System Parameter Optimization
System Parameter Optimization - Related Videos
07:57
Electrospinning Fundamentals: Optimizing Solution and Apparatus Parameters
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Cited by 93 •
2011
Electrospinning techniques can create a variety of nanofibrous scaffolds for tissue engineering or other applications. We describe here a procedure to optimize the parameters of the electrospinning solution and apparatus to obtain fibers with the desired morphology and alignment. Common problems and troubleshooting techniques are also presented.
08:23
Optimization of Performance Parameters of the TAGGG Telomere Length Assay
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Cited by 3 •
2023
Here, we describe in detail the protocol for quantifying telomere length using non-radioactive chemiluminescent detection, with a focus on the optimization of various performance parameters of the TAGGG telomere length assay kit, such as buffer quantities and probe concentrations.
10:16
Optimization of Flow Cytometric Sorting Parameters for High-Throughput Isolation and Purification of Small Extracellular Vesicles
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Cited by 2 •
2023
This protocol provides a rapid and size-specific isolation method for small extracellular vesicles by optimizing the size of the air spray nozzle, sheath fluid pressure, sample flow pressure, voltage, gain, and triggering threshold parameters.
01:10
Wave Parameters
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2023
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
11:19
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
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Cited by 4 •
2017
We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.
FAQs
An objective function translates the desired performance outcome into a criterion that alternatives can be compared against. For example, optimization may prioritize yield, stability, cost, or safety, depending on the project goal. This framing helps researchers assess how changing temperature, pH, flow rate, material properties, or genetic expression affects performance rather than judging each parameter in isolation.
Constraints define conditions that acceptable solutions must satisfy, even when another setting could improve a single performance measure. They help manage trade-offs among outcomes such as yield, stability, cost, and safety. In practice, a parameter combination is valuable only when its predicted or measured improvement remains compatible with the system requirements and other specified limits.
Mathematical models provide a structured way to compare parameter settings and anticipate system responses, while experiments test how the biological or engineered system actually behaves. Experimental design helps select informative conditions for evaluation, and the resulting observations support iterative refinement. Combining both approaches can make comparisons more systematic than relying on isolated trial-and-error adjustments.
A typical workflow identifies controllable variables, selects a performance objective, and specifies relevant constraints. Researchers then choose parameter settings, evaluate the resulting system response through a model or experiment, compare the outcome with alternatives, and adjust the settings iteratively. The process continues until the selected combination provides an acceptable balance of performance and restrictions.
The approach supports several bioengineering activities, including bioprocess development, biomaterial design, therapeutic production, and refinement of biomedical devices. In each setting, researchers can examine how controllable conditions or design properties influence performance. Optimization is especially useful when improving one outcome may affect another, such as balancing production yield with stability, cost, or safety.
Optimized settings can reveal which combinations best support a defined performance objective while meeting the project’s constraints. Depending on the application, evaluation may focus on yield, stability, cost, safety, or related system responses. These comparisons help researchers identify trade-offs and select conditions or designs that are more suitable for biological systems, engineered materials, or biomedical devices.