Draft — Engineering Biological and Biomedical Systems (EBBS)

MOTIVATION LETTER

The Engineering Biological and Biomedical Systems programme at the U.S. National Science Foundation funds projects that apply quantitative engineering principles to unsolved biomedical problems. Addiction remains a condition without a preventative pharmacological strategy. The Conjunctive Consolidation Threshold (CCT) model addresses this gap directly. It is a tripartite framework that prevents reward-memory encoding by pharmacologically raising the conjunctive threshold required for memory consolidation during drug exposure. Three sole-authored preprints on OSF and Zenodo specify the model, its formal mathematics, and a Bayesian clinical trial architecture. ODE/RK45 and Bayesian MCMC validation demonstrate an 85.8% reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A provisional patent on the core architecture is scheduled for Q3 2026.

The EBBS programme is the correct home for this work because the CCT model is not a pharmacological hypothesis alone. It is an engineered biological system. The model specifies a control problem: maintain drug concentration within a therapeutic window that blocks consolidation without triggering toxicity or withdrawal. I have built two computational platforms that solve sub-problems within this system. IMPRINT screens addiction liability from molecular structure. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity prediction. GATE evaluates safety parameters for BCI neural-stimulation interfaces. These platforms are released under Apache 2.0 and are available for community use.

Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the theoretical grounding and computational rigor of the approach. Gershman provided an arXiv endorsement. A co-authored paper on alcohol pharmacology is under review at Alcohol (Elsevier). A review article on the CCT framework is under review at Neuroscience and Biobehavioral Reviews.

I am an independent researcher based in Lagos, Nigeria. This position provides a unique broader impact for EBBS. Addiction treatment infrastructure in sub-Saharan Africa is minimal. Pharmacological interventions are imported, expensive, and rarely tailored to local genetic or metabolic profiles. The CCT model, if validated, could be deployed as a low-cost, orally administered prophylactic regimen. My platforms are designed for open-source distribution and low-resource deployment. The EBBS programme would fund the next phase: experimental validation in rodent models, refinement of the Bayesian trial design, and construction of a closed-loop drug delivery prototype. The provisional patent protects the core architecture while the open-source platforms ensure rapid dissemination.

RESEARCH STATEMENT

The CCT model proposes that reward-memory encoding in addiction requires the simultaneous activation of three neural systems: dopaminergic reward signaling, glutamatergic plasticity at the synapse, and noradrenergic arousal. If any one of these systems is suppressed below a threshold during the consolidation window, the memory trace does not form. The model is mathematically specified as a system of coupled ordinary differential equations representing drug concentration, dopamine receptor occupancy, NMDA receptor activation, and norepinephrine release. The threshold function is a sigmoidal gate that outputs encoding probability as a function of the product of the three activation levels. Validation used RK45 integration for deterministic dynamics and PyMC for Bayesian MCMC sampling of parameter posteriors from simulated clinical data. The 85.8% reduction in encoding probability is the primary endpoint. The 12.8 percentage point super-additivity indicates that the triple combination outperforms any pairwise combination by a statistically significant margin.

The next step is experimental. I have designed a Bayesian adaptive clinical trial architecture that minimizes sample size while maximizing information gain about the dose-response surface. The trial design is specified in the third preprint on Zenodo. The EBBS programme would support the construction of a closed-loop drug delivery system that maintains each drug concentration within its therapeutic window using real-time pharmacokinetic modeling. This is an engineering problem: sensor selection, control algorithm design, and safety validation. My platform GATE provides the safety evaluation framework for the neural-stimulation component if the system is extended to include closed-loop neuromodulation as a fourth intervention arm.

The broader impact of this research extends beyond addiction. The CCT framework is generalizable to any disorder involving maladaptive memory consolidation, including post-traumatic stress disorder, phobias, and compulsive behaviors. The computational platforms I have built are modular. TOPOLOGIX can be applied to any drug-protein interaction network. IMPRINT can screen any compound library for addiction liability. GATE can evaluate any neural interface for safety. The EBBS programme would fund the integration of these platforms into a single pipeline for rational design of consolidation-blocking therapies.

The LMIC perspective is central. Nigeria has no dedicated addiction research institute. Clinical trials for psychiatric medications are rare. The CCT model, if validated, could be manufactured locally using generic active pharmaceutical ingredients. The Bayesian trial design is adaptive and requires fewer participants than traditional designs, making it feasible in settings with limited patient recruitment infrastructure. I have built the computational infrastructure on open-source tools and HPC clusters accessible via Nextflow and SLURM. The work is reproducible, transparent, and designed for global collaboration.

PROJECT DESCRIPTION

The proposed project is titled: Engineering a Closed-Loop Pharmacological System for Reward-Memory Encoding Prevention. The objective is to design, simulate, and validate a control system that maintains three drug concentrations within their respective therapeutic windows during the consolidation window following drug exposure. The system will use a pharmacokinetic-pharmacodynamic model coupled to a real-time sensor for a surrogate biomarker of consolidation (e.g., salivary cortisol for noradrenergic tone or pupil diameter for arousal). The control algorithm will be a model predictive controller that adjusts infusion rates based on the current state estimate from a Kalman filter.

The project has three aims. Aim 1: Extend the existing ODE model to include a pharmacokinetic compartment for each of the three drugs, with inter-individual variability parameters estimated from published clinical data. Aim 2: Design and simulate the model predictive controller in silico, testing robustness to parameter uncertainty, sensor noise, and actuator delay. Aim 3: Build a benchtop prototype using a programmable syringe pump, a commercial cortisol biosensor, and a Raspberry Pi running the control algorithm in Python. The prototype will be tested with saline and a surrogate drug cocktail in a flow-through system to verify that the controller maintains setpoint concentrations within 10% of target.

The timeline is 24 months. Months 1-6: literature review, parameter estimation, model extension. Months 7-12: control algorithm design and in silico validation. Months 13-18: hardware procurement, assembly, and benchtop testing. Months 19-24: data analysis, manuscript preparation, and patent filing for the control system.

The budget request is for equipment (syringe pump, biosensor, Raspberry Pi, flow cell, tubing, reagents) at USD 8,500, computational resources (cloud HPC credits) at USD 3,000, and stipend support for the applicant at USD 28,500 for 12 months, totaling USD 40,000. No indirect costs are requested as the applicant is independent.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun. B.Pharm, University of Ibadan, 2021. CGPA 5.1/7.0, German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria. Current position: National Product Manager, Synthcare, Lagos (March 2026-present). Previous positions: Clinical Pharmacist, Ramset Pharmacy (January-March 2026); Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan (NMDA/insulin docking studies); Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Alliance (GHRU-GSAR), AMR genomics and surveillance pipeline.

Independent research output: Three sole-authored preprints on the Conjunctive Consolidation Threshold model. Foundational paper on OSF (10.17605/OSF.IO/KG7B5). Formal mathematical specification on OSF (10.17605/OSF.IO/EMY4U). Bayesian population dynamics and clinical trial architecture on Zenodo (10.5281/zenodo.20492472). One co-authored paper under review at Alcohol (Elsevier). One review article under review at Neuroscience and Biobehavioral Reviews. Provisional patent on CCT core architecture, Q3 2026.

Computational platforms built: IMPRINT (addiction-liability screening), TOPOLOGIX (topological data analysis for drug-protein interaction, persistent homology, bipartite simplicial complexes, hERG cardiotoxicity MVP), GATE (BCI neural-stimulation safety evaluation, Apache 2.0 license). All platforms are open-source and available on GitHub.

Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js.

Endorsements: Kent Berridge (University of Michigan), Samuel Gershman (Harvard University, provided arXiv endorsement), Nathaniel Daw (Princeton University), Marcelo Mattar (New York University).

BUDGET JUSTIFICATION

Equipment: Programmable syringe pump (Harvard Apparatus PHD Ultra, USD 3,200), cortisol biosensor kit (Salimetrics, USD 1,800), Raspberry Pi 5 with case and power supply (USD 200), flow cell and tubing (USD 800), reagents and saline (USD 500), miscellaneous hardware (USD 1,000). Total equipment: USD 8,500.

Computational resources: Cloud HPC credits on AWS or Google Cloud for Bayesian MCMC sampling and control algorithm simulations. Estimated 10,000 core-hours at USD 0.30 per core-hour. Total: USD 3,000.

Stipend: 12 months at USD 2,375 per month to support full-time work on the project. Total: USD 28,500.

Total direct costs: USD 40,000. No indirect costs requested.

CHECKLIST

- [ ] Complete NSF EBBS grant application form on Grants.gov
- [ ] Upload project description (15 pages maximum, single-spaced, 12-point font)
- [ ] Upload biographical sketch (2 pages maximum, NSF format)
- [ ] Upload budget and budget justification (1 page each)
- [ ] Upload current and pending support statement
- [ ] Upload facilities, equipment, and other resources statement
- [ ] Upload data management plan (2 pages maximum)
- [ ] Upload postdoctoral mentoring plan (not applicable, applicant is independent)
- [ ] Verify eligibility: independent researcher without institutional affiliation may need to identify a sponsoring organization or request a waiver
- [ ] Confirm deadline on Grants.gov and NSF website
- [ ] Obtain letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar
- [ ] Verify that provisional patent filing does not conflict with NSF intellectual property terms
- [ ] Prepare ORCID iD and GitHub profile for inclusion in biographical sketch

EDITOR NOTES

- Eligibility risk: NSF grants typically require an institutional sponsor (university or non-profit). The applicant is independent. Check if a U.S.-based institution can serve as the awardee organization, or if the applicant can apply as an individual under a limited circumstances clause. Contact the NSF EBBS programme officer before submission.
- Verification needed: Confirm that the provisional patent filing (Q3 2026) has been submitted and that the filing date is before the grant submission deadline. NSF requires disclosure of all pending patents.
- Gap: The applicant's employment as National Product Manager at Synthcare (March 2026-present) may conflict with full-time research on this project. Clarify whether the position is part-time or if a leave of absence is possible. The budget requests 12 months of stipend, implying full-time commitment.
- Gap: No mention of a sponsoring organization for the grant. The applicant should identify a U.S.-based university or non-profit willing to host the grant and provide facilities. The University of Michigan (Berridge) or Harvard (Gershman) are potential hosts.
- Verification needed: Confirm that the review article at Neuroscience and Biobehavioral Reviews and the co-authored paper at Alcohol are still under review and have not been rejected. Update status in the biographical sketch before submission.

MOTIVATION LETTER

The Engineering Biological and Biomedical Systems programme at the U.S. National Science Foundation funds projects that apply quantitative engineering principles to unsolved biomedical problems. Addiction remains a condition without a preventative pharmacological strategy. The Conjunctive Consolidation Threshold (CCT) model addresses this gap directly. It is a tripartite framework that prevents reward-memory encoding by pharmacologically raising the conjunctive threshold required for memory consolidation during drug exposure. Three sole-authored preprints on OSF and Zenodo specify the model, its formal mathematics, and a Bayesian clinical trial architecture. ODE/RK45 and Bayesian MCMC validation demonstrate an 85.8% reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A provisional patent on the core architecture is scheduled for Q3 2026.

The EBBS programme is the correct home for this work because the CCT model is not a pharmacological hypothesis alone. It is an engineered biological system. The model specifies a control problem: maintain drug concentration within a therapeutic window that blocks consolidation without triggering toxicity or withdrawal. I have built two computational platforms that solve sub-problems within this system. IMPRINT screens addiction liability from molecular structure. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity prediction. GATE evaluates safety parameters for BCI neural-stimulation interfaces. These platforms are released under Apache 2.0 and are available for community use.

Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the theoretical grounding and computational rigor of the approach. Gershman provided an arXiv endorsement. A co-authored paper on alcohol pharmacology is under review at Alcohol (Elsevier). A review article on the CCT framework is under review at Neuroscience and Biobehavioral Reviews.

I am an independent researcher based in Lagos, Nigeria. This position provides a unique broader impact for EBBS. Addiction treatment infrastructure in sub-Saharan Africa is minimal. Pharmacological interventions are imported, expensive, and rarely tailored to local genetic or metabolic profiles. The CCT model, if validated, could be deployed as a low-cost, orally administered prophylactic regimen. My platforms are designed for open-source distribution and low-resource deployment. The EBBS programme would fund the next phase: experimental validation in rodent models, refinement of the Bayesian trial design, and construction of a closed-loop drug delivery prototype. The provisional patent protects the core architecture while the open-source platforms ensure rapid dissemination.

RESEARCH STATEMENT

The CCT model proposes that reward-memory encoding in addiction requires the simultaneous activation of three neural systems: dopaminergic reward signaling, glutamatergic plasticity at the synapse, and noradrenergic arousal. If any one of these systems is suppressed below a threshold during the consolidation window, the memory trace does not form. The model is mathematically specified as a system of coupled ordinary differential equations representing drug concentration, dopamine receptor occupancy, NMDA receptor activation, and norepinephrine release. The threshold function is a sigmoidal gate that outputs encoding probability as a function of the product of the three activation levels. Validation used RK45 integration for deterministic dynamics and PyMC for Bayesian MCMC sampling of parameter posteriors from simulated clinical data. The 85.8% reduction in encoding probability is the primary endpoint. The 12.8 percentage point super-additivity indicates that the triple combination outperforms any pairwise combination by a statistically significant margin.

The next step is experimental. I have designed a Bayesian adaptive clinical trial architecture that minimizes sample size while maximizing information gain about the dose-response surface. The trial design is specified in the third preprint on Zenodo. The EBBS programme would support the construction of a closed-loop drug delivery system that maintains each drug concentration within its therapeutic window using real-time pharmacokinetic modeling. This is an engineering problem: sensor selection, control algorithm design, and safety validation. My platform GATE provides the safety evaluation framework for the neural-stimulation component if the system is extended to include closed-loop neuromodulation as a fourth intervention arm.

The broader impact of this research extends beyond addiction. The CCT framework is generalizable to any disorder involving maladaptive memory consolidation, including post-traumatic stress disorder, phobias, and compulsive behaviors. The computational platforms I have built are modular. TOPOLOGIX can be applied to any drug-protein interaction network. IMPRINT can screen any compound library for addiction liability. GATE can evaluate any neural interface for safety. The EBBS programme would fund the integration of these platforms into a single pipeline for rational design of consolidation-blocking therapies.

The LMIC perspective is central. Nigeria has no dedicated addiction research institute. Clinical trials for psychiatric medications are rare. The CCT model, if validated, could be manufactured locally using generic active pharmaceutical ingredients. The Bayesian trial design is adaptive and requires fewer participants than traditional designs, making it feasible in settings with limited patient recruitment infrastructure. I have built the computational infrastructure on open-source tools and HPC clusters accessible via Nextflow and SLURM. The work is reproducible, transparent, and designed for global collaboration.

PROJECT DESCRIPTION

The proposed project is titled: Engineering a Closed-Loop Pharmacological System for Reward-Memory Encoding Prevention. The objective is to design, simulate, and validate a control system that maintains three drug concentrations within their respective therapeutic windows during the consolidation window following drug exposure. The system will use a pharmacokinetic-pharmacodynamic model coupled to a real-time sensor for a surrogate biomarker of consolidation (e.g., salivary cortisol for noradrenergic tone or pupil diameter for arousal). The control algorithm will be a model predictive controller that adjusts infusion rates based on the current state estimate from a Kalman filter.

The project has three aims. Aim 1: Extend the existing ODE model to include a pharmacokinetic compartment for each of the three drugs, with inter-individual variability parameters estimated from published clinical data. Aim 2: Design and simulate the model predictive controller in silico, testing robustness to parameter uncertainty, sensor noise, and actuator delay. Aim 3: Build a benchtop prototype using a programmable syringe pump, a commercial cortisol biosensor, and a Raspberry Pi running the control algorithm in Python. The prototype will be tested with saline and a surrogate drug cocktail in a flow-through system to verify that the controller maintains setpoint concentrations within 10% of target.

The timeline is 24 months. Months 1-6: literature review, parameter estimation, model extension. Months 7-12: control algorithm design and in silico validation. Months 13-18: hardware procurement, assembly, and benchtop testing. Months 19-24: data analysis, manuscript preparation, and patent filing for the control system.

The budget request is for equipment (syringe pump, biosensor, Raspberry Pi, flow cell, tubing, reagents) at USD 8,500, computational resources (cloud HPC credits) at USD 3,000, and stipend support for the applicant at USD 28,500 for 12 months, totaling USD 40,000. No indirect costs are requested as the applicant is independent.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun. B.Pharm, University of Ibadan, 2021. CGPA 5.1/7.0, German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria. Current position: National Product Manager, Synthcare, Lagos (March 2026-present). Previous positions: Clinical Pharmacist, Ramset Pharmacy (January-March 2026); Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan (NMDA/insulin docking studies); Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Alliance (GHRU-GSAR), AMR genomics and surveillance pipeline.

Independent research output: Three sole-authored preprints on the Conjunctive Consolidation Threshold model. Foundational paper on OSF (10.17605/OSF.IO/KG7B5). Formal mathematical specification on OSF (10.17605/OSF.IO/EMY4U). Bayesian population dynamics and clinical trial architecture on Zenodo (10.5281/zenodo.20492472). One co-authored paper under review at Alcohol (Elsevier). One review article under review at Neuroscience and Biobehavioral Reviews. Provisional patent on CCT core architecture, Q3 2026.

Computational platforms built: IMPRINT (addiction-liability screening), TOPOLOGIX (topological data analysis for drug-protein interaction, persistent homology, bipartite simplicial complexes, hERG cardiotoxicity MVP), GATE (BCI neural-stimulation safety evaluation, Apache 2.0 license). All platforms are open-source and available on GitHub.

Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js.

Endorsements: Kent Berridge (University of Michigan), Samuel Gershman (Harvard University, provided arXiv endorsement), Nathaniel Daw (Princeton University), Marcelo Mattar (New York University).

BUDGET JUSTIFICATION

Equipment: Programmable syringe pump (Harvard Apparatus PHD Ultra, USD 3,200), cortisol biosensor kit (Salimetrics, USD 1,800), Raspberry Pi 5 with case and power supply (USD 200), flow cell and tubing (USD 800), reagents and saline (USD 500), miscellaneous hardware (USD 1,000). Total equipment: USD 8,500.

Computational resources: Cloud HPC credits on AWS or Google Cloud for Bayesian MCMC sampling and control algorithm simulations. Estimated 10,000 core-hours at USD 0.30 per core-hour. Total: USD 3,000.

Stipend: 12 months at USD 2,375 per month to support full-time work on the project. Total: USD 28,500.

Total direct costs: USD 40,000. No indirect costs requested.

CHECKLIST

- [ ] Complete NSF EBBS grant application form on Grants.gov
- [ ] Upload project description (15 pages maximum, single-spaced, 12-point font)
- [ ] Upload biographical sketch (2 pages maximum, NSF format)
- [ ] Upload budget and budget justification (1 page each)
- [ ] Upload current and pending support statement
- [ ] Upload facilities, equipment, and other resources statement
- [ ] Upload data management plan (2 pages maximum)
- [ ] Upload postdoctoral mentoring plan (not applicable, applicant is independent)
- [ ] Verify eligibility: independent researcher without institutional affiliation may need to identify a sponsoring organization or request a waiver
- [ ] Confirm deadline on Grants.gov and NSF website
- [ ] Obtain letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar
- [ ] Verify that provisional patent filing does not conflict with NSF intellectual property terms
- [ ] Prepare ORCID iD and GitHub profile for inclusion in biographical sketch

EDITOR NOTES

- Eligibility risk: NSF grants typically require an institutional sponsor (university or non-profit). The applicant is independent. Check if a U.S.-based institution can serve as the awardee organization, or if the applicant can apply as an individual under a limited circumstances clause. Contact the NSF EBBS programme officer before submission.
- Verification needed: Confirm that the provisional patent filing (Q3 2026) has been submitted and that the filing date is before the grant submission deadline. NSF requires disclosure of all pending patents.
- Gap: The applicant's employment as National Product Manager at Synthcare (March 2026-present) may conflict with full-time research on this project. Clarify whether the position is part-time or if a leave of absence is possible. The budget requests 12 months of stipend, implying full-time commitment.
- Gap: No mention of a sponsoring organization for the grant. The applicant should identify a U.S.-based university or non-profit willing to host the grant and provide facilities. The University of Michigan (Berridge) or Harvard (Gershman) are potential hosts.
- Verification needed: Confirm that the review article at Neuroscience and Biobehavioral Reviews and the co-authored paper at Alcohol are still under review and have not been rejected. Update status in the biographical sketch before submission.

AI Draft — Engineering Biological and Biomedical Systems (EBBS)