Hosting Conversations about Questions that Matter
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:
Abstract/Outline for Ghent workshop
Table 5 “Epigenetics/Cell Biology/New Biology/Healthcare and U-CEP”
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
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
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
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
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)
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
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.)
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