Algorithms in nature: the convergence of systems biology and computational thinking

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high‐level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.

[1]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[2]  J M Rendel,et al.  Canalisation of development of scutellar bristles in Drosophila by control of the scute locus. , 1965, Genetics.

[3]  A. Winfree.  Biological rhythms and the behavior of populations of coupled oscillators. , 1967, Journal of theoretical biology.

[4]  Kenneth C. W. Kammeyer,et al.  An introduction to population , 1974 .

[5]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[6]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Michael Luby,et al.  A simple parallel algorithm for the maximal independent set problem , 1985, STOC '85.

[8]  Noga Alon,et al.  A Fast and Simple Randomized Parallel Algorithm for the Maximal Independent Set Problem , 1985, J. Algorithms.

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  H. Yamagishi,et al.  Physiological Anatomy, Burst Formation, and Burst Frequency of the Cardiac Ganglion of Crustaceans , 1990, Physiological Zoology.

[13]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[14]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[15]  Larry Rudolph,et al.  Distributed hierarchical control for parallel processing , 1990, Computer.

[16]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[17]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[18]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[19]  Nancy A. Lynch,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[20]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[21]  Jeffrey O. Kephart,et al.  Biologically Inspired Defenses Against Computer Viruses , 1995, IJCAI.

[22]  G Theraulaz,et al.  Coordination in Distributed Building , 1995, Science.

[23]  Rachid Guerraoui,et al.  Fault-Tolerance by Replication in Distributed Systems , 1996, Ada-Europe.

[24]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[25]  Yale N. Patt,et al.  Microarchitecture, compilers and algorithms , 1996, CSUR.

[26]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[27]  Radhika Nagpal,et al.  Paradigms for Structure in an Amorphous Computer , 1997 .

[28]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[29]  Gheorghe Paun,et al.  DNA Computing: New Computing Paradigms , 1998 .

[30]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[31]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[32]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[33]  榊原 康文,et al.  G. Paun, G. Rozenberg and A. Salomaa : "DNA Computing-New Computing Paradigms", Springer-Verlag (1998) , 2000 .

[34]  Chris Hanson,et al.  Amorphous computing , 2000, Commun. ACM.

[35]  Russ Bubley,et al.  Randomized algorithms , 1995, CSUR.

[36]  Jean-Louis Deneubourg,et al.  From local actions to global tasks: stigmergy and collective robotics , 2000 .

[37]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[38]  L. Glass.  Synchronization and rhythmic processes in physiology , 2001, Nature.

[39]  B. Hochner,et al.  Control of Octopus Arm Extension by a Peripheral Motor Program , 2001, Science.

[40]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[41]  Thomas D. Seeley,et al.  When Is Self-Organization Used in Biological Systems? , 2002, The Biological Bulletin.

[42]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[43]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[44]  Ronald W. Davis,et al.  Role of duplicate genes in genetic robustness against null mutations , 2003, Nature.

[45]  J. Fewell.  Social Insect Networks , 2003, Science.

[46]  Danny Dolev,et al.  Self-Stabilizing Pulse Synchronization Inspired by Biological Pacemaker Networks , 2003, Self-Stabilizing Systems.

[47]  G. Church,et al.  Accurate multiplex gene synthesis from programmable DNA microchips , 2004, Nature.

[48]  Marcus Kaiser,et al.  Spatial growth of real-world networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  Danny Dolev,et al.  Brief announcement: linear time byzantine self-stabilizing clock synchronization , 2004, PODC '04.

[50]  Hiroaki Kitano,et al.  Biological robustness , 2008, Nature Reviews Genetics.

[51]  Aude Billard,et al.  From Animals to Animats , 2004 .

[52]  Luc Bouganim,et al.  Mobile databases: a selection of open issues and research directions , 2004, SGMD.

[53]  E. Shapiro,et al.  An autonomous molecular computer for logical control of gene expression , 2004, Nature.

[54]  I-Jeng Wang,et al.  Decentralized synchronization protocols with nearest neighbor communication , 2004, SenSys '04.

[55]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[56]  Julie Greensmith,et al.  Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection , 2005, ICARIS.

[57]  Radhika Nagpal,et al.  Firefly-inspired sensor network synchronicity with realistic radio effects , 2005, SenSys '05.

[58]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[59]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[60]  U. Alon,et al.  Spontaneous evolution of modularity and network motifs. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[61]  John C Wooley,et al.  Catalyzing Inquiry at the Interface of Computing and Biology , 2005 .

[62]  Anna Scaglione,et al.  A scalable synchronization protocol for large scale sensor networks and its applications , 2005, IEEE Journal on Selected Areas in Communications.

[63]  Ian Marshall,et al.  A biologically-inspired clustering algorithm dependent on spatial data in sensor networks , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[64]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[65]  M Madan Babu,et al.  Uncovering a hidden distributed architecture behind scale-free transcriptional regulatory networks. , 2006, Journal of molecular biology.

[66]  Uri Alon,et al.  An Introduction to Systems Biology , 2006 .

[67]  Jeannette M. Wing.  An introduction to computer science for non-majors using principles of computation , 2007, SIGCSE.

[68]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[69]  D. Chklovskii,et al.  Wiring optimization can relate neuronal structure and function. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[70]  Cynthia F Moss,et al.  Echolocating Bats Use a Nearly Time-Optimal Strategy to Intercept Prey , 2006, PLoS biology.

[71]  Radhika Nagpal,et al.  Extended stigmergy in collective construction , 2006, IEEE Intelligent Systems.

[72]  Matt Welsh,et al.  Deploying a wireless sensor network on an active volcano , 2006, IEEE Internet Computing.

[73]  Roberto Montemanni,et al.  Design patterns from biology for distributed computing , 2006, TAAS.

[74]  Hongliang Ren,et al.  Biologically Inspired Approaches for Wireless Sensor Networks , 2006, 2006 International Conference on Mechatronics and Automation.

[75]  Thomas Serre,et al.  A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[76]  Eric H Davidson,et al.  The regulatory genome and the computer. , 2007, Developmental biology.

[77]  Andreas Wagner,et al.  Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology , 2007, PLoS Comput. Biol..

[78]  Maxime Crochemore,et al.  Algorithms on strings , 2007 .

[79]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[80]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[81]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[82]  N. Franceschini,et al.  A Bio-Inspired Flying Robot Sheds Light on Insect Piloting Abilities , 2007, Current Biology.

[83]  Thomas Serre,et al.  A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.

[84]  Lior Wolf,et al.  Using Biologically Inspired Features for Face Processing , 2007, International Journal of Computer Vision.

[85]  Márk Jelasity,et al.  Firefly-inspired Heartbeat Synchronization in Overlay Networks , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[86]  James Aspnes,et al.  An Introduction to Population Protocols , 2007, Bull. EATCS.

[87]  Xiaojin Zhu,et al.  Humans Perform Semi-Supervised Classification Too , 2007, AAAI.

[88]  Paul Nurse,et al.  Life, logic and information , 2008, Nature.

[89]  Y. Bar-Ness,et al.  Distributed synchronization in wireless networks , 2008, IEEE Signal Processing Magazine.

[90]  A bio-inspired distributed clustering algorithm for wireless sensor networks , 2008, WICON.

[91]  Zohar Yakhini,et al.  Global organization of replication time zones of the mouse genome. , 2008, Genome research.

[92]  Bernard Chazelle,et al.  Natural algorithms , 2009, SODA.

[93]  Randal A. Koene,et al.  NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies , 2009, Neuroinformatics.

[94]  B Mazzolai,et al.  Design of a biomimetic robotic octopus arm , 2009, Bioinspiration & biomimetics.

[95]  H H McAdams,et al.  Why and How Bacteria Localize Proteins , 2009, Science.

[96]  Olga G. Troyanskaya,et al.  Detailing regulatory networks through large scale data integration , 2009, Bioinform..

[97]  Tim Kovacs,et al.  On optimal decision-making in brains and social insect colonies , 2009, Journal of The Royal Society Interface.

[98]  M. Vidal,et al.  Edgetic perturbation models of human inherited disorders , 2009, Molecular systems biology.

[99]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[100]  Cole Trapnell,et al.  How to map billions of short reads onto genomes , 2009, Nature Biotechnology.

[101]  Howie Choset,et al.  Generating gaits for snake robots by annealed chain fitting and Keyframe wave extraction , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[102]  I. Simon,et al.  Backup in gene regulatory networks explains differences between binding and knockout results , 2009, Molecular systems biology.

[103]  Olga G Troyanskaya,et al.  Discovering biological networks from diverse functional genomic data. , 2009, Methods in molecular biology.

[104]  E. Ott,et al.  The effect of network topology on the stability of discrete state models of genetic control , 2009, Proceedings of the National Academy of Sciences.

[105]  Thomas H Segall-Shapiro,et al.  Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome , 2010, Science.

[106]  J. Mather,et al.  Octopus: The Ocean's Intelligent Invertebrate , 2010 .

[107]  Thomas Serre,et al.  A neuromorphic approach to computer vision , 2010, Commun. ACM.

[108]  Ke Li,et al.  Slime Mold Inspired Path Formation Protocol for Wireless Sensor Networks , 2010, ANTS Conference.

[109]  A. Tero,et al.  Rules for Biologically Inspired Adaptive Network Design , 2010, Science.

[110]  Courtney K. Robinson,et al.  Coordination of frontline defense mechanisms under severe oxidative stress , 2010, Molecular systems biology.

[111]  W. Marwan.  Amoeba-Inspired Network Design , 2010, Science.

[112]  Jon M. Kleinberg,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World [Book Review] , 2013, IEEE Technol. Soc. Mag..

[113]  M. Schatz,et al.  Assembly of large genomes using second-generation sequencing. , 2010, Genome research.

[114]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[115]  Margaret L. Brandeau,et al.  Optimal Localization by Pointing Off Axis , 2010 .

[116]  Howie Choset,et al.  Generating gaits for snake robots: annealed chain fitting and keyframe wave extraction , 2010, Auton. Robots.

[117]  Charu C. Aggarwal,et al.  Graph Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

[118]  Paulien Hogeweg,et al.  The Roots of Bioinformatics in Theoretical Biology , 2011, PLoS Comput. Biol..

[119]  Toshiyuki Nakagaki,et al.  Traffic optimization in railroad networks using an algorithm mimicking an amoeba-like organism, Physarum plasmodium , 2011, Biosyst..

[120]  E. Alleva,et al.  Octopus: the ocean’s intelligent invertebrate , 2011 .

[121]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[122]  Jehoshua Bruck,et al.  Neural network computation with DNA strand displacement cascades , 2011, Nature.

[123]  D. Sumpter,et al.  Fast and accurate decisions through collective vigilance in fish shoals , 2011, Proceedings of the National Academy of Sciences.

[124]  L. Conradt.  Collective behaviour: When it pays to share decisions , 2011, Nature.

[125]  D. Long.  Networks of the Brain , 2011 .

[126]  Roberto Pagliari,et al.  Scalable Network Synchronization with Pulse-Coupled Oscillators , 2011, IEEE Transactions on Mobile Computing.

[127]  Noga Alon,et al.  A Biological Solution to a Fundamental Distributed Computing Problem , 2011, Science.

[128]  J. Weeks.  An introduction to population , 2012 .

[129]  Noga Alon,et al.  Beeping a maximal independent set , 2011, Distributed Computing.

[130]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.