MOTIVATION LETTER The Transport Phenomena programme at the National Science Foundation supports fundamental research on the movement of mass, momentum, and energy across physical and biological systems. My independent research on the Conjunctive Consolidation Threshold model addresses a specific transport problem: the transport of pharmacological agents across neural reward circuits and the subsequent transport of molecular signals that encode drug-associated memories. The CCT model, which I have specified mathematically using ODE/RK45 solvers and validated with Bayesian MCMC methods, predicts that three drug classes administered at precise temporal intervals can reduce reward-memory encoding probability from 0.855 to 0.122, an 85.8 percent reduction. This is a transport phenomenon at the synaptic and circuit level. I am a 29-year-old Nigerian independent researcher with a B.Pharm from the University of Ibadan, CGPA 5.1/7.0, German equivalent 1.9. I hold a provisional patent on the CCT core architecture filed Q3 2026. My three sole-authored preprints on OSF and Zenodo document the foundational framework, the formal mathematical specification, and the Bayesian population dynamics with clinical trial architecture. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier). My endorsements include Kent Berridge at Michigan, Samuel Gershman at Harvard who endorsed my arXiv submission, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. The Transport Phenomena programme funds research on convective and diffusive transport, multiphase flows, and transport in biological systems. My work directly engages the transport of drug molecules across the blood-brain barrier, the diffusion of neurotransmitters in the synaptic cleft, and the convective transport of neural signals through reward circuitry. The CCT model specifies the temporal transport dynamics of three pharmacological agents: a dopamine D1 antagonist, an NMDA receptor antagonist, and a beta-adrenergic blocker. The super-additivity effect of 12.8 percentage points above individual drug effects is a direct consequence of the transport kinetics of these agents across the reward-memory consolidation window. I seek funding to develop a computational transport model that simulates the spatiotemporal distribution of these three agents in the human brain using finite element methods and to validate the model against published pharmacokinetic data. This work would extend the CCT framework from a lumped-parameter ODE model to a spatially resolved partial differential equation model of drug transport in brain tissue. The results would inform clinical trial design for CCT-based addiction therapy. My platforms IMPRINT for addiction-liability screening and TOPOLOGIX for topological data analysis of drug-protein interactions demonstrate my capacity to build computational tools for drug transport problems. My technical stack includes Python with scipy and numpy for ODE solving, PyMC for Bayesian inference, and NEURON and Brian2 for neural simulation. I have experience with HPC workflows using Nextflow and SLURM. The Transport Phenomena programme would enable me to transition from an independent researcher in Lagos to a computational transport scientist with a formal research programme. I am applying to MSc programmes at Medical University of Graz and University of Graz for October 2026 start. This grant would support the computational infrastructure and collaborative travel needed to establish the transport-physics foundation of the CCT model. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model proposes that drug-associated reward memories are encoded through a temporally constrained window of neurochemical transport and binding. Three pharmacological agents administered in sequence disrupt this window by altering the transport and binding kinetics of dopamine, glutamate, and norepinephrine at their respective receptors. My research aims to formalize this as a transport phenomenon governed by the convection-diffusion-reaction equation in brain extracellular space. The problem is fundamentally a transport problem. Drug molecules must traverse the blood-brain barrier, diffuse through the extracellular matrix, bind to receptors, and trigger intracellular signaling cascades that modify synaptic weights. The temporal coordination of these transport processes determines whether a reward memory is consolidated. My ODE model treats the brain as a well-mixed compartment with first-order transport kinetics. The next step is to develop a spatially resolved model using partial differential equations that account for heterogeneous tissue properties, regional blood flow, and receptor density distributions. My specific aims are threefold. First, to develop a finite element model of drug transport in a realistic brain geometry derived from the MNI152 atlas, incorporating regional variations in blood-brain barrier permeability, extracellular volume fraction, and receptor density. Second, to validate the model against published microdialysis data for dopamine D1 antagonists, NMDA antagonists, and beta-blockers in rodent and human studies. Third, to use the validated model to optimize the dosing interval and sequence for the CCT protocol, minimizing off-target effects while maximizing reward-memory disruption. The methodology draws on established transport physics. I will implement the convection-diffusion-reaction equation using the finite element method in FEniCS or deal.II, with boundary conditions representing the blood-brain barrier as a semi-permeable membrane. The reaction term will incorporate Michaelis-Menten kinetics for receptor binding and internalization. Parameter estimation will use Bayesian inference with PyMC, drawing on prior distributions from published pharmacokinetic studies. The model will be validated against the ODE model predictions using the Kolmogorov-Smirnov test for distributional similarity. The expected outcomes include a validated spatiotemporal transport model of the CCT drug cocktail in human brain tissue, a set of optimized dosing protocols that reduce the required drug concentrations by at least 30 percent while maintaining the 85.8 percent encoding reduction, and a publication in a transport-physics or computational neuroscience journal. The model will be released as open-source code on GitHub under the Apache 2.0 license, consistent with my existing open-source contributions including the GATE platform for BCI neural-stimulation safety evaluation. This research is relevant to the Transport Phenomena programme because it applies fundamental transport physics to a biomedical problem with high societal impact. Addiction affects 35 million people globally according to the World Health Organization, and current pharmacological treatments have limited efficacy. The CCT model offers a mechanistically grounded alternative that targets the transport dynamics of memory consolidation rather than receptor occupancy alone. The transport model will provide the quantitative foundation for clinical translation. PROJECT DESCRIPTION Project Title: Spatiotemporal Drug Transport Modeling for the Conjunctive Consolidation Threshold Protocol in Addiction Therapy Objective: To develop and validate a finite element model of the transport of three pharmacological agents in human brain tissue, optimizing the Conjunctive Consolidation Threshold protocol for clinical translation. Background: The CCT model specifies that three drug classes administered within a 60-minute window disrupt the encoding of drug-associated reward memories. The current ODE model assumes well-mixed compartments and first-order kinetics. A spatially resolved transport model is needed to account for heterogeneous brain tissue properties, regional blood flow variations, and the diffusion gradients that determine receptor binding kinetics. Methods: I will construct a three-dimensional brain geometry from the MNI152 atlas using the Computational Anatomy Toolbox for SPM. The mesh will be generated using Gmsh with adaptive refinement in regions of interest including the nucleus accumbens, ventral tegmental area, and prefrontal cortex. The convection-diffusion-reaction equation will be solved using the finite element method in FEniCS, with boundary conditions representing the blood-brain barrier as a Robin-type condition with spatially varying permeability. The reaction term will include saturable binding to D1, NMDA, and beta-adrenergic receptors with dissociation constants from the literature. Parameter estimation will use Bayesian inference with PyMC. Prior distributions will be derived from published pharmacokinetic studies of each drug class in humans. The likelihood function will compare model predictions to microdialysis data from rodent studies, scaled to human brain volumes using allometric scaling. Posterior distributions will be sampled using the No-U-Turn Sampler with four chains of 5000 samples each. Validation will proceed in three stages. First, the model will reproduce the ODE model predictions in the limit of uniform tissue properties. Second, the model will match published concentration-time profiles for each drug individually. Third, the model will predict the super-additivity effect of 12.8 percentage points observed in the ODE model, demonstrating that the spatial heterogeneity enhances the temporal coordination of the three drugs. Timeline: Months 1-3: Brain geometry construction and mesh generation. Months 4-6: Finite element solver implementation and parameter estimation. Months 7-9: Validation against published data and sensitivity analysis. Months 10-12: Optimization of dosing protocol and manuscript preparation. Budget: Computational infrastructure including GPU-accelerated workstation and cloud computing credits for HPC simulations, $15,000. Travel to collaborating laboratories at University of Michigan and Harvard for experimental validation discussions, $8,000. Open-access publication fees, $3,000. Software licenses and data access fees, $2,000. Total direct costs, $28,000. Indirect costs at 10 percent, $2,800. Total requested, $30,800. Deliverables: Open-source finite element code repository on GitHub. Validated transport model with parameter posterior distributions. Optimized dosing protocol for clinical trial design. One manuscript submitted to a peer-reviewed journal. One conference presentation at the Society for Neuroscience or the American Institute of Chemical Engineers. CHECKLIST - [ ] Complete NSF FastLane or Research.gov registration as an individual investigator - [ ] Verify eligibility for Transport Phenomena programme as an independent researcher without academic appointment - [ ] Prepare Project Description (15-page limit, single-spaced, 12-point font) - [ ] Prepare Biographical Sketch using NSF format (2-page limit) - [ ] Prepare Budget and Budget Justification for $30,800 total - [ ] Prepare Current and Pending Support document listing all current funding sources - [ ] Prepare Facilities, Equipment, and Other Resources statement describing access to computational resources in Lagos - [ ] Prepare Data Management Plan following NSF guidelines - [ ] Obtain letters of collaboration from Kent Berridge (Michigan) and Samuel Gershman (Harvard) - [ ] Submit through Research.gov before programme deadline - [ ] Confirm that the programme accepts proposals from non-US institutions and independent researchers - [ ] Verify that the research qualifies as transport phenomena rather than neuroscience or pharmacology EDITOR NOTES - Eligibility risk: The Transport Phenomena programme typically funds US academic institutions. Eniola is an independent researcher based in Nigeria. Verify whether NSF allows proposals from foreign individuals without US institutional affiliation. If not, consider collaborating with a US-based co-PI at Michigan or Harvard. - Fact verification: The MNI152 atlas and Computational Anatomy Toolbox are real. Confirm that FEniCS supports the convection-diffusion-reaction equation with Robin boundary conditions. The microdialysis data for the three drug classes must be identified and cited in the proposal. - Gap: The budget does not include salary for Eniola. The programme may require salary support for the PI. Add a line for PI salary at a reasonable rate for independent researchers, or justify in-kind contribution from current employment at Synthcare. - Gap: The proposal does not address how the transport model will be validated against human data. Add a section on planned collaboration with clinical partners for future PET or MRI studies of drug distribution. - Risk: The programme may consider this proposal too biomedical and insufficiently focused on fundamental transport physics. Strengthen the connection to transport phenomena by emphasizing the convection-diffusion-reaction framework and the development of new numerical methods for heterogeneous brain tissue.