, Andrea Sottoriva, Trevor Graham, and Christina Curtis in 2017

• evolution in fluctuating environments <2020-07-31 Fri>

  • Model reduction methods for classical stochastic systems with fast-switching environments reduced master equations, stochastic differential equations, and applications by Hufton, Lin, and Galla in 2018
  • Fate of a mutation in a fluctuating environment by Cvijovic, Good, and Desai in 2015
  • Master equations and the theory of stochastic path integrals by Markus Weber and Erwin Frey in 2017
  • Statistical Physics of Complex Systems by Eric Bertin in 2016

• applying probabilistic inference, active inference, and the free energy principle <2020-07-13 Mon>

  1. probabilistic inference
     1. turing.jl “tutorials”
     2. Probabilistic Modeling and Statistical Inference by Michael Betancourt in 2019
     3. Towards A Principled Bayesian Workflow by Michael Betancourt in 2020
     4. Data analysis using regression and multilevel hierarchical models by Andrew Gelman and Jennifer Hill in 2006
     5. Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari in 2020
     6. Bayesian data analysis by Andrew Gelman, Aki Vehtari, et al in 2014
     7. Statistical rethinking by Richard McElreath in 2020
     8. Du Phan’s implementation of Statistical rethinking in pyro
     9. Pattern recognition and machine learning by Chris Bishop in 2006
     10. Bayesian reasoning and machine learning by David Barber in 2012
     11. An introduction to probabilistic programming by Jan-Willem van de Meent, Frank Wood et al in 2018
     12. Michael Betancourt’s blog betanalpha
  2. free energy principle
     1. Geometry of Friston’s active inference by Martin Biehl in 2018
     2. A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
     3. The free energy principle for action and perception A mathematical review by Christopher Buckley, Anil Seth, et al in 2017
     4. Active inference on discrete state-spaces - a synthesis by Lancelot Da Costa, Thomas Parr, Karl Friston et al in 2020
     5. Expanding the Active Inference Landscape More Intrinsic Motivations in the Perception-Action Loop by Martin Biehl, Daniel Polani, et al in 2018
     6. A free energy principle for a particular physics by Karl Friston in 2019
  3. stochastic processes
     1. Master equations and the theory of stochastic path integrals by Markus Weber and Erwin Frey in 2017
     2. What Is a Macrostate? Subjective Observations and Objective Dynamics by Cosma Shalizi and Chris Moore in 2003
     3. Introduction to dynamical large deviations of Markov processes by Hugo Touchette in 2018
  4. information geometry
     1. Information geometry by Nihat Ay, Jurgen Jost, et al in 2017
     2. Information Geometry and Population Genetics by Julian Hofrichter, Jurgen Jost, and Tat Dat Tran in 2017
     3. The information geometry of 2-field functional integrals by Eric Smith in 2019
     4. Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding by Han Teoh, Katherine Quinn, James Sethna et al in 2020
     5. Theoretical investigations of an information geometric approach to complexity by Sean Ali and Carlo Cafaro in 2017
     6. Relating Fisher Information to order parameters by Mikhail Prokopenko, Rosalind Wang, et al in 2011
  5. approximate bayesian computation
     1. GpABC a Julia package for approximate bayesian computation with Gaussian process emulation by Evgeny Tankhilevich, Michael P. H. Stumpf et al in 2020
     2. approximate bayesian computation scheme for parameter inference and model selection in dynamical systems by Tina Toni, Michael P. H. Stumpf et al in 2009
     3. A framework for parameter estimation and model selection from experimental data in systems biology using approximate bayesian computation by Juliane Liepe, Michael P. H. Stumpf et al in 2014
     4. Efficient exact inference for dynamical systems with noisy measurements using sequential approximate bayesian computation by Yannik Schalte and Jan Hasenauer in 2020
  6. [[Bayesian probabilistic inference for stochastic processes
  7. invariant measures of random dynamical systems as analogs to attractors in deterministic dynamical systems
  8. applications of stochastic processes
     1. Clustering gene expression time series data using an infinite Gaussian process mixture model by Ian McDowell, Timothy Reddy, Barbara Engelhardt, et al in 2018
     2. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters by James Hensman, Neil Lawrence, and Magnus Rattray in 2013

• geometry of physics <2020-07-18 Sat>

  1. Physics and geometry by Edward Witten in 1986
  2. Geometry and Physics by Jurgen Jost in 2009
  3. A Visual Introduction to Differential Forms and Calculus on Manifolds by Fortney in 2018
  4. The geometry of physics by Theodore Frankel in 2012
  5. A thorough introduction to the theory of general relativity by Frederic Schuller in 2015
  6. Catalogue of Spacetimes by Mueller and Grave in 2009
  7. Lectures on Geometrical Anatomy of Theoretical Physics by Frederic Schuller in 2015
  8. representation theory
     1. Notes on representation theory and quantum mechanics by Noah Miller in 2018
     2. The unitary representations of the Poincare group in any spacetime dimension by Xavier Bekaert and Nicolas Boulanger in 2006
     3. Group theory in a nutshell for physicists by Anthony Zee in 2016
  9. Physics from Symmetry by Jakob Schwichtenberg in 2015
  10. The Structure and Interpretation of the Standard Model by Gordon McCabe in 2007
  11. Gauge Theories in Particle Physics A Practical Introduction by Aitchison and Hey in 2013
  12. gauge gravitation theory
  13. SageManifolds

• philosophy <2020-07-19 Sun>

  • pragmatism and process philosophy

    • Nicholas Rescher
    • process philosophy
    • Pragmatism, Objectivity, and Experience by Steven Levine in 2019
    • Consciousness and intentionality in the Stanford Encyclopedia of Philosophy

  • Bayesian brain hypothesis and the prediction error minimization framework

    • Whatever next? Predictive brains, situated agents, and the future of cognitive science by Andy Clark in 2013
    • The Self-Evidencing Brain by Jakob Hohwy in 2016
    • Intelligence and spirit by Reza Negarestani in 2018

screenshots

mendeley

<2020-06-30 Tue>

• 

  • not yet in roam

    • Saakian, D. B., Vardanyan, E., & Yakushkina, T. (2020). Evolutionary model with recombination and randomly changing fitness landscape. Physica A, 541, 123091. https://doi.org/10.1016/j.physa.2019.123091
    • Saakian, D. B., Yakushkina, T., & Koonin, E. V. (2019). Allele fixation probability in a Moran model with fluctuating fitness landscapes. Physical Review E, __99__(2), 022407. https://doi.org/10.1103/PhysRevE.99.022407
    • Dawson, D. A. (2017). Introductory Lectures on Stochastic Population Systems. Retrieved from https://arxiv.org/pdf/1705.03781.pdf Introductory Lectures on Stochastic Population Systems by Donald Dawson in 2017
    • Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science, __3__(JUN), 42. https://doi.org/10.3389/fenvs.2015.00042
    • Ferguson, J. M., & Buzbas, E. O. (2018). Inference from the stationary distribution of allele frequencies in a family of Wright–Fisher models with two levels of genetic variability. Theoretical Population Biology, 122, 78–87. https://doi.org/10.1016/j.tpb.2018.03.004
    • Rota, G. C. (1973). The end of objectivity. The Legacy of Phenomenology. Lectures at MIT, 174.
    • Franz, A., Antonenko, O., & Soletskyi, R. (2019). A theory of incremental compression, 44–58. Retrieved from http://arxiv.org/abs/1908.03781
    • A theory of incremental compression by Arthur Franz et al in 2019
    • Hooft, G. ’t. (2014). The Cellular Automaton Interpretation of Quantum Mechanics, 2015. Retrieved from http://arxiv.org/abs/1405.1548
    • Hooft, G. t. (2020). Deterministic Quantum Mechanics: the Mathematical Equations, 1–26. Retrieved from http://arxiv.org/abs/2005.06374

    • examples that connect individual-level stochastic processes to Bayesian hierarchical models

      • Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science, 3(JUN), 42. https://doi.org/10.3389/fenvs.2015.00042

        Stollenwerk, N., Mateus, L., Rocha, F., Skwara, U., Ghaffari, P., & Aguiar, M. (2015). Prediction and Predictability in Population Biology: Noise and Chaos. Mathematical Modelling of Natural Phenomena, 10(2), 142–164. https://doi.org/10.1051/mmnp/201510210 Mateus, L., Stollenwerk, N., & Zambrini, J.-C. (2013). Stochastic models in population biology: from dynamic noise to Bayesian description and model comparison for given data sets. International Journal of Computer Mathematics, 90(10), 2161–2173. https://doi.org/10.1080/00207160.2013.792924

    • invariant measures of random dynamical systems as analogs to attractors in deterministic dynamical systems

  • papers

    • Saakian, D. B., Vardanyan, E., & Yakushkina, T. (2020). Evolutionary model with recombination and randomly changing fitness landscape. Physica A, 541, 123091. https://doi.org/10.1016/j.physa.2019.123091
    • Saakian, D. B., Yakushkina, T., & Koonin, E. V. (2019). Allele fixation probability in a Moran model with fluctuating fitness landscapes. Physical Review E, __99__(2), 022407. https://doi.org/10.1103/PhysRevE.99.022407
    • Friston, K. (2019). A free energy principle for a particular physics. Retrieved from http://arxiv.org/abs/1906.10184 A free energy principle for a particular physics by Karl Friston in 2019
    • Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. Journal of Mathematical Psychology, 76, 198–211. https://doi.org/10.1016/j.jmp.2015.11.003 A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
    • Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017). The free energy principle for action and perception: A mathematical review. Journal of Mathematical Psychology, 81, 55–79. The free energy principle for action and perception A mathematical review by Christopher Buckley, Anil Seth, et al in 2017 https://doi.org/10.1016/j.jmp.2017.09.004
    • Weber, M. F., & Frey, E. (2017). Master equations and the theory of stochastic path integrals. Reports on Progress in Physics, __80__(4), 046601. https://doi.org/10.1088/1361-6633/aa5ae2 Master equations and the theory of stochastic path integrals by Markus Weber and Erwin Frey in 2017
    • Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science, __3__(JUN), 42. https://doi.org/10.3389/fenvs.2015.00042
    • Johnston, I. G., & Jones, N. S. (2015). Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, __471__(2180), 20150050. https://doi.org/10.1098/rspa.2015.0050
    • From computation to life The challenge of a science of organization by Walter Fontana in 2020

  • books

    • Introductory Lectures on Stochastic Population Systems by Donald Dawson in 2017
    • Bishop, C. M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer. Retrieved from http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 Pattern recognition and machine learning by Chris Bishop in 2006

tags

• multiomics , data integration

• single-cell transcriptome analysis

• time series

• network inference

  • Network inference and biological dynamics by Chris Oates and Sach Mukherjee in 2012
  • Causal network inference using biochemical kinetics by Chris Oates, Sach Mukherjee et al in 2014
  • Approximate Bayesian inference in semi-mechanistic models by Andrej Aderhold, Dirk Husmeier, and Marco Grzegorczyk in 2017
  • Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures by Thalia Chan, Michael P. H. Stumpf, and Ann C. Babtie in 2017
  • Inferring Interaction Networks From Multi-Omics Data by Johann Hawe, Fabian Theis, and Matthias Heinig in 2019
  • Multi-Omics Factor Analysis v2 MOFA+ a statistical framework for comprehensive integration of multi-modal single-cell data by Ricard Argelaguet, Oliver Stegle, et al in 2020
  • Mathematical modeling with single-cell sequencing data by Heyrim Cho and Russell Rockne in 2019
  • Gene regulatory network inference from sparsely sampled noisy data by Atte Aalto, Jorge Goncalves, et al in 2020

• approximate bayesian computation

• probabilistic programming

• information geometry and statistical inference

  • Statistical exponential families A digest with flash cards by Frank Nielsen and Vincent Garcia in 2011

• bayesian data analysis and bayesian inference

• evolutionary prediction

  • Nghe, P., De Vos, M. G. J., Kingma, E., Kogenaru, M., Poelwijk, F. J., Laan, L., & Tans, S. J. (2020). Predicting Evolution Using Regulatory Architecture. Annual Review of Biophysics. https://doi.org/10.1146/annurev-biophys-070317
  • Lässig, M., Mustonen, V., & Walczak, A. M. (2017). Predicting evolution. Nature Ecology and Evolution, __1__(3), 0077. https://doi.org/10.1038/s41559-017-0077

• large deviations

  • Ge, H., & Qian, H. (2012). Analytical mechanics in stochastic dynamics: most probable path, large-deviation rate function and Hamilton–Jacobi equation. International Journal of Modern Physics B, __26__(24), 1230012. Mathematical Physics; Statistical Mechanics; Dynamical Systems; Mathematical Physics; Probability. https://doi.org/10.1142/S0217979212300125

• bayesian nonparametrics (books)

  • Ghosal, S., & Vaart, A. van der. (2017). Fundamentals of Nonparametric Bayesian Inference. Cambridge University Press.

• review

pdf (skim)

test-session-06302020