Inverse Problems and Hierarchical Multiscale Modelling of Biological Matter

[1]  Andrew L. Ferguson,et al.  Machine learning and data science in soft materials engineering , 2018, Journal of physics. Condensed matter : an Institute of Physics journal.

[2]  Hong Liu,et al.  GALAMOST: GPU‐accelerated large‐scale molecular simulation toolkit , 2013, J. Comput. Chem..

[3]  M Scott Shell,et al.  The relative entropy is fundamental to multiscale and inverse thermodynamic problems. , 2008, The Journal of chemical physics.

[4]  Hyung Min Cho,et al.  Inversion of radial distribution functions to pair forces by solving the Yvon-Born-Green equation iteratively. , 2009, The Journal of chemical physics.

[5]  Robert H. Swendsen,et al.  Monte Carlo Renormalization Group , 1979 .

[6]  Leonid A. Mirny,et al.  Chromatin Loops as Allosteric Modulators of Enhancer-Promoter Interactions , 2014, bioRxiv.

[7]  Dariusz Plewczynski,et al.  An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization , 2016, Genome research.

[8]  Alexander P. Lyubartsev,et al.  A multiscale model of protein adsorption on a nanoparticle surface , 2019, Modelling and Simulation in Materials Science and Engineering.

[9]  Modesto Orozco,et al.  Multiscale simulation of DNA. , 2016, Current opinion in structural biology.

[10]  William George Noid,et al.  A Generalized-Yvon−Born−Green Theory for Determining Coarse-Grained Interaction Potentials† , 2010 .

[11]  Janet E. Jones.  On the determination of molecular fields. —II. From the equation of state of a gas , 1924 .

[12]  A. Laaksonen,et al.  Multiscale simulations of human telomeric G-quadruplex DNA. , 2015, The journal of physical chemistry. B.

[13]  Kevin J. Emmett,et al.  Multiscale Topology of Chromatin Folding , 2015, BICT.

[14]  Dirk Reith,et al.  Deriving effective mesoscale potentials from atomistic simulations , 2002, J. Comput. Chem..

[15]  R. L. Henderson.  A uniqueness theorem for fluid pair correlation functions , 1974 .

[16]  David N. Beratan,et al.  Emergent strategies for inverse molecular design , 2009 .

[17]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[18]  Anna Tonazzini,et al.  Estimation of the Spatial Chromatin Structure Based on a Multiresolution Bead-Chain Model , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[19]  Aatto Laaksonen,et al.  Systematic hierarchical coarse-graining with the inverse Monte Carlo method. , 2015, The Journal of chemical physics.

[20]  Bashar Ibrahim,et al.  Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore , 2013, Cells.

[21]  W. Roos,et al.  Special Issue on the Physics of Viral Capsids. , 2018, Journal of physics. Condensed matter : an Institute of Physics journal.

[22]  Gregory A Voth,et al.  Multiscale coarse graining of liquid-state systems. , 2005, The Journal of chemical physics.

[23]  Tamar Schlick,et al.  Linking Chromatin Fibers to Gene Folding by Hierarchical Looping. , 2017, Biophysical journal.

[24]  Alexander Lyubartsev,et al.  Multiscale Modelling of Bionano Interface. , 2015, Advances in experimental medicine and biology.

[25]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[26]  Karen Lipkow,et al.  Higher order assembly: folding the chromosome. , 2017, Current opinion in structural biology.

[27]  Edith Heard,et al.  Segmental folding of chromosomes: A basis for structural and regulatory chromosomal neighborhoods? , 2013, BioEssays : news and reviews in molecular, cellular and developmental biology.

[28]  Alexander Lyubartsev,et al.  Systematic coarse-graining of molecular models by the Newton inversion method. , 2010, Faraday discussions.

[29]  Penny Nymark,et al.  Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale , 2020, Particle and Fibre Toxicology.

[30]  Eric Jonas,et al.  Deep imitation learning for molecular inverse problems , 2019, NeurIPS.

[31]  A. Lyubartsev,et al.  A multiscale analysis of DNA phase separation: from atomistic to mesoscale level , 2019, Nucleic acids research.

[32]  Aatto Laaksonen,et al.  Concentration Effects in Aqueous NaCl Solutions. A Molecular Dynamics Simulation , 1996 .

[33]  A. Lyubartsev,et al.  Coarse-Grained Simulation of Rodlike Higher-Order Quadruplex Structures at Different Salt Concentrations , 2017, ACS omega.

[34]  Sungwon Kim,et al.  Machine-enabled inverse design of inorganic solid materials: promises and challenges , 2020, Chemical science.

[35]  C. Adjiman,et al.  Computer-aided molecular design of solvents for accelerated reaction kinetics. , 2013, Nature chemistry.

[36]  Roland Potthast,et al.  A Survey on Inverse Problems for Applied Sciences , 2013 .

[37]  Alex Zunger,et al.  The inverse band-structure problem of finding an atomic configuration with given electronic properties , 1999, Nature.

[38]  A. Lyubartsev,et al.  Molecular dynamics simulations of DNA in solutions with different counter-ions. , 1998, Journal of biomolecular structure & dynamics.

[39]  Alexander P. Lyubartsev,et al.  Hierarchical multiscale modelling scheme from first principles to mesoscale , 2009 .

[40]  A. Feinberg,et al.  Higher order chromatin organization in cancer. , 2013, Seminars in cancer biology.

[41]  Alán Aspuru-Guzik,et al.  Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.

[42]  A. Pombo,et al.  Three-dimensional genome architecture: players and mechanisms , 2015, Nature Reviews Molecular Cell Biology.

[43]  Thomas Hinze,et al.  Rule-based spatial modeling with diffusing, geometrically constrained molecules , 2010, BMC Bioinformatics.

[44]  B. A. Lindquist,et al.  Inverse methods for design of soft materials. , 2020, The Journal of chemical physics.

[45]  Alexander P. Lyubartsev,et al.  OSMOTIC AND ACTIVITY COEFFICIENTS FROM EFFECTIVE POTENTIALS FOR HYDRATED IONS , 1997 .

[46]  W. Schommers,et al.  Pair potentials in disordered many-particle systems: A study for liquid gallium , 1983 .

[47]  Kari Laasonen,et al.  Hydration of Li+ ion. An ab initio molecular dynamics simulation , 2001 .

[48]  J. Mallm,et al.  Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions. , 2017, Biophysical journal.

[49]  A. Lyubartsev,et al.  Effective solvent mediated potentials of Na+ and Cl- ions in aqueous solution: temperature dependence. , 2011, Physical chemistry chemical physics : PCCP.

[50]  Erik G. Brandt,et al.  Molecular Dynamics Simulations of Adsorption of Amino Acid Side Chain Analogues and a Titanium Binding Peptide on the TiO2 (100) Surface , 2015 .

[51]  Tamar Schlick,et al.  The chromatin fiber: multiscale problems and approaches. , 2015, Current opinion in structural biology.

[52]  Geoffrey Fudenberg,et al.  Modeling chromosomes: Beyond pretty pictures , 2015, FEBS letters.

[53]  Iris Eisenberger,et al.  Making Nanomaterials Safer by Design? , 2017 .

[54]  V. MARTÍNEZ-LUACES.  Chemical Kinetics and Inverse Modelling Problems , 2012 .

[55]  I. Amit,et al.  Comprehensive mapping of long range interactions reveals folding principles of the human genome , 2011 .

[56]  Alan K. Soper,et al.  Empirical potential Monte Carlo simulation of fluid structure , 1996 .

[57]  H. L. Frisch,et al.  Inverse Problem in Classical Statistical Mechanics , 1969 .

[58]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[59]  James B. Adams,et al.  Interatomic Potentials from First-Principles Calculations: The Force-Matching Method , 1993, cond-mat/9306054.

[60]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[61]  Caroline Chaux,et al.  Challenges in the decomposition of 2D NMR spectra of mixtures of small molecules. , 2019, Faraday discussions.

[62]  A. Laaksonen,et al.  A Solvent-Mediated Coarse-Grained Model of DNA Derived with the Systematic Newton Inversion Method. , 2014, Journal of chemical theory and computation.

[63]  Michael Nilges,et al.  Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data , 2016, PLoS Comput. Biol..

[64]  Lars Nordenskiöld,et al.  An Advanced Coarse-Grained Nucleosome Core Particle Model for Computer Simulations of Nucleosome-Nucleosome Interactions under Varying Ionic Conditions , 2013, PloS one.

[65]  Ursula Rothlisberger,et al.  Variational particle number approach for rational compound design. , 2005, Physical review letters.

[66]  Weis,et al.  Iterative predictor-corrector method for extraction of the pair interaction from structural data for dense classical liquids. , 1986, Physical review. A, General physics.

[67]  Yannick G. Spill,et al.  Restraint‐based three‐dimensional modeling of genomes and genomic domains , 2015, FEBS letters.

[68]  Dieter W Heermann,et al.  Computational modeling of the chromatin fiber. , 2007, Seminars in cell & developmental biology.

[69]  Tom Misteli,et al.  The lamin protein family , 2011, Genome Biology.

[70]  Alexander Mirzoev,et al.  MagiC: Software Package for Multiscale Modeling. , 2013, Journal of chemical theory and computation.

[71]  Alexander P. Lyubartsev,et al.  Effective potentials for ion-DNA interactions , 1999 .

[72]  A. Lyubartsev,et al.  Computer modeling demonstrates that electrostatic attraction of nucleosomal DNA is mediated by histone tails. , 2006, Biophysical journal.

[73]  Juan J de Pablo,et al.  Bottom-Up Meets Top-Down: The Crossroads of Multiscale Chromatin Modeling. , 2020, Biophysical journal.

[74]  Eric S. Lander,et al.  Hi-C: A Method to Study the Three-dimensional Architecture of Genomes. , 2010, Journal of visualized experiments : JoVE.

[75]  J. Lammerding,et al.  Lamins at a glance , 2012, Journal of Cell Science.

[76]  Jinwei Zhu,et al.  Characterizing the Binding Sites for GK Domain of DLG1 and DLG4 via Molecular Dynamics Simulation , 2020, Frontiers in Molecular Biosciences.

[77]  A. Lyubartsev,et al.  Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[78]  Kelin Xia.  Sequence-based Multiscale Model (SeqMM) for High-throughput chromosome conformation capture (Hi-C) data analysis , 2017 .

[79]  W. Rocchia,et al.  Chromatin Compaction Multiscale Modeling: A Complex Synergy Between Theory, Simulation, and Experiment , 2020, Frontiers in Molecular Biosciences.

[80]  Mohammad Atif Faiz Afzal,et al.  Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space , 2018 .

[81]  Michael Habeck,et al.  Estimation of Interaction Potentials through the Configurational Temperature Formalism. , 2013, Journal of chemical theory and computation.

[82]  A. Laaksonen,et al.  A coarse-grained simulation study of the structures, energetics, and dynamics of linear and circular DNA with its ions. , 2015, Journal of chemical theory and computation.

[83]  Weitao Yang,et al.  Designing molecules by optimizing potentials. , 2006, Journal of the American Chemical Society.

[84]  Aatto Laaksonen,et al.  Electrostatic Background of Chromatin Fiber Stretching , 2004, Journal of biomolecular structure & dynamics.

[85]  Alexander Lyubartsev,et al.  Magic v.3: An integrated software package for systematic structure-based coarse-graining , 2019, Comput. Phys. Commun..

[86]  A. Lyubartsev,et al.  Electrostatic origin of salt-induced nucleosome array compaction. , 2010, Biophysical journal.

[87]  Alexander P. Lyubartsev,et al.  Determination of effective pair potentials from ab initio simulations: application to liquid water , 2000 .