Researchers

This page  includes information about members of the degeneracy research community. If you would like your information to be included, please add a reply in the form at the bottom of the page that includes any of the following optional details:

  • Name, Affiliation (Link to website)
  • Research interests (brief description or keywords)
  • Selected publications on degeneracy (3 max, you may include links and a brief description for each paper)

Sergei P Atamas

Departments of Medicine and Microbiology & Immunology, University of Maryland School of Medicine, and Baltimore VA Medical Center, Baltimore, MD, U.S.A. (http://www.biobitfield.com/dso/)

Research Interests: Dr. Atamas’ research focuses on the consequences of degeneracy-driven dynamics in repertoires of structurally distinct yet functionally overlapping elements. Such “degenerate” repertoires are ubiquitous in biology and society. Dr. Atamas argues that degeneracy defines a previously not considered mode of competition, in which partial, but not complete, functional similarity leads to a complex network of non-local competitive interactions among peers composing such repertoires. Computer modelling suggests that such competition can result in population re-structuring and the emergence of traits that deviate strongly from the selection pressure imposed by the environment. Specific applications to peer selection, including in human societies, is considered.

  1. Atamas SP. Self-organization in computer simulated selective systems. Biosystems. 1996; 39(2):143-51
  2. Atamas SP, Luzina IG, Handwerger BS, White B. 5′-degenerate 3′-dideoxy-terminated competitors of PCR primers increase specificity of amplification. Biotechniques. 1998; 24(3):445-50
  3. Atamas SP, Bell J. Degeneracy-driven self-structuring dynamics in selective repertoires. Bull Math Biol. 2009; 71(6):1349-65

Axel Bender

Land Operations Division, Defence Science and Technology Organization, Australia

  1. Whitacre J. M. and Bender A., “Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems” Theoretical Biology and Medical Modelling, vol. 7(20), 2010  http://www.tbiomed.com/content/7/1/20
  2. Whitacre J. M. and Bender A., “Degeneracy: a design principle for robustness and evolvability” Journal of Theoretical Biology, 263(1): 143-153, 2010 http://arxiv.org/ftp/arxiv/papers/0907/0907.0510.pdf
  3. Whitacre, J. M., Rohlfshagen, P., Bender, A, and  Yao, X.  “Evolutionary Mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world,” Journal of Natural Computing (in press)  http://www.box.net/shared/l56kcd62uk

Giovanni Bonifati

Dipartimento di Comunicazione ed Economia, Università di Modena e Reggio Emilia – Italy

Research Interests: I am interested in the degeneracy in relation to the economics of innovation. In particular, my research focuses on the analysis of how the degeneracy can emerge in connection with the process of exaptation by which the initial attributions of new functionalities to existing artifacts and technologies lead to new artifacts and new technologies.

  1. Bonifati, G. (2010), ”More is different’, exaptation and uncertainty: three foundational concepts for a complexity theory of innovation’, Economics of Innovation and New Technology, vol. 19 n. 8, pp. 743 – 760. http://ideas.repec.org/a/taf/ecinnt/v19y2010i8p743-760.html
  2. Bonifati, G. (2010), Exaptation, degeneracy and innovation, Materiali di Discussione 638, Modena: Dipartimento di Economia Politica. http://ideas.repec.org/p/mod/depeco/0638.html
  3. Bonifati, G. and Villani, M. (forthcoming), Exaptations in innovation processes: theory and models, in Handbook of economic organization, ed. A. Grandori, Edward Elgar, Cheltenham.

Ed Clark

Member of the York Centre for Complex Systems Analysis (YCCSA) and Department of Computer Sciecne at the University of York UK.

Research Interests: My interests in degeneracy fall into three related areas:

  • Degeneracy in paratope-epitope interactions in the thymus (theoretical descriptions and models)
  • Degeneracy in binding systems in Artificial Chemistries
  • Formal definitions and methods of calculation degeneracy in a system

I have paper in ECAL 2011 that address the last two topics http://www-users.cs.york.ac.uk/edclark/Clark_ECAL2011.pdf. Ed Clark, Adam Nellis, Simon Hickinbotham, Susan Stepney, Tim Clarke, Mungo Pay, Peter Young. Degeneracy Enriches Artificial Chemistry Binding Systems. ECAL 11, Paris, France, August 2011. IN PRESS

Peter Csermely

Department of Medicine, Semmelweis University, Hungary (http://www.linkgroup.hu/)

  1. Csermely, P., Weak links: Stabilizers of complex systems from proteins to social networks. 2006: Springer Verlag.

Jose A. Fernandez-Leon, DPhil & Dr.Sc.Comp

Cortical Mechanisms of Visual Behavior Lab, Dept. of Neurobiology and Anatomy-Houston Medical School. (http://jafphd.wordpress.com/)

Affiliations:

– Research Fellow at the Neurobiology and Anatomy Department, University of Texas Health Science Center-Houston, USA.

– Visiting Research Fellow at the Department of Informatics, University of Sussex, UK.

Current research interests:  The two great complex adaptive biological systems that humans have to deal with in the diverse world that surrounds us are the central nervous system, where the brain is the main ‘component’, and the immune system, which operates below the level of consciousness as a relational phenomenon. Failures of adaptivity in these systems can produce inconveniences that attain to systemic robustness which usually associates to diseases at organism level in the biological realm. My current research also focuses on how complex adaptive systems like the nervous system and the immune system maintain functions despite internal malfunctions and external perturbations in organisms that couples with the environment. I focus on theoretical and experimental foundations with a computational approach in order to understand this issue in association to degeneracy at brain-environment level. My studies mainly focus on behavioural robustness and the distributed mechanisms hypothesis.

General research interest:

– Neurobiology and Systems Neuroscience: visual learning and memory

– Theoretical and Computational Neurosciences: minimally cognitive, situated and embodied systems

– Theoretical Systems Biology: evolutionary complex adaptive systems

– Bio-Inspired Systems Engineering: multi-agents, neural- and immune-based robotic control

Selected Publications:

  1. Fernandez-Leon, J.A. (2011). Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics. BioSystems 105 , Elsevier, pp. 49-61.
  2. Fernandez-Leon, J.A., Acosta, G., & Mayosky, M. (2011). From network-to-antibody robustness in a bio-inspired immune system. BioSystems 104(2-3), Elsevier, pp. 109-17.
  3. Fernandez-Leon, J.A. (2010). Evolving experience-dependent robust behaviour in embodied agents. BioSystems 103:1, Elsevier, pp. 45-56.

Andrea Grignolio

University of Bologna (http://www.centrogalvani.unibo.it)

Research interests: History of Immunology and Molecular Biology

  1. Grignolio A. (ed. by), Immunology Today. Three Historical Perspectives under Three Theoretical Horizons, Bononia University Press, Bologna, 2010.
  2. Tieri P., Grignolio A., Zaikin A., Mishto M., Remondini D., Castellani G.C ., Franceschi C., “Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system,” Theoretical Biology and Medical Modelling, 2010, 7, 32, pp.1-16.
  3. Franceschi C., Grignolio A., “Immunosenescence within an Evolutionary Perspective”, in Grignolio A. (ed. by), Immunology Today. Three Historical Perspectives under Three Theoretical Horizons, Bononia University Press, Bologna, 2010, pp. 79-99.

Angus Harding

The University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia. (http://www.di.uq.edu.au/ahardingprofile)

  1. Tian, T., S. Olson, J.M. Whitacre, and A. Harding, The origins of cancer robustness and evolvability. Integrative Biology, 2011. 3: p. 17-30. http://pubs.rsc.org/en/Content/ArticleLanding/2011/IB/c0ib00046a

Sjoerd Kerkstra

Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

Research Interests: Degeneracy and its effect on evolvability studied through the genotype phenotype map paradigm. In my masters thesis I explore this relationship in Random Boolean Network (RBN) models. By defining measures of degeneracy, redundancy, robustness and evolvability I found that highly redundant RBNs are robust to mutations but lack potential to reach new phenotypes. Highly degenerate RBNs are robust but also evolvable. Furthermore, degeneracy cannot exist without redundancy, but redundancy can exist without degeneracy.

  1. Evolution and the Genotype Phenotype map.pdf, Masters Thesis for Artificial Intelligence at the University of Amsterdam, 2008

Paul Mason

Department of Anthropology, Macquarie University (http://paul.sobriquet.net/)

  1. Mason, P.H., Degeneracy at Multiple Levels of Complexity. Biological Theory, 2010. 5(3): p. 277-288.

Nicholas M Mellen

KCHRI, University of Louisville, Louisville KY 40202 USA

Research Interests: Dr. Mellen characterizes network level interactions between neurons in ventrolateral medulla that control breathing. The conceptual focus of this work is to elucidate how these networks regulate blood-gas homeostasis. The conjecture Dr. Mellen has put forward is that respiratory network degeneracy provides the substrate for this regulatory function.

  1. Mellen NM Degeneracy as a substrate for respiratory regulation. Respir Physiol Neurobiol, 2010 172:1-7 http://www.sciencedirect.com/science/article/pii/S1569904810001485

Julian Miller

Research Interests: Dr. Miller’s research is primarily related to the evolution of computer programs and other computational structures. This is known as genetic programming (GP). He invented a well-known form of GP called Cartesian genetic programming (CGP) which encodes graph-structures in an integer genotype. The genotype is highly redundant and contains non-coding genes. He has published a number of studies into the nature and efficacy of this form of redundancy.

  1. Miller J.F., Smith S.L. Redundancy and Computational Efficiency in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation, Vol. 10 (2006) 167-174
  2. Yu T., Miller J.F., Through the Interaction of Neutral and Adaptive Mutations Evolutionary Search Finds a Way. Artificial Life, Vol. 12 (2006) 525-551
  3. Yu T., Miller J. F. Neutrality and Evolvability of a Boolean Function Landscape, Proceedings of the 4th European Conference on Genetic Programming, Springer LNCS, Vol. 2038,(2001) 204-217.
  4. Vassilev V. K., Miller J. F. The Advantages of Landscape Neutrality in Digital Circuit Evolution. Proceedings of the 3rd International Conference on Evolvable Systems: From Biology to Hardware. Springer LNCS, Vol. 1801 (2000) 252-263

Most publications are available from https://sites.google.com/site/julianfrancismiller/publications

Martin Randles

School of Computing and Mathematical Sciences, Liverpool John Moores University (http://www.ljmu.ac.uk/cmp)

Research Interests: Dr. Randles research, in degeneracy, arises out of work seeking tools and techniques for modelling, monitoring, analysing and verifying complex computational systems. Many systems routinely employ redundancy to enhance performance and aid resilience and robustness. Complex computational systems, however, may also make advantage of emergent phenomena, such as self-organisation, whereby advantageous conditions, such as distributed redundancy, may emerge in components, allowing structurally different software services, for example, to offer the same or similar functionality. The goal of engendering this distributed redundancy or degeneracy, for robustness and quasi-global monitoring, for efficient feedback and system verification, is investigated through identifying key metrics and features associated with engineering the emergence of beneficial system outcomes.

  1. Martin Randles, David Lamb, E. Odat, A. Taleb-Bendiab, “Distributed redundancy and robustness in complex systems”, Journal of Computer and System Sciences, Elsevier. In Press, Corrected Proof, Available online 1 February 2010, ISSN 0022-0000, DOI: 10.1016/j.jcss.2010.01.008.
  2. Martin Randles, Osama Abu-Rahmeh, Princy Johnson and A. Taleb-Bendiab, “Biased Random Walks on Resource Network Graphs for Load Balancing”, The Journal of Supercomputing, Volume 53 Issue 1, pp: 138-162, Springer, 2010.
  3. Martin Randles, A. Taleb-Bendiab, Cherif Branki and David Lamb, ” Evolving Formal Models: Multi Agent Self-Organisation for Governance and Regulation in Service Oriented Systems” In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2010, Toronto, Canada.

Philipp Rohlfshagen

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom (http://www.philipprohlfshagen.net/)

  1. Whitacre, J. M., Rohlfshagen, P., Bender, A, and  Yao, X.  “Evolutionary Mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world,” Journal of Natural Computing – Special Issue on Emergent Engineering (in press)  http://www.box.net/shared/l56kcd62uk
  2. Whitacre, J. M., Rohlfshagen, P.,  Yao, X. and Bender, A.  “The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments,” R. Schaefer et al. (Eds.): PPSN XI, Part I, LNCS 6238, pp. 284–293, 2010. (Krakow, Poland, Sept 11-15) https://www.box.net/shared/ulq49rbvtk

John Timmis

  1. Mendao, M., J. Timmis, P.S. Andrews, and M. Davies, The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System. Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on, 2007: p. 394-400.
  2. Andrews, P.S. and J. Timmis, A Computational Model of Degeneracy in a Lymph Node. Lecture Notes in Computer Science, 2006. 4163: p. 164.

Paolo Tieri

“Luigi Galvani” Interdepr Center for Integrated Studies of Bioinformatics, Biophysics, Biocomplexity (C.I.G.), University of Bologna ( http://www.centrogalvani.unibo.it/)

  1. Paolo Tieri, Gastone Castellani, Daniel Remondini, Silvana Valensin, Jonathan Loroni, Stefano Salvioli and Claudio Franceschi. Capturing Degeneracy in the Immune System. In Silico Immunology, 2007, Part II, 109-118, DOI: 10.1007/978-0-387-39241-7_7 http://www.scribd.com/doc/57439071/Capturing-Degeneracy-in-the-Immune-System
  2. Tieri P, Grignolio A, Zaikin A, Mishto M, Remondini D, Castellani GC, Franceschi C. Network, degeneracy and bow tie integrating paradigms and architectures to grasp the complexity of the immune system. Theor Biol Med Model. 2010 Aug 11;7:32. PubMed PMID: 20701759 http://www.tbiomed.com/content/7/1/32

James M Whitacre

CERCIA Computational Intelligence Lab, University of Birmingham, U.K., (https://sites.google.com/site/jamesmwhitacre/Home)

Research Interests: Dr Whitacre’s research explores the relationships between robustness, evolvability, and complexity and how these relationships are influenced by the presence of degeneracy. Important research findings include: i) in silico evidence that degeneracy is essential to the establishment of positive robustness-evolvability relationships in evolution ii) evidence that degeneracy can lead to emergent forms of distributed robustness that are superior to robustness achieved through pure redundancy iii) theoretical arguments that degeneracy helps to resolve exploration/exploitation conflicts that commonly arise in systems with distinct short-term and long-term objectives.  Insights from these theoretical developments have been applied within the context of artificial life, dynamic optimization, strategic planning, and therapeutic strategies for evolvable cancers.

  1. Whitacre J. M. and Bender A., “Degeneracy: a design principle for robustness and evolvability” Journal of Theoretical Biology, 263(1): 143-153, 2010 http://arxiv.org/ftp/arxiv/papers/0907/0907.0510.pdf
  2. Whitacre J. M., “Degeneracy: a link between evolvability, robustness and complexity in biological systems”  Theoretical Biology and Medical Modelling, vol. 7(6), 2010  http://www.tbiomed.com/content/7/1/6


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