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A New Biology – The Co-evolution of Epigenetics, Cell Biology, Physiology and Computational Medicine



Dear Colleague,



herewith I invite you to participate our WorldCafe
table discussion session entitled “
A New Biology – The Co-evolution of Epigenetics, Cell Biology and
Computational Medicine –
On the Role of Ubiquitous Complex Event Processing
(U-CEP) in Life Sciences and Healthcare” in preparation of the workshop
„From Event-Driven Business Process Management
to Ubiquitous Complex Event Processing“
in Ghent on Dec. 13th 2010 (
http://www.lswn.it/en/workshops/2010/from_event_driven_business_pro...)



Our preliminary agenda is set in the outline below. Please feel free to deepen and extend it with topics
of your interest as related to the common base of event-driven processing. I would appreciate your feedback and suggestions for points
4.e, 4.f and 4.g. Please free to contact me.





To find out what this term means in the software engineering domain, the bridge to which we intend to build in our session, you
may consult the following links:


http://www.complexevents.com/2010/05/16/ubiquitous-complex-event-pr...


http://www.complexevents.com/


http://en.wikipedia.org/wiki/Complex_event_processing


http://en.wikipedia.org/wiki/Event-driven_architecture



+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


Abstract/Outline for Ghent workshop


Table 5 “Epigenetics/Cell Biology/New Biology/Healthcare and U-CEP”


http://www.citt-online.com/CfP-edBPM-UCEP.htm



A New Biology – The Co-evolution of Epigenetics, Cell Biology, Physiology and Computational Medicine – On the Challenge of a Mediator and Catalyzer Role for Ubiquitous Complex Event Processing (U-CEP) in Life Sciences
and Healthcare



Plamen L. Simeonov (JSRC) et al.






1. Introduction – The World through the Human Eyes (State of the Art)



a. Everything that happens in (living) Nature is based on the interactions and
self-assembly reactions that are entropically driven (Lauffer, 1975; Nicolis
& Prigogine, 1977; Brooks & Wiley, 1988; Prigogine, 1997). Their common
characteristics are events. But what is reality (Penrose, 2006)?


b. Interactions are continuous and everywhere; they are ubiquitous. An important aspect of interactions is their organization on a multiplicity of scales/viewpoints within the same context/location with different complexity level and rhythm. Yet, continuity itself depends on the timescale where it is observed.


c. At a small timescale (for instance, at the quantum level) can be considered as continuous at a much larger timescale where some irregularities are not perceived. Hence, we have to do with scales and relativity here or perhaps even with scale
relativity
(Auffray & Nottale, 2008, Nottale & Auffray, 2008).
Another aspect of such complex irregular systems is the event-triggered
emergence and development of abstract heterarchies (i.e. dynamical hierarchical
systems inheriting logical inconsistencies between levels) in terms of a
time/state-scale re-entrant forms which are very difficult to formalize as
dynamical systems because of their intrinsic inconsistencies (Gunji et al.,
2008). Finally, there are interactions between heterogeneous viewpoints
(models), modes or development stages of such systems (in a timeline that
extrapolates to evolution), while their self-organization depends on the
cooperation/competition between a net of internal regulatory processes executed
by physical entities (organs) or CoRegulators (CR’s) each operating in an
internalistic, endophysical manner (Rössler, 1998) at its own complexity level
and with its own temporality (Ehresmann & Vanbremeersch, 2007). Thus, events
can result from the interactions between CoRegulators. This interdependence can
be observed at all scales in (living) Nature. Ultimately, multiscale interactions
and their nonlocal characteristics at the deepest quantum lead to the question
and hypotheses about the emergence and evolution of consciousness (Hameroff
& Penrose, 1996).


d. Human beings, being large lumps of matter (made up of large numbers of cells, each made up of large numbers of complex molecules, atoms, etc.) comprehend the world as interactions, given the scale at which they see things and processes
as made up of very large numbers of interactions between them at lower levels
and registered as events.


e. Today we observe the emergence of a New Biology (Rose & Oakley, 2007; Connelly et al., 2009) fostering the integration of disciplines and institutions, the
collaboration and the systems approach in many advanced areas such as genomics
(Eckardt, 2001) and evolutionary genetics (Bard, 2010). However, despite this integrative tendency we still observe inaction and even resistance to place and develop biological research on rigorous theoretical foundations such as biological mathematics and biocomputation (Hong, 2005a,b; Simeonov, 2010).
Therefore, we accentuate on this important aspect of pivotal impact for
future European research.



f. Understanding ubiquitous complex events in living systems do certainly matter in medicine today: neuro- and electrophysiology (Wang & Ding, 2010; Toscano et al.,
2010; Nanova et al., 2010), neuropsychopharmacology (Kenemans & Kähkönen,
2010), noninvasive electrocardiology (Molon et al., 2010), etc.



g. Can the new software engineering paradigm of event-driven architectures and complex event processing for large enterprise systems (Luckham, 2002) gain ground as model and technology bridging and automating research in the life science
disciplines?




2. Events in the Real World (Motivation)



a. What we choose to call occurrences or events in this context thus depends on our viewpoint.



b. Computers we have designed to understand the world, help us to precisely identify and qualify events with the mediation of deterministic electronic logic.
Yet, they remain tools, machines and magnifying glasses, supporting a limited
(set of) viewpoint(s) backed by the development stage of science and
technology.



c. Each time human thought crossed a tenet’s border allowing a new assumption, new tools were developed to prove the new hypothesis and advanced our understanding of the world.



d. Real events, however, such as those we perceive, single or in or those which the structures we perceive (companies, families, nation-states, flocks of birds, etc.) are
more complex, and are made up of a lower-level structures which are not indefinitely characterisable, and which probably doesn’t need to be (Bard, 2010).




3. What is U-CEP in the context of Life Sciences and Medicine? (Discussion Part 1)



a. Which context do we mean? definitely the
context of system biology and of course, the context of cellular and molecular
biology, epigenetics, physiology, neuroscience & brain research, psychology
and healthcare in general.



b. Do today’s digital computers and the world we see reflect the ultimate reality at all
scales?



c. Is this yet another approach to the mind-body problem?



d. U-CEP could be regarded as a possible unifying model to cover all interactions in the
living world: interactions between cells, between collections of cells, organs
and individual living entities, swarms of such entities, etc. Such an approach
can only be measured by its utility.




4. Behind events, evolution and development (Discussion Part 2)



a. Are not most processes event-driven in the living body?



b. The evolution of things in the living nature.



c. From automata and reactive systems through (M,R) systems, to gene regulatory networks and (neo-)autopoiesis and beyond?



d. Events don’t themselves evolve. The underlying interactions change because of changes in energy distributions, changes in the matter around, etc. Living systems have captured this in order to live, and this is encapsulated in DNA making living
things different from non-living ones. Thus, events or what underlies them
(usually at the level of protein formation and the interaction of proteins)
does evolve.



e. If we can get a mathematics that helps us to get a better handle on it so much the better. What kind of mathematics do we need? Are there any
promising approaches? Some of them could be:


i. dynamic systems (non-linear dynamics and chaos dynamics, control theory)


ii. topology and algebraic topology (homotopy theory); (co)homological algebra


iii. category theory (included groupoids, topological or multiple categories)


iv. probability, stochastic processes


v. complex networks theory


vi. information theory


vii. the logic of (Gödelian) self-reference


viii. deductive reasoning


ix. any other?


These various domains should be blended into new mathematical constructions adapted to specific problems raised by biology and cognition (e.g. probabilistic categories generalizing random graphs).


f. Which problems (tasks) have to be solved on the way?


i. the large gap between the disciplines (physics, biology, logic)


ii. short-term vs. very long term research


iii. philosophy – holism vs. reductionism;
developing respect towards doubting attitude, skepticism and Platonism


iv. In a specific context (viewpoint of a particular regulatory process or
CR), characterize what to call events and distinguish between 'simple' changes of state (mere 'phenomena' in Pierce's meaning) and events as 'ruptures' (in Badiou's meaning) or 'fractures' (in Ehresmann’s meaning).


v. In a multi-scale system (both in terms of complexity and temporality),
analyze how regulatory processes (or CRs) at a given scale can give rise to
material or epistemological events at other scales, and how the interactions
between heterogeneous regulatory processes modulate the dynamics and evolution of the system.


vi. Characterize the properties at the root of complex events, such as
degeneracy (or multiplicity) properties ensuring the existence of
non-isomorphic structures sustaining the same functional role, thus allowing
for complex switches between them which increase the degree of freedom.


vii. Dynamic computer graphics for compositions of 3D curves: Development of mathematical algorithms and software for computer graphics imaging of rotated 3D curves and fractals.


viii. any other?


g. How can we get these results?


i. Develop mathematical models such as MES (Ehresmann & Vanbremeersch, 2007) of multi-scale systems and of their
self-organization through the 'interplay' between their different regulatory
processes accounting for their different complexities and rhythms. Examine the role of specific properties, for instance degeneracy (or multiplicity) properties.


ii. Fractals of 2D curves show interesting geometry. Fractals or
compositions of 3D curves show much more complex 3D geometry exhibiting interesting features. Their properties, however, could not be demonstrated with static views only. They need to be rotated. The preferable mathematical expression of a 3D curve is by parametric equations with 3 variables and a few parameters. Presently there is no computer software (incl. such systems as MathematicaTM, MathLabTM and MapleTM) that allows the creation, rotation and composition of 3D curves in order to get 3D fractals.


iii. any other?



5. Conclusions, next steps, roadmap



a. Increasing U-CEP based biological search and analysis methods towards an intelligent
methodology – starting from 2013 with increasingly enhanced event-driven types
and processes for research automation (heuristic database sequencing, etc.).



b. Driving and delivering novel biochemical technology based on self-assembling micro and nano- robots – starting from 2020 with increasingly enhanced event-types and
processes



c. Driving and delivering global ecological and space exploration – starting 2025



d. Challenges are not that much the U-CEP platforms/middleware, but the complex event and process models



e. Aims as part of a long-term FET-Flagship



f. Project partners from biotech and pharma industries, equipment suppliers, U-CEP platform providers, RTO’s (together with international partners like Harvard Medical
School, Stanford U, MIT, etc.)




6. References



Auffray, C., Nottale, L., 2008. Scale relativity and integrative systems biology. 1. Founding principles and scale laws. Prog. Biophys. Mol. Biol. 97, 79-114 (Elsevier).



Bard, J. 2010. A systems biology view of evolutionary genetics: network-driven processes incorporate much more variation than evolutionary genetics can handle. This
variation is hard to formalise but allows fast change. Bioessay. 32(7):559-63..
DOI: 10.1002/bies.200900166. http://www.ncbi.nlm.nih.gov/pubmed/20544731.



Brooks, D. R., Wiley, E. O. 1988. Evolution As Entropy. University Of Chicago Press. ISBN-10: 0226075745; ISBN-13: 978-0226075747.



Connelly, T. et al. 2009. A New Biology for the 21st Century. The National Academies Press. ISBN-10: 0-309-14488-4; ISBN-13: 978-0-309-14488-9. http://www.nap.edu/catalog/12764.html.



Eckardt, N. A. 2001. The New Biology. Genomics Fosters a “Systems Approach” and Collaborations between Academic, Government, and Industry Scientists. Plant Cell. 2001 April;
13(4): 725–732. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC526014/.



Ehresmann, A. C., Vanbremeersch, J.-P. 2007. Memory Evolutive Systems: Hierarchy, Emergence, Cognition. Elsevier Science. ISBN-10: 0444522441; ISBN-13: 978-0444522443.



Gunji, Y-P., Sasai, K., Wakisaka, S. 2008. Abstract heterarchy: Time/state-scale re-entrant form. Biosystems, Vol.91, 13-33. doi:10.1016/j.biosystems.2007.06.005.



Hameroff, S., Penrose, R. 1996. Orchestrated reduction of quantum coherence in brain microtubules: A model for consciousness. Mathematics and Computers in
Simulation. Volume 40, Issues 3-4, April 1996, 453-480.
doi:10.1016/0378-4754(96)80476-9.



Hong, F.T., 2005a. A multi-disciplinary survey of biocomputing: part 1: molecular and cellular aspects. In: Bajic, V.B., Wee, T.T. (Eds.), Information Processing and Living
Systems (Advances in Bioinformatics and Computational Biology). World
Scientific, pp. 1e139. ISBN-10: 1860945635; ISBN-13: 978-1860945632.



Hong, F.T., 2005b. A multi-disciplinary survey of biocomputing: part 2: systems and evolutionary aspects, and technological applications. In: Bajic, V.B., Wee, T.T. (Eds.), Information
Processing and Living Systems (Advances in Bioinformatics and Computational
Biology). World Scientific, pp. 141e573. ISBN-10: 1860945635; ISBN-13:
978-1860945632.



Kenemans J. L., Kähkönen, S. 2010. How Human Electrophysiology Informs Psychopharmacology: from Bottom-up Driven Processing to Top-Down Control. Neuropsychopharmacology. 2010
Oct 6. [Epub ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/20927044.




Lauffer M. A. 1975. Entropy-Driven Processes in Biology: Polymerization of Tobacco Mosaic Virus Protein and Similar Reactions (Molecular Biology, Biochemistry and Biophysics).
Springer. ISBN-10: 038706933X; ISBN-13: 978-0387069333.



Luckham, D. 2002. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Professional (May 18, 2002). ISBN-10:
0201727897; ISBN-13: 978-0201727890.



Molon, G., Solimene, F., Melissano, D., Curnis, A., Belotti, G., Marrazzo, N., Marczyk, J., Accardi, F., Raciti, G., Zecchi, P. 2010. Baseline heart rate variability predicts
clinical events in heart failure patients implanted with cardiac
resynchronization therapy: validation by means of related complexity index. Ann
Noninvasive Electrocardiol. 2010 Oct; 15(4):301-7. doi:
10.1111/j.1542-474X.2010.00384.x. http://www.ncbi.nlm.nih.gov/pubmed/20946551.



Nanova, P., Kolev V., Yordanova, J. 2010. Developmental gender differences in the synchronization of auditory event-related oscillations. Clin Neurophysiol. 2010 Oct 6. [Epub ahead
of print]. http://www.ncbi.nlm.nih.gov/pubmed/20933464.



Nicolis, G., Prigogine, I. 1977. Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order through Fluctuations. John Wiley & Sons.
ISBN-10: 0471024015; ISBN-13: 978-0471024019.



Nottale, L., Auffray, C., 2008. Scale relativity and integrative systems biology 2. Macroscopic quantum-type mechanics. Prog. Biophys. Mol. Biol. 97, 115-157.



Penrose, R. 2006. What is reality? The New Scientist. Volume 192, Issue 2578, 18 November 2006, 32-39. doi:10.1016/S0262-4079(06)61094-4.



Prigogine, I. 1997. The End of Certainty. Free Press. ISBN-10: 0684837056. ISBN-13: 978-0684837055.



Rose, M. R., Oakley, T, H. 2007. The new biology: beyond the Modern Synthesis. Biology Direct 2007, 2:30doi:10.1186/1745-6150-2-30. http://www.biology-direct.com/content/2/1/30.



Rössler, O.E., 1998. Endophysics: the World as an Interface. World Scientific, ISBN 981-02-2752-3.



Simeonov, P. L. 2010. Integral biomathics: a post-Newtonian view into the logos of bios. Progress in Biophysics and Molecular Biology. 2010 Jun-Jul; 102(2-3):85-121. ISSN: 0079-6107. doi:10.1016/j.pbiomolbio.2010.01.005.



Toscano, J. C., McMurray, B., Dennhardt, J., Luck, S. J. 2010. Continuous perception and graded categorization: electrophysiological evidence for a linear relationship between
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Wang, X., Ding, M. 2010. Relation between P300 and event-related theta-band synchronization: A single-trial analysis. Clin Neurophysiol. 2010 Oct 11. [Epub ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/20943435.





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Comment by Ria Baeck on December 7, 2010 at 11:08am
I surely can say that I don't understand the topics you are mentioning here, but I can say something about the process you want to use. In the program of the event - which I chekced out because it is happening in my country! - is an announcement of a World Café, but in seeing - or sensing - what you want to achieve I think you could better use a Pro Action Café - please find some .pdfs to download.

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