scholarly journals Mathematical framework for activity-based cancer biomarkers

2015 ◽  
Vol 112 (41) ◽  
pp. 12627-12632 ◽  
Author(s):  
Gabriel A. Kwong ◽  
Jaideep S. Dudani ◽  
Emmanuel Carrodeguas ◽  
Eric V. Mazumdar ◽  
Seyedeh M. Zekavat ◽  
...  
Keyword(s):  
Allometric Scaling ◽  
Design Space ◽  
Cancer Biomarkers ◽  
Critical Parameters ◽  
Ultrasensitive Detection ◽  
Mathematical Framework ◽  
Model Formulation ◽  
Blood Biomarkers ◽  
Cross Platform ◽  
Nanoparticle Design

Advances in nanomedicine are providing sophisticated functions to precisely control the behavior of nanoscale drugs and diagnostics. Strategies that coopt protease activity as molecular triggers are increasingly important in nanoparticle design, yet the pharmacokinetics of these systems are challenging to understand without a quantitative framework to reveal nonintuitive associations. We describe a multicompartment mathematical model to predict strategies for ultrasensitive detection of cancer using synthetic biomarkers, a class of activity-based probes that amplify cancer-derived signals into urine as a noninvasive diagnostic. Using a model formulation made of a PEG core conjugated with protease-cleavable peptides, we explore a vast design space and identify guidelines for increasing sensitivity that depend on critical parameters such as enzyme kinetics, dosage, and probe stability. According to this model, synthetic biomarkers that circulate in stealth but then activate at sites of disease have the theoretical capacity to discriminate tumors as small as 5 mm in diameter—a threshold sensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve. This model may be adapted to describe the behavior of additional activity-based approaches to allow cross-platform comparisons, and to predict allometric scaling across species.

scholarly journals XShields: Cross-platform Application for the Design of Shields against Ionizing Radiation

2019 ◽  
Vol 60 (1) ◽  
pp. 75-84
Author(s):  
Wojciech Chlewicki ◽  
Katarzyna Cichoń ◽  
Magda Zolubak ◽  
Stepan Ozana ◽  
Aleksandra Kawala-Sterniuk
Keyword(s):  
Ionizing Radiation ◽  
Medical Diagnosis ◽  
Medical Staff ◽  
Critical Parameters ◽  
Proper Design ◽  
Cross Platform ◽  
Platform Development

Abstract In many cases medical diagnosis is based on information obtained through a process involving the emission of different forms of ionizing radiation. The safety of the medical staff and patients exposed to ionizing radiation is highly dependent on the proper design of the shielding used in the laboratory. Therefore, the authors propose a multi-platform application supporting such a design through the computation of the critical parameters of shielding. The specific requirements for shielding are defined by government authorities so the algorithm must comply with all the written standards. The application was implemented using Xamarin. Forms for cross-platform development. The results obtained with the use of the developed tool were compared with those calculated manually for the design of stationary shields developed, deployed, and validated by local inspection.

Design of Complex Nano-Scale Systems Using Multi-Agent Simulations and Structure-Behavior-Function Representations

2012 ◽  
Author(s):  
Paul F. Egan ◽  
Jonathan Cagan ◽  
Christian Schunn ◽  
Philip R. LeDuc
Keyword(s):  
Complex Systems ◽  
System Performance ◽  
Design Space ◽  
Critical Parameters ◽  
Virtual Design ◽  
Nano Scale ◽  
Agent Simulation ◽  
Multi Agent ◽  
Behavior Function ◽  
Domain Independent

Recent trends in technology are challenging engineers to configure products at ever smaller scales. At the nano-scale, biological protein machines are commonly chosen as a power-source for a broad-range of nano-devices. This paper explores the challenges in designing these and similar systems, such as improving the emergent system performance that arises from the interactions of many stochastic components. We develop a domain-independent methodology, using multi-agent simulations as a means of modeling and predicting emergent system behavior across scales and structure-behavior-function representations for understanding and navigating the resulting design space. This methodology is validated with an application of synthetic myosin motor design at the nanoscale, with simulation results aligning well with the macroscopic performance of myosin-powered muscular contractions. The multi-agent simulation is implemented with myosins modeled as agents, allowing for the virtual design and experimentation of synthetic myosins with altered structures and mechanochemical behaviors. Four myosin populations are designed and simulated, with their emergent system performance determined by aggregating the contributions of each myosin agent over time. Although the multi-agent simulation successfully recreates the emergent behaviors of the myosins, it is difficult to draw conclusions about how each structural variation influences aggregate performance. SBF representations of the system are then developed to describe how the aggregate performance of the system is explainable in terms of myosin behaviors, which map directly to altered myosin structures. It is then demonstrated how an engineer may utilize these representations and experimental results to reason about, and configure a myosin system with optimal performance. The methodology is domain-independent, ensuring its extendibility to similar complex systems while aiding a designer in simplifying a complex physical phenomenon to a design space consisting of only a few critical parameters. The methodology is particularly suited for complex systems with many parts operating stochastically across scales, and should prove invaluable for engineers facing the challenges of biological nanoscale design, where designs with unique properties require novel approaches or useful configurations in nature await discovery.

scholarly journals BioCCP.jl: Collecting Coupons in combinatorial biotechnology

2021 ◽  
Author(s):  
Kirsten Van Huffel ◽  
Michiel Stock ◽  
Bernard De Baets
Keyword(s):  
Sample Size ◽  
Design Space ◽  
Source Code ◽  
Sample Size Determination ◽  
Mathematical Framework ◽  
Minimum Sample Size ◽  
Size Determination ◽  
Screening Experiments ◽  
Coupon Collector ◽  
Minimum Sample

In combinatorial biotechnology, it is crucial for screening experiments to sufficiently cover the design space. In the BioCCP.jl package, we provide functions for minimum sample size determination based on the mathematical framework coined the Coupon Collector Problem. BioCCP.jl, including source code, documentation and a Pluto notebook is available at https://github.com/kirstvh/BioCCP.

Ultrasensitive Detection of Dual Cancer Biomarkers with Integrated CMOS-Compatible Nanowire Arrays

2015 ◽  
Vol 87 (22) ◽  
pp. 11203-11208 ◽  
Author(s):  
Na Lu ◽  
Anran Gao ◽  
Pengfei Dai ◽  
Hongju Mao ◽  
Xiaolei Zuo ◽  
...  
Keyword(s):  
Cancer Biomarkers ◽  
Nanowire Arrays ◽  
Ultrasensitive Detection ◽  
Cmos Compatible

Effects of Neighborhood Grant-Allocations on Trade-Offs among Urban Outcomes. Part 2: Model Formulation

1983 ◽  
Vol 15 (12) ◽  
pp. 1653-1667 ◽  
Author(s):  
A Nesher ◽  
A P Schinnar
Keyword(s):  
Community Development ◽  
Mathematical Framework ◽  
Model Formulation ◽  
Previous Issue ◽  
Trade Offs ◽  
The Us ◽  
Spatially Distributed ◽  
The Subject ◽  
Grant Allocations ◽  
Evaluation Project

A mathematical framework for evaluating substitutions and complementarities among urban outcomes is presented. A solution of a linear-programming problem, entailing the minimization of expenditure overruns above a spatially distributed community development grant, is used to assay the opportunity cost of grant allocations. This is the second essay on the subject developed under the auspices of the HUD Community Development Strategies Evaluation Project sponsored by the US Department of Housing and Urban Development. The first essay appeared in the previous issue of this journal.

A Prescription for Information Prospecting, Data Mining, and Design Refinement

1997 ◽  
Author(s):  
William H. Wood ◽  
Alice M. Agogino
Keyword(s):  
Conceptual Design ◽  
Design Process ◽  
Design Space ◽  
Mathematical Framework ◽  
Design Specification ◽  
Design Data ◽  
Design Decisions ◽  
Overall Design ◽  
Critical Design ◽  
Downstream Processes

Abstract We present a prescriptive methodology for conceptual design based on a process of information gathering and refinement. While these activities are generic to conceptual design, a mathematical framework is developed toward structuring the design space, approximating the design space by generalizing design data, and formalizing the iterative process of narrowing the design space while refining the level detail in the design specification. As a prescription for conceptual design, this method formalizes the conceptual design process around a key tradeoff — the value to be gained by making design commitments balanced against the reduction in size of design space these commitments bring. Because conceptual design decisions carry tremendous leverage through to all downstream processes, formalizing conceptual design toward reducing arbitrary design decisions and focusing attention on the most critical design concerns holds the potential to improve greatly the ultimate product of the overall design process.

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