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Abstract The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels "decide" to change phenotype—remains descriptive. We propose Neural Mycelium, a project that applies Mechanistic Interpretability to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the InterpretabilitySentinel logic, we aim to identify the specific logic gates governing Endothelial-to-Mesenchymal Transition (EndMT) in chronic inflammatory states. 2. Scientific Rationale & Hypotheses Traditional differential expression analysis identifies what genes change, but not the logic of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling "motifs" that function similarly to the attention heads in transformer architectures. Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity. Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis. 3. Specific Aims Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories. Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium. Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity. 4. Feasibility & Timeline (6 Months) Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data. Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits. Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal. 5. Budget & Open Science Commitment Funding Goal: $10,000 USD (Pilot Phase). Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees. Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability. Personal Staking Commitment I am an active ResearchHub ecosystem participant with a currently staked RSC balance, demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the "Neural Mycelium" project, I pledge to re-stake 80% of this bounty reward immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research.59:Tc95,PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability 1. Abstract The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels "decide" to change phenotype—remains descriptive. We propose Neural Mycelium, a project that applies Mechanistic Interpretability to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the InterpretabilitySentinel logic, we aim to identify the specific logic gates governing Endothelial-to-Mesenchymal Transition (EndMT) in chronic inflammatory states. 2. Scientific Rationale & Hypotheses Traditional differential expression analysis identifies what genes change, but not the logic of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling "motifs" that function similarly to the attention heads in transformer architectures. Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity. Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis. 3. Specific Aims Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories. Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium. Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity. 4. Feasibility & Timeline (6 Months) Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data. Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits. Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal. 5. Budget & Open Science Commitment Funding Goal: $10,000 USD (Pilot Phase). Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees. Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability. Personal Staking Commitment I am an active ResearchHub ecosystem participant with a currently staked RSC balance, demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the "Neural Mycelium" project, I pledge to re-stake 80% of this bounty reward immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research.5a:T2575,{"type":"doc","content":[{"type":"heading","attrs":{"id":"b819ef80-5dea-4763-92bc-e8e536cd210c","data-toc-id":"b819ef80-5dea-4763-92bc-e8e536cd210c","textAlign":null,"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability"}]},{"type":"heading","attrs":{"id":"40cba43a-1cd6-4c5b-bd5c-9c4c42957c9d","data-toc-id":"40cba43a-1cd6-4c5b-bd5c-9c4c42957c9d","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1. Abstract"}]},{"type":"paragraph","attrs":{"id":"7352b218-fac6-4704-84fa-525492767a0a","class":null,"textAlign":null},"content":[{"type":"text","text":"The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels \"decide\" to change phenotype—remains descriptive. We propose "},{"type":"text","marks":[{"type":"bold"}],"text":"Neural Mycelium"},{"type":"text","text":", a project that applies "},{"type":"text","marks":[{"type":"bold"}],"text":"Mechanistic Interpretability"},{"type":"text","text":" to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the "},{"type":"text","marks":[{"type":"bold"}],"text":"InterpretabilitySentinel"},{"type":"text","text":" logic, we aim to identify the specific logic gates governing "},{"type":"text","marks":[{"type":"bold"}],"text":"Endothelial-to-Mesenchymal Transition (EndMT)"},{"type":"text","text":" in chronic inflammatory states."}]},{"type":"heading","attrs":{"id":"b887b0f7-b18c-4eda-925f-55f5cac4a8e4","data-toc-id":"b887b0f7-b18c-4eda-925f-55f5cac4a8e4","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2. Scientific Rationale & Hypotheses"}]},{"type":"paragraph","attrs":{"id":"258868a1-6b27-4f1a-b072-0b77519e228e","class":null,"textAlign":null},"content":[{"type":"text","text":"Traditional differential expression analysis identifies "},{"type":"text","marks":[{"type":"italic"}],"text":"what"},{"type":"text","text":" genes change, but not the "},{"type":"text","marks":[{"type":"italic"}],"text":"logic"},{"type":"text","text":" of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling \"motifs\" that function similarly to the attention heads in transformer architectures."}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"2e387ce7-f697-4516-9373-0c0d922f4511","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Hypothesis 1 ("},{"type":"text","marks":[{"type":"textStyle","attrs":{"fontSize":"","fontFamily":"\"Google Sans Text\", sans-serif","color":""}},{"type":"bold"}],"text":"$H_1$"},{"type":"text","marks":[{"type":"bold"}],"text":"):"},{"type":"text","text":" Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"ae2efe67-d5df-4617-948a-76598d1d17db","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Hypothesis 2 ("},{"type":"text","marks":[{"type":"textStyle","attrs":{"fontSize":"","fontFamily":"\"Google Sans Text\", sans-serif","color":""}},{"type":"bold"}],"text":"$H_2$"},{"type":"text","marks":[{"type":"bold"}],"text":"):"},{"type":"text","text":" Mechanistic interpretability audits of these graphs can isolate the specific \"circuits\" driving EndMT with higher predictive accuracy than linear pathway analysis."}]}]}]},{"type":"heading","attrs":{"id":"689640d9-b2dc-46f1-a535-499673799995","data-toc-id":"689640d9-b2dc-46f1-a535-499673799995","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3. Specific Aims"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"e1eda403-7aad-4cf4-968a-f70ac7a870a3","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Aim 1: Graph Construction."},{"type":"text","text":" Build a "},{"type":"text","marks":[{"type":"bold"}],"text":"Distributed Signaling Graph (DSG)"},{"type":"text","text":" using a "},{"type":"text","marks":[{"type":"bold"}],"text":"Vector Mesh"},{"type":"text","text":" architecture to integrate spatial proteomics and scRNA-seq data from the "},{"type":"text","marks":[{"type":"bold"}],"text":"Tabula Sapiens"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"bold"}],"text":"GTEx"},{"type":"text","text":" repositories."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"6057c4e0-8944-4d65-bf53-03171fc0cb11","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Aim 2: Circuit Audit."},{"type":"text","text":" Apply "},{"type":"text","marks":[{"type":"bold"}],"text":"InterpretabilitySentinel"},{"type":"text","text":" logic to audit the DSG, specifically seeking motifs related to "},{"type":"text","marks":[{"type":"bold"}],"text":"TGF-β"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"bold"}],"text":"Wnt"},{"type":"text","text":" signaling that precede physical morphological changes in the endothelium."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"c11355f0-9b80-49ce-ae23-c03b3dbb3bc7","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Aim 3: In Silico Validation."},{"type":"text","text":" Use the "},{"type":"text","marks":[{"type":"bold"}],"text":"DialecticEngine"},{"type":"text","text":" to perform high-throughput \"knockout\" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity."}]}]}]},{"type":"heading","attrs":{"id":"8a873364-b2d7-490c-bff1-93b7f665b026","data-toc-id":"8a873364-b2d7-490c-bff1-93b7f665b026","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"4. Feasibility & Timeline (6 Months)"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"9a79ebac-4be1-4da6-bba6-2374b6be9d30","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Months 1-2:"},{"type":"text","text":" Dataset ingestion and preprocessing of open-access human vascular data."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"523f8f53-e9c6-4e60-a105-4330e488cd29","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Months 3-4:"},{"type":"text","text":" DSG construction and initial motif identification via Mechanistic Interpretability audits."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"b2bc013e-3ef3-4e0b-8bc2-fa1dd5507891","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Months 5-6:"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"In silico"},{"type":"text","text":" validation of \"Neural Mycelium\" motifs and submission of a "},{"type":"text","marks":[{"type":"bold"}],"text":"Registered Report"},{"type":"text","text":" to the ResearchHub Journal."}]}]}]},{"type":"heading","attrs":{"id":"8ab35de7-f704-40c1-97cf-da8d121700d0","data-toc-id":"8ab35de7-f704-40c1-97cf-da8d121700d0","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"5. Budget & Open Science Commitment"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"ec91ab45-0fcc-45fd-a2ce-e9f5f63fc18c","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Funding Goal:"},{"type":"text","text":" $10,000 USD (Pilot Phase)."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"2fa6ef66-b19a-4def-bfab-3cf187ebef70","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Allocation:"},{"type":"text","text":" 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"6d2c3896-749d-4cd7-83cc-28262b4ff2b8","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Open Science:"},{"type":"text","text":" All code will be released under a "},{"type":"text","marks":[{"type":"bold"}],"text":"MIT License"},{"type":"text","text":" on GitHub, and data models will be shared under "},{"type":"text","marks":[{"type":"bold"}],"text":"CC-BY-4.0"},{"type":"text","text":" to ensure full community replicability."}]}]}]},{"type":"paragraph","attrs":{"id":"3412ac7b-1051-4524-b1ca-2dbdbc02d82b","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Personal Staking Commitment"},{"type":"text","text":" I am an active ResearchHub ecosystem participant with a currently "},{"type":"text","marks":[{"type":"bold"}],"text":"staked RSC balance"},{"type":"text","text":", demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the \"Neural Mycelium\" project, I pledge to "},{"type":"text","marks":[{"type":"bold"}],"text":"re-stake 80% of this bounty reward"},{"type":"text","text":" immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research."}]}]}5b:Tc95,PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability 1. Abstract The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels "decide" to change phenotype—remains descriptive. We propose Neural Mycelium, a project that applies Mechanistic Interpretability to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the InterpretabilitySentinel logic, we aim to identify the specific logic gates governing Endothelial-to-Mesenchymal Transition (EndMT) in chronic inflammatory states. 2. Scientific Rationale & Hypotheses Traditional differential expression analysis identifies what genes change, but not the logic of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling "motifs" that function similarly to the attention heads in transformer architectures. Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity. Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis. 3. Specific Aims Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories. Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium. Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity. 4. Feasibility & Timeline (6 Months) Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data. Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits. Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal. 5. Budget & Open Science Commitment Funding Goal: $10,000 USD (Pilot Phase). Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees. Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability. Personal Staking Commitment I am an active ResearchHub ecosystem participant with a currently staked RSC balance, demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the "Neural Mycelium" project, I pledge to re-stake 80% of this bounty reward immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research.5c:T2575,{"type":"doc","content":[{"type":"heading","attrs":{"id":"b819ef80-5dea-4763-92bc-e8e536cd210c","data-toc-id":"b819ef80-5dea-4763-92bc-e8e536cd210c","textAlign":null,"level":2},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability"}]},{"type":"heading","attrs":{"id":"40cba43a-1cd6-4c5b-bd5c-9c4c42957c9d","data-toc-id":"40cba43a-1cd6-4c5b-bd5c-9c4c42957c9d","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1. Abstract"}]},{"type":"paragraph","attrs":{"id":"7352b218-fac6-4704-84fa-525492767a0a","class":null,"textAlign":null},"content":[{"type":"text","text":"The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels \"decide\" to change phenotype—remains descriptive. We propose "},{"type":"text","marks":[{"type":"bold"}],"text":"Neural Mycelium"},{"type":"text","text":", a project that applies "},{"type":"text","marks":[{"type":"bold"}],"text":"Mechanistic Interpretability"},{"type":"text","text":" to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the "},{"type":"text","marks":[{"type":"bold"}],"text":"InterpretabilitySentinel"},{"type":"text","text":" logic, we aim to identify the specific logic gates governing "},{"type":"text","marks":[{"type":"bold"}],"text":"Endothelial-to-Mesenchymal Transition (EndMT)"},{"type":"text","text":" in chronic inflammatory states."}]},{"type":"heading","attrs":{"id":"b887b0f7-b18c-4eda-925f-55f5cac4a8e4","data-toc-id":"b887b0f7-b18c-4eda-925f-55f5cac4a8e4","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2. Scientific Rationale & Hypotheses"}]},{"type":"paragraph","attrs":{"id":"258868a1-6b27-4f1a-b072-0b77519e228e","class":null,"textAlign":null},"content":[{"type":"text","text":"Traditional differential expression analysis identifies "},{"type":"text","marks":[{"type":"italic"}],"text":"what"},{"type":"text","text":" genes change, but not the "},{"type":"text","marks":[{"type":"italic"}],"text":"logic"},{"type":"text","text":" of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling \"motifs\" that function similarly to the attention heads in transformer architectures."}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"2e387ce7-f697-4516-9373-0c0d922f4511","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Hypothesis 1 ("},{"type":"text","marks":[{"type":"textStyle","attrs":{"fontSize":"","fontFamily":"\"Google Sans Text\", sans-serif","color":""}},{"type":"bold"}],"text":"$H_1$"},{"type":"text","marks":[{"type":"bold"}],"text":"):"},{"type":"text","text":" Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"id":"ae2efe67-d5df-4617-948a-76598d1d17db","class":null,"textAlign":null},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Hypothesis 2 ("},{"type":"text","marks":[{"type":"textStyle","attrs":{"fontSize":"","fontFamily":"\"Google Sans Text\", sans-serif","color":""}},{"type":"bold"}],"text":"$H_2$"},{"type":"text","marks":[{"type":"bold"}],"text":"):"},{"type":"text","text":" Mechanistic interpretability audits of these graphs can isolate the specific \"circuits\" driving EndMT with higher predictive accuracy than linear pathway analysis."}]}]}]},{"type":"heading","attrs":{"id":"689640d9-b2dc-46f1-a535-499673799995","data-toc-id":"689640d9-b2dc-46f1-a535-499673799995","textAlign":null,"level":3},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3. 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To further show my commitment to the \"Neural Mycelium\" project, I pledge to "},{"type":"text","marks":[{"type":"bold"}],"text":"re-stake 80% of this bounty reward"},{"type":"text","text":" immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research."}]}]}5d:T16a0,

PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability

1. Abstract

The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels "decide" to change phenotype—remains descriptive. We propose Neural Mycelium, a project that applies Mechanistic Interpretability to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the InterpretabilitySentinel logic, we aim to identify the specific logic gates governing Endothelial-to-Mesenchymal Transition (EndMT) in chronic inflammatory states.

2. Scientific Rationale & Hypotheses

Traditional differential expression analysis identifies what genes change, but not the logic of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling "motifs" that function similarly to the attention heads in transformer architectures.

Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity.
Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis.

3. Specific Aims

Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories.
Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium.
Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity.

4. Feasibility & Timeline (6 Months)

Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data.
Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits.
Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal.

5. Budget & Open Science Commitment

Funding Goal: $10,000 USD (Pilot Phase).
Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees.
Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability.

Personal Staking Commitment I am an active ResearchHub ecosystem participant with a currently staked RSC balance, demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the "Neural Mycelium" project, I pledge to re-stake 80% of this bounty reward immediately upon receipt. This ensures that the majority of the funding remains within the ecosystem to support ongoing governance and future phases of this research.

5e:T16a0,

PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability

1. Abstract

2. Scientific Rationale & Hypotheses

Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity.
Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis.

3. Specific Aims

Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories.
Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium.
Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity.

4. Feasibility & Timeline (6 Months)

Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data.
Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits.
Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal.

5. Budget & Open Science Commitment

Funding Goal: $10,000 USD (Pilot Phase).
Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees.
Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability.

5f:Tc95,PROPOSAL: Neural Mycelium: Decoding Vascular Signaling Plasticity via Mechanistic Interpretability 1. Abstract The vascular system integrates multi-modal mechanical and immune signals to maintain tissue homeostasis. Despite advances in high-resolution sequencing, our understanding of vascular signaling plasticity—how vessels "decide" to change phenotype—remains descriptive. We propose Neural Mycelium, a project that applies Mechanistic Interpretability to treat the vascular signaling network as a distributed computational graph. By auditing these networks with the InterpretabilitySentinel logic, we aim to identify the specific logic gates governing Endothelial-to-Mesenchymal Transition (EndMT) in chronic inflammatory states. 2. Scientific Rationale & Hypotheses Traditional differential expression analysis identifies what genes change, but not the logic of the transition. We hypothesize that vascular plasticity is governed by identifiable signaling "motifs" that function similarly to the attention heads in transformer architectures. Hypothesis 1 ($H_1$): Vascular signaling networks contain non-linear, identifiable motifs that function as logic gates for phenotypic plasticity. Hypothesis 2 ($H_2$): Mechanistic interpretability audits of these graphs can isolate the specific "circuits" driving EndMT with higher predictive accuracy than linear pathway analysis. 3. Specific Aims Aim 1: Graph Construction. Build a Distributed Signaling Graph (DSG) using a Vector Mesh architecture to integrate spatial proteomics and scRNA-seq data from the Tabula Sapiens and GTEx repositories. Aim 2: Circuit Audit. Apply InterpretabilitySentinel logic to audit the DSG, specifically seeking motifs related to TGF-β and Wnt signaling that precede physical morphological changes in the endothelium. Aim 3: In Silico Validation. Use the DialecticEngine to perform high-throughput "knockout" simulations on identified circuits to predict which nodes, when silenced, most effectively halt pathological plasticity. 4. Feasibility & Timeline (6 Months) Months 1-2: Dataset ingestion and preprocessing of open-access human vascular data. Months 3-4: DSG construction and initial motif identification via Mechanistic Interpretability audits. Months 5-6: In silico validation of "Neural Mycelium" motifs and submission of a Registered Report to the ResearchHub Journal. 5. Budget & Open Science Commitment Funding Goal: $10,000 USD (Pilot Phase). Allocation: 70% Computational resources (GPU time for DSG training), 30% Open Access publication fees. Open Science: All code will be released under a MIT License on GitHub, and data models will be shared under CC-BY-4.0 to ensure full community replicability. Personal Staking Commitment I am an active ResearchHub ecosystem participant with a currently staked RSC balance, demonstrating my existing alignment with the platform's long-term growth. To further show my commitment to the "Neural Mycelium" project, I pledge to re-stake 80% of this bounty reward immediately upon receipt. 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