An engineering design approach to systems biology

An engineering approach to systems biology applies educational philosophy, engineering design, and predictive models to solve contemporary problems in biomedicine.

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Toward a next generation of predictive models: A systems biology primer

Food Control ◽

10.1016/j.foodcont.2012.06.019 ◽

2013 ◽

Vol 29 (2) ◽

pp. 336-342 ◽

Cited By ~ 13

Author(s):

J.F. Van Impe ◽

D. Vercammen ◽

E. Van Derlinden

Keyword(s):

Systems Biology ◽

Predictive Models ◽

Next Generation

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Computational Systems Biology Perspective on Tuberculosis in Big Data Era

Web Services ◽

10.4018/978-1-5225-7501-6.ch115 ◽

2019 ◽

pp. 2230-2254

Author(s):

Amandeep Kaur Kahlon ◽

Ashok Sharma

Keyword(s):

Big Data ◽

Systems Biology ◽

Data Management ◽

High Throughput ◽

Predictive Models ◽

System Biology ◽

Prediction Models ◽

Computational Systems Biology ◽

Future Directions ◽

Computational Systems

The major concern in this chapter is to understand the need of system biology in prediction models in studying tuberculosis infection in the big data era. The overall complexity of biological phenomenon, such as biochemical, biophysical, and other molecular processes, within pathogen as well as their interaction with host is studied through system biology approaches. First, consideration is given to the necessity of prediction models integrating system biology approaches and later on for their replacement and refinement using high throughput data. Various ongoing projects, consortium, databases, and research groups involved in tuberculosis eradication are also discussed. This chapter provides a brief account of TB predictive models and their importance in system biology to study tuberculosis and host-pathogen interactions. This chapter also addresses big data resources and applications, data management, limitations, challenges, solutions, and future directions.

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Robust Approaches to Generating Reliable Predictive Models in Systems Biology

RNA Technologies - Systems Biology ◽

10.1007/978-3-319-92967-5_15 ◽

2018 ◽

pp. 301-312 ◽

Cited By ~ 1

Author(s):

Kiri Choi

Keyword(s):

Systems Biology ◽

Predictive Models

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The Influence of Physical Examples on Originality and Fixation in Engineering Design

Volume 7: 30th International Conference on Design Theory and Methodology ◽

10.1115/detc2018-85396 ◽

2018 ◽

Cited By ~ 1

Author(s):

Christopher C. Simmons ◽

Trina C. Kershaw ◽

Alexander LeGendre ◽

Sankha Bhowmick

Keyword(s):

Engineering Design ◽

Significant Relationship ◽

Mechanical Engineering ◽

Engineering Students ◽

Garbage Collection ◽

Negative Relationship ◽

Design Approach ◽

Engineering Program ◽

The Relationship

Improving creativity in engineering design continues to be a challenge. The relationship between fixation and creativity within engineering is mixed, as engineers desire to be innovative, yet are usually working from their existing knowledge to redesign existing products. In the current study, we wanted to examine the influence of physical examples on originality and fixation at the freshmen and senior level in a Mechanical Engineering program. We compared concepts for garbage collection systems generated by two groups — one provided with an example product (Example group), and another who did not receive an example product (No Example group). Using metrics established in prior publications, we found that seniors had higher levels of originality than freshmen whether an example product was received or not, reinforcing our previous findings. Fixation scores were higher for the group that did have an example. Receiving an example product was not a predictor of originality on its own, but did interact with curriculum and fixation level. Within the group that received an example product, there was a negative relationship between fixation and originality, particularly for the seniors. Within the group that did not receive an example product, there was no significant relationship between fixation and originality. Further analysis of our results are required to delineate how not receiving an example product influences design approach in freshmen and senior engineering students.

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New Engineering Approach for the Development and Demonstration of a Multi-Purpose Platform for the Blue Growth Economy

Volume 6: Ocean Space Utilization ◽

10.1115/omae2019-96104 ◽

2019 ◽

Cited By ~ 1

Author(s):

F. Lagasco ◽

M. Collu ◽

A. Mariotti ◽

E. Safier ◽

F. Arena ◽

...

Keyword(s):

Engineering Design ◽

Large Scale ◽

Marine Ecosystem ◽

Research Work ◽

Electric Energy ◽

Electricity Production ◽

Floating Structure ◽

Original Solution ◽

Engineering Approach ◽

Blue Growth

Abstract Aquaculture is currently the fastest growing food sector in the world and the open oceans are seen as one of the most likely areas for large-scale expansion [1], [2], [3]. The global demand for seafood is continuing to rise sharply, driven by both population growth and increased per capita consumption, whilst wild-capture fisheries are constrained in their potential to produce more seafood. A recently funded EC project, the Blue Growth Farm – BGF (GA n. 774426, 1st June 2018 – 30th September 2021) aims at contributing to this world need with an original solution. The Blue Growth Farm proposes an efficient, cost-competitive and environmentally friendly multi-purpose offshore farm concept. It is based on a modular floating structure, moored to the seabed, meeting requirements of efficiency, cost-competitiveness and environmental friendless, where automated aquaculture and renewable energy production systems are integrated and engineered for profitable applications in the open sea. In the present paper, the overall engineering approach developed to carry out the research work is presented, described and justified. Different technical and scientific challenges are addressed through an integrated industrial engineering design approach, where all disciplines are tuned to achieve the Blue Growth Farm main targets. These are represented by: i) guaranteeing expected nominal fish production thanks to advanced automation and remote control capabilities; ii) minimizing the pollution introduced at marine ecosystem level when exploiting the marine natural resources, whilst increasing the social acceptance and users community agreement; iii) maximizing the electricity production in the Blue Growth Farm potential installation area ecosystem to provide energy supply to the on-board electrical equipment and to dispatch the extra produced electric energy to the land network. Preliminary engineering design results are promising to demonstrate effective increase of safety and efficiency by reducing on-board human effort and consequently risks at offshore, thus to make commercial-scale open ocean farming a reality. The present paper introduces overall concepts and design methodology whilst other companion works submitted at OMAE2019 [4], [5], [6] provide insight of specific aspects of the Blue Growth Farm project elaborated during the first six months activity.

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Enabling Collaborative Engineering With Computer Tools

Volume 1: 20th Computers and Information in Engineering Conference ◽

10.1115/detc2000/cie-14600 ◽

2000 ◽

Author(s):

Kathleen L. Kitto ◽

Eric K. McKell

Keyword(s):

Engineering Design ◽

Numerical Control ◽

Computer Integrated Manufacturing ◽

Engineering Technology ◽

Collaborative Engineering ◽

Computer Tools ◽

Engineering Approach ◽

Advisory Boards ◽

Engineering Design Graphics ◽

Advanced Computer

Abstract The Engineering Technology Department at Western Washington University has been using the integration of advanced Computer Aided Engineering (CAE) tools to enable a redesign of the curriculum that uses a collaborative engineering approach similar to the environment used within our industrial counterparts. In today’s competitive global marketplace, those industrial organizations must produce higher quality, easier to manufacture and maintain parts in shorter periods of time. Products are most often created in concurrent engineering or collaborative business environments where rapid sharing of information is the very essence of modern engineering. In addition, the widespread use and availability of the Internet has changed the nature of engineering data management and exchange. Therefore, CAE tools must enable engineers, analysts, technologists and designers to do their jobs more efficiently in a world where time to market is ever shortening. The best CAE tools increase productivity, because they are “smarter” tools. Only when the students are well versed in the multi-faceted collaborative engineering atmosphere and the accompanying modern CAE tools within the curriculum are they truly ready to become immediately productive in the workplace after graduation. This paper first describes the collaborative engineering approach used in the curriculum within the Engineering Technology Department during the past two years. It then describes the use of CAE tools used in the collaborative engineering approach in departmental projects. Next, it details the classes that have been specifically enabled by the use CAE tools including Engineering Design Graphics I, Engineering Design Graphics II, Numerical Control Operations, Advanced Computer Numerical Control (CNC), Tool Design, and Computer Integrated Manufacturing (which includes rapid prototyping and finite element analysis). The final section of the paper outlines future plans for enhancing the curriculum further in both the integration of computer tools and the continued development of continuing cross-disciplinary projects based on careful outcomes assessment and feedback from industrial advisory boards and professional societies.

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Thoughts on Model Validation for Engineering Design

Volume 3a: 8th Design for Manufacturing Conference ◽

10.1115/detc2003/dtm-48632 ◽

2003 ◽

Cited By ~ 6

Author(s):

George A. Hazelrigg

Keyword(s):

Predictive Model ◽

Engineering Design ◽

Decision Maker ◽

Model Validation ◽

Predictive Models ◽

Specific Model ◽

System Behavior ◽

Crucial Element ◽

Model Validity ◽

Subjective Input

Models are the basis for all prediction of system behavior, and hence form a crucial element of engineering design. A key concern is the validity of such models. This paper discusses the notion of model validity and the limits of what one can say about the validity of a specific model. It is shown that predictive models, such as those used in engineering design, cannot be validated objectively. That is, the validation of a predictive model can be accomplished only in the context of a specific decision, and only in the context of subjective input from the decision maker, including preferences.

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