Synthetic microfluidic microvasculature networks
A synthetic microfluidic microvasculature network and associated methods mimic the structure, fluid flow characteristics, and physiological behavior of physiological microvasculature networks. Computational methods for simulating flow and particle adherence in synthetic and physiological microvascular systems and methods for determining parameters influencing particle adhesion and drug delivery are described with applications in the optimization of drug delivery and microvascular treatments and in describing disease mechanisms that affect the microvasculature.
1. A method for computationally simulating fluid flow through a microvascular network having one or more fluid inlets and one or more fluid outlets on a computer having a physical computer-readable medium having computer-executable instructions thereon that when executed by the computer implement the steps of the method, the method comprising:
a) creating a computational mesh representing the geometric features and connectivity of the microvascular network to generate a computational microvascular network, the computational microvascular network being selected from a physiological microvascular network, a synthetic microvascular network, and an averaged microvascular network;
b) specifying fluid flow/pressure conditions at the one or more inlets and one or more outlets; and
c) solving for flow velocities and pressures using one or more mathematical models.
2. The method of claim 1 , wherein the microvascular network is a physiological microvascular network.
3. The method of claim 1 , wherein the microvascular network is a synthetic microvascular network having a network of nonlinear, interconnected flow channels in fluid communication with a network inlet and a network outlet, the network of non-linear, interconnected flow channels having a geometric characteristic selected from the group consisting of a variable cross-sectional shape, a variable cross-sectional area, a turn, a bend, a bifurcation, a junction, a convolution, an anastomosis, and combinations thereof.
4. The method of claim 1 , wherein the microvascular network is an averaged microvascular network.
5. The method of claim 1 , further comprising the steps of simulating particle adhesion in the microvascular network by:
d) specifying one or more physical and biological particle properties; and
e) solving for particle transport using one or more mathematical models.
6. The method of claim 5 , wherein the particle comprises a drug.
7. The method of claim 5 , wherein data collected from particle adhesion experiments using synthetic microvascular networks is used to generate input for the one or more mathematical models.
8. The method of claim 7 , wherein the data collected from particle adhesion experiments using synthetic microvascular networks is used for deconvolution of the flow dynamics and the adhesion process in the microvascular network.
9. The method of claim 1 , comprising discretizing and solving the one or more mathematical models with a Finite Volume Method (FVM).
10. The method of claim 9 , comprising computing a pressure-based integration of Navier-Stokes equations on arbitrary mesh topologies.
11. The method of claim 1 , comprising calculating a converged solution for fluid flow by an iterative solution procedure.
12. The method of claim 1 , the computational mesh being a three dimensional hybrid mesh including hexahedral and prismatic elements.
13. The method of claim 1 , comprising:
obtaining a digitized microvascular network; and
building computational domains of the digitized microvascular network.
14. The method of claim 13 , wherein the digitized microvascular network including data for one or more of: inlet branches, outlet branches, flow rates, shear stress, diameter, distance between nodes, and vessel length.
15. The method of claim 1 , comprising calculating pressures and shear stresses within the computational microvascular network.
16. The method of claim 1 , comprising generating shear rate and flow rate maps of the computational microvascular network.
17. The method of claim 5 , comprising extracting predictive relationships between two or more of flow rate, particle size, receptor density, and network geometry.
18. The method of claim 5 , the one or more mathematical models used for solving for particle transport including one or more of:
shear-induced lift forces;
particle-wall lubrication effects;
nanoparticle and/or porous particle transport;
particle-particle interactions; and
abstraction of reduced order adhesion models.
19. The method of claim 5 , comprising computing particle collisions based on physical proximity of neighboring particles.
20. The method of claim 5 , comprising comparing fluid and particle flows at one or more junctions of the computational microvascular network.