System-oriented modeling and simulation of biofluidic lab-on-a chip

Design Automation Methods and Tools for Microfluidics-Based Biochips

Lin

Mukherjee

2006

This paper presents composable behavioral models and a schematic-based simulation methodology to enable topdown design of electrokinetic (EK) lab-on-a-chip (LoC). Complex EK LoCs are shown to be decomposable into a system of elements with simple geometry and specific function. Parameterized and analytical models are developed to describe the electric and biofluidic behavior within each element. Electric and biofluidic pins at element terminals support the communication between adjacent elements in a simulation schematic. An analog hardware description language implementation of the models is used to simulate LoC subsystems for micromixing and electrophoretic separation. Both direct current (dc) and transient analysis can be performed to capture the influence of system topology, element sizes, material properties, and operational parameters on LoC system performance. Accuracy (relative error generally less than 5%) and speedup (> 100×) of the schematic-based simulation methodology are demonstrated by comparison to experimental measurements and continuum numerical simulation. Index Terms-Behavioral model, dispersion, lab-on-a-chip, micromixing, schematic-based simulation, top-down design.NOMENCLATURE Variables A Maximum concentration of species band. c Concentration of species band. c m Channel width-averaged concentration of species band. c p pth moment of the concentration in a longitudinal filament of the species band. C e Electric conductivity of buffer, S/m. d n Mixing concentration coefficients. d m Mixing concentration coefficients at inlet of converging/diverging mixing intersection. D Molecular diffusivity of species, m 2 /s. E Electric field strength, V/cm. h Depth of microchannels, µm. I Electric current through buffer, A. L Length of microchannels, µm. L det Detector path length, µm.

Section: Future Workmentioning

confidence: 99%

Composable Behavioral Models and Schematic-Based Simulation of Electrokinetic Lab-on-a-Chip Systems

Design Automation Methods and Tools for Microfluidics-Based Biochips

Lin

Mukherjee

2006

Section: Future Workmentioning

confidence: 99%

“…In this paper, we will discuss the behavioral models and schematic-based simulations of micromixers and electrophoretic separators separately, attributed to their distinctly different biofluidic behavior. However, the top-down design methodology based on hierarchical decomposition that is valid to the entire integrated system [25] will be described jointly (Sections II-B and C).…”

Section: A Loc Introductionmentioning

confidence: 99%

Composable Behavioral Models and Schematic-Based Simulation of Electrokinetic Lab-on-a-Chip Systems

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Lin

Mukherjee

2006

Self Cite

This paper presents composable behavioral models and a schematic-based simulation methodology to enable topdown design of electrokinetic (EK) lab-on-a-chip (LoC). Complex EK LoCs are shown to be decomposable into a system of elements with simple geometry and specific function. Parameterized and analytical models are developed to describe the electric and biofluidic behavior within each element. Electric and biofluidic pins at element terminals support the communication between adjacent elements in a simulation schematic. An analog hardware description language implementation of the models is used to simulate LoC subsystems for micromixing and electrophoretic separation. Both direct current (dc) and transient analysis can be performed to capture the influence of system topology, element sizes, material properties, and operational parameters on LoC system performance. Accuracy (relative error generally less than 5%) and speedup (> 100×) of the schematic-based simulation methodology are demonstrated by comparison to experimental measurements and continuum numerical simulation. Index Terms-Behavioral model, dispersion, lab-on-a-chip, micromixing, schematic-based simulation, top-down design. NOMENCLATURE Variables A Maximum concentration of species band. c Concentration of species band. c m Channel width-averaged concentration of species band. c p pth moment of the concentration in a longitudinal filament of the species band. C e Electric conductivity of buffer, S/m. d n Mixing concentration coefficients. d m Mixing concentration coefficients at inlet of converging/diverging mixing intersection. D Molecular diffusivity of species, m 2 /s. E Electric field strength, V/cm. h Depth of microchannels, µm. I Electric current through buffer, A. L Length of microchannels, µm. L det Detector path length, µm.

“…More recently, Wang et al have developed a behavioral modeling and schematic simulation environment based on LoC system hierarchy in Verilog-A to investigate an integrated multi-functional (mixing, reaction, injection, and separation) competitive immunoassay microchip. The reaction model studied was limited to a transported-limited immunoassay that allowed a decoupled treatment of mixing and biochemical reactions (Wang et al 2005b). A common issue existing in prior investigations is the insufficient consideration of the coupling between the transport phenomena and biochemical reaction that is important for accurate system-level simulation of microfluidic assays.…”

Section: Introductionmentioning

confidence: 99%

System-level modeling and simulation of biochemical assays in lab-on-a-chip devices

Bedekar

Krishnamoorthy

et al. 2006

Microfluid Nanofluid

We present a ''mixed-methodology'' based system-level modeling and simulation for biochemical assays in lab-on-a-chip (LoC) devices. The methodology uses a combination of numerical schemes and analytical approaches to simulate biological and physicochemical processes, specifically, an integral approach for fluid flow and electric field, method of lines (MOL) and two-compartment models for biochemical reactions, and Fourier series-based model for analyte mixing. The solution procedure begins with decomposing the LoC device into a system of inter-connected components (e.g., channels and junctions) and the models are solved in a network fashion. Models are developed to accurately capture the multi-physics (e.g., flow, mixing, and reaction) behavior of individual components. The assembly of the components is facilitated via exchange of fluid flux and Fourier series coefficients (or average concentration) of analytes between various components, which enables network solution of the models. The system models are validated against both experimental and numerical models on various biochemical assays (e.g. immunoassays and enzymatic reactions), showing significant computational speedup (100-10,000-fold depending on the assay) without appreciably compromising accuracy (<10% error relative to numerical analysis).