Collins Conference Room
Seminar
  US Mountain Time

Our campus is closed to the public for this event.

Jack Winters (Marquette University)

Abstract.  Perhaps missing from the short lists of the great examples of CAS is “living tissue.”  Living tissues form a dynamic bridge between organ systems and cell communities, providing 3D structure and functional capacity.  As local demands change, cooperative adaptive processes strive to enhance the greater self-good (“fitness”).  Growing tissue is complex – just ask tissue bioengineers who’ve strugged to find the optimal mix of cell type diversity plus nurturing biochemical/biomechanical/bioelectrical conditions that enable context-aware, decentralized interconnectivity (including angiogenesis).  

In terms of the great interwoven information-material-energy trinity at the pillar of the laws of nature, muscle tissue provides an intimate natural bridge between neural expression and connective tissue structural/functional design.  A clever neuro-mechanical strategy enables coordinated actuation of millions of nano-motors distributed within a volume, each accessing “wireless energy” via a tightly-regulated ATP supply.  The same basic “winning” myo-design, with a few variants (e.g., ~5 for adult skeletal, 2 for cardiac, ~2 for smooth), enables creation of muscle actuators possessing a remarkable diversity of sizes and functional capacities (e.g., insect wings, elephant legs, beating hearts).

High-throughput microarray mRNA data from a typical skeletal muscle tissue sample finds ~35-40% of genetic content to be “expressed” (e.g., z-scores >-0.1), but to remarkably different degrees (e.g., top 5% with z>1.1, 2% with z>9, with top players changing dramatically with pathology).  These transcripts mostly come from ~7 of the ~250  “potential information field” cell types (e.g., 3 muscle types, fibroblasts, endothelial, vascular smooth muscle, satellite).  The portfolio includes >40% that can be classified as specifying local “controls apparatus” (e.g., ~10% for each for sensory receptors, network cascades,  materials/energy regulators, transcription factors (TFs)).  We’ve recently partitioned the myo-collection into 5 transcript-protein families (1 mechanical, 1 energetic, 3 informational), each with 3-6 subfamilies.    Unlike our engineered motors, muscle tissues selflessly remodel (up-/down-regulate parts) within all families (mechanical, energetic, informational) based on macro-intuitive yet illusive cyber-rules sampling local “use history” (e.g., self-generated action, micro-trauma, endocrine signals). 

Two myo-tissue modeling approaches are briefly presented: i) our “real-time” modeling strategy that extends my work in the 1980s-90s to a “living muscle model” that integrates energetic as well as mechanical-informational dynamic modeling and explicitly ties model parameters to strategic transcript-protein z-scores (e.g., “personalized” for diseased muscle); and ii) our “adaptive-time” modeling strategy based on “trans-genetic soft fuzzy-agent” cyber-nets (TF-nets) that emphasizes TF-circuitry motifs and aims to predict classic resistance and endurance training data, and various muscle “disuse” paradigms.

In closing, it seems that TF-nets – the pillars of living systems - are designed and organized  differently from many CAS designs.  For example, in neural networks it is connectivity rather than diversity that matters most (“who you know” and “what module/hub you are in”), yet in TF-nets it is “what are your skills” and “who do you work for” that matters, with trans-input connectivity limited to ~10.  A typical tissue-specific “portfolio” could be viewed as specialized widgets available to a “workforce” of skilled TF-agents managing cyber-regulation, with no CEO.   There are analogies to infrastructure support in cities; perhaps we can learn from some of the CcAS strategies used by robust living tissues such as muscle.  

Purpose: 
Research Collaboration
SFI Host: 
Jennifer Dunne

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