Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:
graph LR
classDef systems fill:#f9d4d4, font-weight:bold, font-size:14px
classDef aging fill:#d4f9d4, font-weight:bold, font-size:14px
classDef cells fill:#d4d4f9, font-weight:bold, font-size:14px
classDef research fill:#f9f9d4, font-weight:bold, font-size:14px
classDef evolution fill:#f9d4f9, font-weight:bold, font-size:14px
Main[Programming Multiscale Health] --> S[Systems Biology]
Main --> A[Aging Process]
Main --> C[Cellular Dynamics]
Main --> R[Research Advances]
Main --> E[Evolution Patterns]
S --> S1[Life thrives away from
balance 1]
S --> S2[Systems organize many scales 2]
S --> S3[Small chaos creates order 3]
S --> S4[Biology shows system levels 16]
S --> S5[Cell goals conflict body 17]
A --> A1[Aging optimizes outside range 9]
A --> A2[Evolution ignores post-reproduction 10]
A --> A3[Extended breeding delays aging 11]
A --> A4[Early benefits become diseases 12]
A --> A5[Healing shifts toward damage 13]
C --> C1[Cells compete work together 8]
C --> C2[Age makes inflammation harmful 14]
C --> C3[Cell aging role changes 15]
C --> C4[Cells show programming flex 21]
C --> C5[Cell states follow maps 22]
C --> C6[Birth cells reset age 23]
R --> R1[Computer models improve health 20]
R --> R2[Clones prove renewal possible 24]
R --> R3[Yamanaka enables cell potential 25]
R --> R4[Altos builds biology models 26]
R --> R5[Research joins math labs 27]
E --> E1[Evolution matches learning patterns 4]
E --> E2[Life maximizes breeding success 5]
E --> E3[Environment changes update states 6]
E --> E4[Learning mirrors mutation rates 7]
E --> E5[Reproduction beats longevity 19]
R5 --> R6[Three institutes collaborate 28]
R5 --> R7[New factors guide change 29]
R4 --> R8[Data spans micro macro 30]
E5 --> E6[Long breeding fights cancer 18]
class Main,S,S1,S2,S3,S4,S5 systems
class A,A1,A2,A3,A4,A5 aging
class C,C1,C2,C3,C4,C5,C6 cells
class R,R1,R2,R3,R4,R5,R6,R7,R8 research
class E,E1,E2,E3,E4,E5,E6 evolution
Resume:
1.- Living systems operate far from equilibrium
2.- Systems exhibit multiscale organization
3.- Microscopic chaos yields macroscopic order
4.- Evolution parallels multi-agent reinforcement learning
5.- Organisms optimize for reproductive fitness
6.- Environmental changes trigger state updates
7.- Mutations relate to learning rates
8.- Cells work competitively and cooperatively
9.- Aging represents out-of-distribution optimization problem
10.- Post-reproductive periods lack evolutionary optimization
11.- Longer reproductive lifespans delay aging
12.- Disease patterns reflect early beneficial functions
13.- Fibrosis shifts from healing to harmful
14.- Inflammation becomes maladaptive with age
15.- Cellular senescence changes role over time
16.- Multiple hierarchies exist in biological systems
17.- Organism-cell rewards may conflict
18.- Cancer resistance correlates with reproductive lifespan
19.- Living systems prioritize reproduction over longevity
20.- In silico fine-tuning could optimize health
21.- Cells demonstrate programming malleability
22.- Waddington landscape shows cell state transitions
23.- Reproductive cells reset age state
24.- Cloning demonstrates cell reprogramming potential
25.- Yamanaka factors enable pluripotency
26.- Altos develops multiscale biological models
27.- Company combines computation with experiments
28.- Three research institutes collaborate
29.- Novel transcription factors guide intervention
30.- Data collection spans cell to organism
Knowledge Vault built byDavid Vivancos 2024
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