#1

Summary from chatGPT

Here’s a concise overview of Léo Pio-Lopez’s talk “Aging, Goal-Directedness and Bioelectricity” from the Foresight Virtual Seminar Series:

1. Introduction (00:00–00:33)
Léo Pio-Lopez, Senior Scientist at the Allen Discovery Center (Tufts University), opens by framing his work at the intersection of computational biology, machine learning and regenerative medicine. He outlines three main research thrusts: understanding morphogenetic systems through a cognitive lens, dissecting bioelectric dynamics in development and aging, and borrowing biological principles to drive new AI architectures (Luma).

2. Overview of Presentation Topics (00:33–02:10)
He briefly previews the talk structure:

  • Applying a “cognitive lens” to biological systems
  • Delving into bioelectric signaling during development and its dysregulation in aging
  • Proposing how restoring goal-directed bioelectric patterns might form the basis of novel anti-aging interventions (YouTube ).

3. Cognitive Lens on Biological Systems (02:10–09:05)
Pio-Lopez argues that multicellular systems—from cell clusters to whole organisms—operate with goal-directed behaviors (e.g., maintaining tissue shape, repairing wounds). He draws on evolutionary simulations showing that “aging” can emerge when the evolved goal-state of maintenance is insufficiently prioritized after development is complete, leading to drift away from homeostatic targets (Preprints).

4. Bioelectricity and Its Role in Development (09:05–15:39)
Next, he highlights the importance of endogenous electric fields and voltage gradients in guiding cell proliferation, pattern formation and regenerative processes. Using examples from amphibian limb regeneration to organogenesis, he shows how bioelectric cues serve as an instructive layer above genetic regulation, effectively “programming” cells toward a collective goal (ResearchGate).

5. Linking Bioelectric Dysregulation to Aging
Building on the previous sections, he proposes that age-related decline reflects a breakdown in the bioelectric encoding of anatomical goal-states. As tissues lose proper voltage patterning, their capacity for repair and maintenance wanes—manifesting as the morphological and functional hallmarks of aging (ScienceDirect).

6. Toward Aging Interventions
In closing, Pio-Lopez discusses potential intervention strategies:

  • Machine-learning–driven discovery of small-molecule modulators of ion channels
  • Bioelectronic devices to re-establish youthful voltage patterns in aging tissues
  • Integrative AI architectures inspired by morphogenetic control loops, aimed at orchestrating systemic rejuvenation (Luma).

He emphasizes that decoding and manipulating the bioelectric “goal maps” of tissues could unlock regenerative therapies that go beyond symptom management to truly reverse age-associated deficits.

This talk weaves together computational simulations, fundamental bioelectric research and translational ambition, positioning “goal-directed bioelectricity” as a unifying principle for understanding—and ultimately intervening in—the aging process.

#2

I got the transcript and asked chatGPT to tidy it up

Moderator (00:00)
Hello everyone, and welcome to Foresight’s Biotech Group, sponsored by 100+ Capital. Today we have Leo Pilo‐Lopez with us from the Levin Lab. He’ll be talking about goal‐directedness and bioelectricity—our first speaker on that topic. Thank you so much for joining us, Leo.

Leo Pilo‐Lopez
Thank you very much for the introduction and thank you all for being here. I’m Leo Pilo‐Lopez, working with Michael Levin, and today I’ll present our work on aging, goal‐directedness, and bioelectricity. My talk will last about 40 minutes, and I’ll cover three main points:

  1. The “cognitive lens” on biological systems, explaining why we view biology as a hierarchy of goal‐directed agents.
  2. Bioelectricity as a tractable interface for top-down control of growth and form.
  3. Aging as a loss of goal-directedness, viewed not only as damage accumulation but as a collapse or redirection of biological goals—driven by corruption of bioelectric patterns and changes in gene expression.

1. Cognitive Lens on Biological Systems

  • Even single cells (e.g., Paramecium) exhibit “competency”—they navigate, remember, and solve problems without a brain.
  • Molecular networks (gene regulatory networks) can show forms of memory and Pavlovian‐style learning.
  • We propose multiscale competency architecture: from genes to cells to tissues to whole organisms, each level operates with its own goal within a larger system.
  • Cognitive “light cones” illustrate the region of space-time over which a system can perceive, act, and achieve its goals; larger cones mean more sophisticated, longer-range goals.

Defining a Goal:
A “goal” is a target state in a defined problem space (e.g., anatomical shapes for organisms, transcriptional states for cells). A system expends energy to return to or maintain that state, and more sophisticated agents can reach the same goal from diverse starting points.

Examples of Goal-Directed Navigation:

  • Planaria in transcriptional space: Barium treatment blocks potassium channels, “explodes” the head, then regenerates a barium-resistant head—despite no prior exposure.
  • Salamander limb regeneration: Cut at different points yet always regrows the correct limb—demonstrating navigation in “morphospace.”
  • Embryo twinning: A cut embryo splits and still forms two complete twins, again hitting the same anatomical target from different starting conditions.

2. Bioelectricity: The “Software of Life”

  • Before fast neurons arose, cells used the same ion‐channel hardware and bioelectric “software” for local information processing to regulate growth and patterning in morphospace.
  • We can pharmacologically or genetically manipulate ion channels to hyperpolarize/depolarize cells, rewriting their target patterns.
  • Ectopic organ induction: In vivo bioelectric manipulation alone (no genetic edits) can induce organs—eyes, hearts, limbs—in locations they shouldn’t form.
  • Pre-patterns in development: In frog embryos, bioelectric patterns (shown in grayscale imaging) prefigure where eyes and mouths will form.
  • Cancer diagnostics: Tumors display distinctive bioelectric signatures, suggesting applications beyond regeneration.
  • Collective behavior: Introducing ion channels in some cells can recruit neighbors to a new bioelectric state, offloading complexity to the tissue collective.

Implication for Biomedicine:
Current therapies operate at the “hardware” gene/molecule level—equivalent to manually rewiring early computers. Bioelectricity offers a high-level, top-down interface for instructing cellular collectives to solve complex patterning problems.

3. Aging as Loss of Goal-Directedness

A. Bioelectricity in Aging

  • We reviewed literature linking every hallmark of aging to ion channels and gap junction changes.

  • Hypotheses:

    1. Aging arises partly from corruption of the bioelectric pattern over time.
    2. Aging reflects loss of cellular competency to interpret bioelectric cues.
    3. A feedback loop between pattern corruption and interpretive failure.

  • Evidence: Senescent cells show characteristic membrane depolarization; immortal hydra exhibit unique bioelectric patterns.

B. Computational Model of Aging

  • We built an evolved neural‐cellular automaton: each cell has an artificial neural network controlling local behavior, and evolution optimizes development (a “smiley face” target).
  • Result: After reaching the developmental goal, organisms spontaneously degrade—organs disappear, anatomy breaks down—without added noise or damage.
  • Interpretation: Aging emerges as loss of goal-directed morphostasis once the developmental goal has been achieved.
  • Adding known aging factors (genetic damage, communication breakdown) merely accelerates degradation; loss of goal‐directedness is primary.

C. Rejuvenation via Embryonic Cues

  • In silico, replacing mis‐expressed cells with their embryonic state reactivates regeneration and restores anatomy—paralleling “partial reprogramming” (Yamanaka factors) in real tissues.

D. Phylogenetic (Phylo) Transcriptomic Analysis

  • We mapped differentially expressed aging genes onto “phylo-strata” (evolutionary age of genes).
  • Finding: Aging tissues show dissociated expression of very ancient (unicellular-level) genes—sometimes up, sometimes down—mirroring the “atavistic” theory of cancer. Brain and stem cells are exceptions.

E. Unified View in the Cognitive Light Cone

  • During development: Cells align on the organism-level goal (large light cone).
  • Post-development: Organism-level goal is lost; cells shift to their own smaller goals (smaller cones), desynchronize, and anatomy degrades.

Future Directions & Therapeutic Implications

  • Integrate with classical aging theories (damage accumulation, programmed aging) to see where they align or conflict.

  • Experimentally test predictions:

    • Does reimposing embryonic bioelectric patterns rejuvenate mammalian tissues?
    • Can we develop targeted “electroceuticals” (ion channel–modulating drugs) for regeneration and anti-aging?
    • How do healthy versus pathological bioelectric landscapes differ in mammals?

  • Harness cellular competency: Training cells/tissues with bioelectric cues as one would train an AI system, offloading complexity to collective intelligence.

Moderator
Thank you so much, Leo—that was fascinating and impressively multidisciplinary. We’ll start with audience questions.

Audience Q&A

Micah: Why prefer the model that aging is loss of goal-directedness, rather than a shift to a new “aging” goal?
Leo: Excellent point—it’s hard to measure “goals” directly. In our computational models, no new organism-level goal was imposed, yet degradation still occurs. Info-theoretic analyses show no new synchronization that you’d expect if aging were a programmed, organism-level goal.

Micah (follow-up): In your simulations, did you try training a short homeostasis phase after development? Would that stabilize anatomy indefinitely?
Leo: Yes—we ran variants where evolution selected for both development and a brief homeostasis period. Most still degraded afterward. To maintain form, a perpetual regeneration goal would need to be learned.

Abdul: What makes some bioelectric circuits more stable—especially given environmental EM influences (power lines, etc.)—and how might that relate to healthy aging?
Leo: Environmental perturbations can corrupt bioelectric patterns. Highly regenerative organisms (planaria, salamanders) may maintain a “never-ending” regeneration goal, preserving form despite damage. Understanding their bioelectric resilience could guide anti-aging strategies.

Patrick: How can we image bioelectric states in 3D tissues beyond flat models like planaria?
Leo: Our lab’s experts are developing 3D voltage-sensitive imaging in vertebrate models; this is an active technical frontier but progressing rapidly.

Jason: Do you envision anti-aging interventions via purely bioelectric reprogramming, or needing genetic tools too? What if cells lack machinery for a new goal (e.g., repairing mitochondrial DNA)?
Leo: Bioelectric cues act top-down: we impose a target pattern, and cells execute the downstream genetic program themselves. In practice, combining electroceuticals with genetic or immune modulators might be optimal, especially if certain machinery is missing or suppressed.

Moderator
That’s all the time we have today. Thank you again, Leo—this was a great session—and thanks to everyone for the excellent questions. See you next month!

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