Adapting a Formal Model Theory to Applications in Augmented Personalized Medicine
📝 Abstract
The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation and man-machine interface. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.
💡 Analysis
The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation and man-machine interface. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.
📄 Content
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Adapting a Formal Model Theory to Applications in Augmented Personalized Medicine
Plamen L. Simeonov1 and Andrée C. Ehresmann2
- Charité - Universitätsmedizin, Berlin, DCGMS and JSRC, Germany; 2) LAMFA, Université de Picardie Jules Verne, Amiens, France.
Abstract: The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation, man-machine interface and creative design. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.
Key words: – Integral Biomathics, Artificial/Synthetic and Natural Life, Multi-Level Complex Systems, Wandering Logic Intelligence, Memory Evolutive Systems.
- Prelude
Human knowledge about the “nature of laws in nature” expanded in the past 50 years (Mikulecky, 2000, 2007; Maturana, 2000; Simeonov, 2010; Simeonov et al., 2012a). Researchers realize now that living systems exhibit essentially a non-linear dynamics featuring development at the edge of criticality, (Bak et al., 1987 ; Hankey, 2015). However, many structures and functions at the systems level overlap suggesting that they might not have clear physiological definition. Perhaps knowing the fact that Biological First Principles (Torday, 2017) define cellular freedom of action and cellular constraints which are the action central of biology/evolution might help developing more realistic models. But mastering the modeling of complex biological systems is still a serious challenge. Denis Noble has hosted prominent work on defining the problem (Noble, 2012, 2015). In this juncture he appealed for practical and sustainable models that unite the different levels of representation. To achieve this goal, researchers are asked to adopt a more holistic and ‘organic’ view of life that goes beyond general system theory (von Bertalanffy, 1950, 1968), systems biology (Kitano, 2001, 2002; Bard et al., 2013; Covert, 2015) and fuzzy set systems (Zadeh, 1965, 1975, 2004) into the realms of quantum interactions (Gurwitsch, 1922, 1944, 1947; Popp, 1992, 2003; Hameroff and Penrose, 1996; McFaden, 2002, 2011; Beloussov and Voelkov, 2007; Lozneanu and Sandoluviciu, 2008; Beloussov, 2008; Arndt et al., 2009; Levin, 2011, 2012) and multiverse theory (Everett, 1956/1973, 1957). But along the way they face two special challenges. On the one hand, there are extremely complex information and energy flows in living systems. Characteristic for this approach is the appraisal of living systems balanced at multiple levels from molecules through cells, and tissues to organ(ism)s and (eco)systems. This assembly needs to be adequately comprehended and reconstructed in order to solve severe problems such as developmental and epigenetic disorders, autoimmune diseases, limiting and extirpating a virus outbreak, etc. On the other hand, the present specialized biomedical tools are certainly insufficient to bring down new theories to everyday practice easily and to investigate in silico. But also physicians and medical assistants, dealing with these issues lack a sufficient background in mathematics and computer science to first devise theories1 and then model and test them with experiments in the way that physicists have been practicing this in their own domain for ages.
1 Deriving a theory from (“big”) data – induction – “is not a valid method for scientific proof” (Francis, 2017; Preface).
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2 This i
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