State of the art list

Computational Biology

Klaus Schulten (Theoretical and Computational Biophysics Group - University of Illinois)

  • Theoretical physics and theoretical biology
  • Structure and function of supramolecular systems in the living cell, and on the development of non-equilibrium statistical mechanical descriptions and efficient computing tools for structural biology

James Phillips (Theoretical and Computational Biophysics Group - University of Illinois)

  • NAMD: parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms and tens of processors on commodity clusters using gigabit ethernet.

Tamar Schlick (Computational Biology / Chemistry / Biomathematics - New York University)

  • Understand how the structures and motions of complex biological systems regulate fundamental functions like DNA transcription, replication, and recombination and the initiation of disease.
  • Apply simulation approaches (molecular dynamics, energy minimization, free energy calculations, general molecular modeling) to allow large-scale and long-time studies of biological systems.
  • Molecular Modeling and Simulation: An Interdisciplinary Guide

Robert D. Skeel (Computer Science Faculty - Purdue University)

  • Computational methods for biomolecular simulation, which seeks to aid in the discovery of the structures and mechanisms that make life possible.

International Society For Computational Biology

  • Scholarly society dedicated to advancing the scientific understanding of living systems through computation

Barry McMullin
(Dublin City University)

Walter Fontana (Harvard University)

Peter Dittrich (Center for Self-Organized Integration of Computing and Information Systems – Germany)

Wolfgang Banzhaf (Department of Computer Science - Memorial University of Newfoundland – Canada)


Jens Busch (Universität Dortmund)


IBM: Computational Biology & Medical Informatics

  • Computational Biology Center at the T. J. Watson Research Center (IBM)
  • ProteomicsBlueGene
Life Modeling

Przemyslaw Prusinkiewicz (University of Calgary)


Craig W. Reynolds

  • Computer modeling of life-like complex behavior

Xiaoyuan Tu
  • Modelling, control and simulation of life-like, intelligent virtual characters

Aaron Sloman (Computer Science - University of Birmingham)

  • Analysis of evolvable virtual-machine information-processing architectures for human-like minds

Charles Ostman (Institute for Global Futures)

  • Development of the next generation of self evolving computing systems, artificial life forms, Nanotechnology, and virtual reality as an art medium

Demetri Terzopoulos (University of Toronto)

  • Computer vision and graphics, and also in computer-aided design, medical imaging, artificial intelligence, and artificial life.

Larry Yaeger (Indiana University)

  • Artificial Life, Complexity, Information Theory, Neural Networks, Artificial Intelligence, Cognition, Computer Graphics, Genetic Algorithms, Ecological Simulation, Evolution, Handwriting Recognition
  • Homepage
  • Polyworld: An Artificial Life System and Computational Ecology (movies)

Steve Grand (Cyberlife Research)


Thomas S. Ray (University of Oklahoma)

  • Digital evolution.
  • Tierra

Moshe Sipper (Ben-Gurion University, Israel)

  • Evolutionary Computation
  • Bio-Inspired Computing
  • Artificial Life


Chris Adami (The Digital Life LaboratoryCalifornia Institute of Technology)

  • Dynamics of simple living systems, in particular their evolution.

Charles A. Ofria
(The Digital Evolution LaboratoryMichigan State University)
  • Interplay between computer science and Darwinian evolution.

Richard Lenski
(Michigan State University)
  • Experimental evolution
  • Bacteria and AVIDA

Maciej Komosinski
(Institute of Computing Science - Poznan University of Technology)

Richard Dawkins (Oxford University)

  • The Selfish Gene

Christian Jacob (University of Calgary)

  • Emergent Computing. Evolutionary Computing. Bioinformatics & Computationasl Biology.
  • Evolutionary and Swarm Design Group: How specific natural design principles - such as mutation & selection, self-organization & self-assembly, and emergent computing through swarm intelligence - can be better understood by developing mathematical and computational models.

  • The Evolutionary Emergence of Intelligent Behaviours via Computational Natural Selection
Gene Regulatory Networks


Stuart A. Kauffman (Santa Fe Institute)

  • Random Boolean networks
  • A Proposal for Using the Ensemble Approach to Understand Genetic Regulatory Networks (Kauffman’s paper in JTB)
  • Understanding Genetic Regulatory Networks (Kauffman’s paper in IJA)

Leon Glass (The Centre for Nonlinear Dynamics in Physioloy and Medicine)

  • Piecewise-linear models

Bayesian Network

  • Used to deduce the structure and logic of GRN from gene expression and proteomic data.
  • Wikipedia

Petri Nets


Other mathematical tools

  • Ensemble approach (scale-free networks and “medusa networks”)

Andreas Zell (CS Dept. - Universität Tübingen - Germany)

GINsim (Les Universités à Marseille)

  • Java simulator of genetic regulatory networks

Carter GW (Institute for Systems Biology)

Soule C (CNRS & IHES - Institut des hautes etudes scientifiques)

  • Modelling pathways of cell differentiation in GRN with random Boolean networks (Dealy’s MS thesis at CS – University of New Mexico)

Erez Braun & Naama Brenner (Israel Institute of Technology)


Marwan W (Science and Technology Research Institute - University of Hertfordshire)

  • Morphomatics (course on Mathematical models for biological pattern formation)

Nic Geard (Complex and Intelligent Systems Group - School of Information Technology and Electrical Engineering - The University of Queensland)


Artificial Genome

Jennifer Hallinan (Institute for Molecular Biosciences and School of ITEE - The University of Queensland)


Nick Jakobi (School of cognitive and computing sciencesUniversity of Sussex)

Janet Wiles (UQ Computational Evolution and Complexity Research Group - School of Information Technology and Electrical Engineering - University of Queensland)

Josh Bongard (Computational Synthesis Lab - Sibley School of Mechanical and Aerospace Engineering - Cornell University)

Life Theory

Francisco Varela & Humberto Maturana

Dominique Chu (Computing Laboratory - University of Kent)


Complex Systems

Stuart A. Kauffman (University of Pennsylvania, Santa Fe Institute, Universisty of New Mexico)

  • Complexity, biomedicine

Christopher G. Langton

Stephen Wolfram

Computer Art

Jeffrey Ventrella

  • Algorithmic Art . Virtual Creatures
 darwin      evomusic smalldisney
Karl Sims (US R&D - Sony Computer Entertainment)
  • Computer Graphics. Evolution.
  • Prophet works across disciplines on a number of internationally acclaimed projects that have broken new ground in art, technology and science

Steven Rooke

  • Evolutionary Art

Gerald de Jong(Beautiful Code)



Hod Lipson (Cornell Computational Synthesis Lab - Cornell University)

  • Methods for autonomous adaptation in behavior and morphology of robotic systems
  • Biologically-inspired approaches


Dario Floreano (Laboratory of Intelligent Systems - Ecole Polytechnique Fédérale de Lausanne )

  • Development of intelligent robotics and software inspired by biological principles of self-organization
  • Bio-mimetic micro-flying robots
  • Evolutionary Software and Hardware
  • Collective and Swarm Systems


Rolf Pfeifer (Artificial Intelligence Laboratory University of Zurich )

  • Embodied Cognitive Science
  • Biorobotics
  • Autonomous agents/mobile robots
  • Educational Technology
  • Artificial Life
  • Morphology/morpho-functional machines
  • Situated Design
  • Emotion

Rodney Brooks (Computer Science and Artificial Intelligence Laboratory - MIT)


Bruce Rennegar (University of California)


Nasa’s Astrobiology Institute



High Performance Computing

Tommaso Toffoli (Boston University)

  • Cellular Automata
  • Fine-grained architectures for massively parallel computation
  • Connections between microscopic dynamical processes and macroscopic phenomenology. Physical modeling approaches that take advantage of massively parallel, fine-grained computational resources.


Norman Margolus (MIT Computer Science and Artificial Intelligence Laboratory)

  • Cellular Automata
  • Fine-grained parallelism that is available in nature

World Community Grid

  • Public computing grid benefiting humanity


David P. Anderson (Berkeley University of California)


Rich Wolski (University of California)


Thomas Sterling (Center for Advanced Computing Research| - California Institute of Technology)


Robert G. Brown (Duke Physics - Duke University)



Tim Tyler

  • Cellular Automata, tensegrity, domes, alife, …



Alife Database

  • A Searchable Database of Alife Related Sites on the Net, Automatically Gathered by an Intelligent Search Bot


  • Collection of Alife resources

The Physics Encyclopedia

  • The Digital Biology Project: promote and assist in the engineering of complete, biologically-inspired, synthetic ecosystems and organisms


Lindenmayer Systems



The Netron Project

  • Graph library for graph-drawing


CAMELot (Institute of High Performance Computing and Networking - Italy)

  • A cellular automata programing environment

SIMP/STEP (Boston University)

  • A platform for CA and LG (Crystalline Computation)


  • A platform for agent-based models.



  • A Java-based architecture for the construction of large-scale distributed agent-based applications.


  • Multi-agent simulation environment – implemented in Java: cross-platform. Based on hubnet, not so much tested.



  • C++ engine for modeling Bayesian networks



  • Programmable modeling environment for distributed systems – for students.

Web-Tools for Bioinformatics and Genome research (University of Pittsburgh)


  • Material on developmental biology