Computer-Aided Modeling of Chemical and Biological Systems:  Methods, Tools, and Applications

Mangold, Michael; Angeles-Palacios, O.; Ginkel, Martin; Kremling, Andreas; Waschler, R.; Kienle, Achim; Gilles, Ernst Dieter

doi:10.1021/ie0496434

Cited by 16 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bogusch et al refer to an ontology-based approach for managing process model equations that defines the semantics between the equations and their associated variables. Although many research groups have tried to use such an ontological structured framework for managing mathematical knowledge in process models (e.g., ModKit or ProMot), most of them have had to rely upon the simulation environment/engine to analyze and solve the systems of equations while powerful commercial solvers have been available. Marquardt et al described the design and implementation of ROME (Repository of a Modeling Environment), which is a model repository to support the maintenance of heterogeneous process models and their integration from a data management point of view.…”

Section: Existing Modeling Knowledge Management Approaches and Limita...mentioning

confidence: 99%

OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

Suresh

Hsu

Akkisetty

et al. 2010

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Pharmaceutical product development is a critical step in the path of a drug therapy from its discovery to its delivery to the patient. It is capital-intensive, time-consuming, and extremely information-and knowledgeintensive. This presents various challenges to manage the information and knowledge involved in a systematic, reusable, and user-friendly manner. Knowledge, in this context, means decision-making knowledge and mathematical knowledge that capture the families of mathematical models that exist in this domain. In this paper, which is the first of this two-part series of papers, we describe OntoMODEL, which is an ontological tool for mechanistic mathematical model management that facilitates systematic and standardizable methods for model storage, usage, and solution. [Suresh and co-workers have presented discussions on OntoMODEL at AIChE meetings in San Francisco, CA (2006) and Salt Lake City, UT (2007), as well as at the 18th European Symposium on Computer-Aided Process Engineering (ESCAPE-18) in Lyon, France (2008).] While the declarative knowledge in mathematical models is captured using ontologies, the procedural knowledge required for solving these models is handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well-established and the approach-intuitive; therefore, they do not require user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more-advanced applications such as model-based fault diagnosis, model predictive control (which is decribed in the second paper of this series), knowledge-based decisionmaking, and process flowsheet simulation, making it a useful tool in the intelligent automation of process operations. This paper describes the framework and use of OntoMODEL and discusses how it overcomes the shortcomings of existing approaches toward managing mathematical modeling knowledge.

show abstract

Section: Existing Modeling Knowledge Management Approaches and Limita...mentioning

confidence: 99%

OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

Suresh

Hsu

Akkisetty

et al. 2010

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…A large number of real-world systems can be modeled as network systems: technical systems Marquardt et al 2000), physicochemical systems (e.g., , biological systems (e.g., Mangold et al 2005), economic systems (e.g., Andresen 1999), social systems (e.g., Bunge 1979), and others. Generally, the devices are the crucial elements of a network system and hold the major functionality, while the connections represent the linkages between the devices.…”

Section: Usagementioning

confidence: 99%

OntoCAPE

Marquardt¹,

Morbach²,

Wiesner³

et al. 2010

RWTHedition

View full text Add to dashboard Cite

in 1993. His research interest cover a number of areas in process systems engineering, including modeling methodologies, process synthesis, control and operations, numerical methods as well as information technologies for the support of chemical engineering design processes. engineering. During his post-doctoral career, he worked for several institutions in the area of process systems engineering, with a focus on information modeling and mathematical modeling of chemical process systems. In particular, he was a research fellow at AVT -Process Systems Engineering, RWTH Aachen University, from 1999 to 2004, where his research was mainly devoted to the development and applications of ontologies in process engineering. Aidong is currently a lecturer in Process Systems Engineering at the Faculty of Engineering and Physical Sciences of the University of Surrey, UK. His research interests include the mathematical modeling and optimization of process systems, the application of knowledge engineering methods in process and product development and in manufacturing, and the design of engineering software systems.x Preface Preface xi modeling under different funding schemes. This research formed one of the pillars of our research on ontological engineering. A second pillar are the CAPE-OPEN and Global CAPE-OPEN projects funded by the EU under the Brite-EuRam action line. These industry-driven projects on the standardization of interfaces in and between process engineering software systems provided us with a great deal of insight in many practical issues of software engineering, information modeling, and data integration in the context of process simulation and design in the process industries. Most of the research described in this book has been performed in the context of the IMPROVE project, funded by DFG as CRC 476 and TC 61, and the COGents project, funded by the EU under the IST program in the fifth Framework Program. The generous financial support through these projects is gratefully acknowledged. We are also indebted to a large number of individuals who directly or indirectly contributed to the results presented in this book. We enjoyed the very close and lasting collaboration with a number of research groups in computer science in areas related to ontological engineering. Most notably, we interacted with Franz Baader and his former PhD students Ulrike Sattler and Ralph Molitor, with Bertrand Braunschweig, with Matthias Jarke and his former PhD students Sebastian C. Brandt, Michalis Miatidis, Klaus Pohl and Klaus Weidenhaupt as well as with Manfred Nagl and his former PhD students Simon Becker, Markus Heller and Bernhard Westfechtel. This interdisciplinary interaction, and the sharing of the various perspectives on knowledge representation and information modeling, integration, and management contributed significantly to shape our understanding of the relevant issues. The reconciliation of the diverse schools of thought in computer science has been an important stepping stone towards OntoCAPE, its architectural princi...

show abstract

“…Extensive reviews on current tools and techniques to support and facilitate the modeling have been published (see, e.g., ). Some of these studies concentrate on models from systems biology, which will not be considered here.…”

Section: Introductionmentioning

confidence: 99%

“…Meaningful biotic states are again unknown, necessitating the introduction of formal kinetics for the overall reaction system. Extensive reviews on current tools and techniques to support and facilitate the modeling have been published (see, e.g., [5][6][7][8][9]). Some of these studies concentrate on models from systems biology, which will not be considered here.…”

Section: Introductionmentioning

confidence: 99%

Rapid process synthesis supported by a unified modular software framework

Herold

Krämer

Violet

et al. 2017

Engineering in Life Sciences

View full text Add to dashboard Cite

Rapid process synthesis supported by a unified modular software frameworkAlthough known to be very powerful, the widespread application of model-based techniques is still significantly hampered in the area of bio-processes. Reasons for this situation can be found along the whole chain to set up and implement such approaches. In a time-consuming step, models are typically hand-crafted. Whether alternatives of better models exist to actually fulfill the final goals is undocumented, most often even unknown. In a next step, model-based process control methods are hand-coded in an error-prone procedure. For many of these methods given in the literature, only simulation studies are shown, leaving the interested reader with the unanswered question whether the implementation of a specific method in a real process is viable. As the potentially time-consuming implementation of such a method presents a risk for a rapid process development, promising candidates may be overlooked. To remediate this unsatisfactory situation, a combination of theoretical methods and information technology is proposed here. By an exemplarily realized software tool, it is shown how such an environment helps to promote model-based optimization, supervision, and control of bio-processes and allows for an inexpensive test of new ideas as well in real-life experiments. The contribution concentrates on an overview of a possible software architecture with respect to necessary methods and a meaningful information strategy, highlighting some of the more crucial building blocks. Experimental results exploiting parts of the proposed methods are given for a yeast strain synthesizing a product of industrial interest.

show abstract

Computer-Aided Modeling of Chemical and Biological Systems: Methods, Tools, and Applications

Cited by 16 publications

References 36 publications

OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

OntoCAPE

Rapid process synthesis supported by a unified modular software framework

Contact Info

Product

Resources

About