A logic-reasoning based system to harness bioprocess experimental data and knowledge for design |
| |
| Institution: | 1. Department of Biochemical Engineering, University College London, London WC1E 7JE, United Kingdom;2. Department of Computer Science, University College London, Torrington Place, London WC1E 7JE, United Kingdom;1. Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;2. Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;3. Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;1. Key Laboratory of Nuclear Resources and Environment (National Key Laboratory), East China University of Technology, 418 Guanglan Road, Nanchang 330013, PR China;2. School of Materials Science and Chemical Engineering, Ningbo University, 818 Fenghua Road, Ningbo 315211, PR China;3. State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China;1. Acharya Nagarjuna University, Department of Botany and Microbiology, Guntur, India;2. CSIR-Indian Institute of Chemical Technology, Organic Chemistry Division-II (CPC), Hyderabad, India;3. CSIR-Indian Institute of Chemical Technology, Organic Chemistry Division-I (Natural products Laboratory), Hyderabad, India |
| |
| Abstract: | Bioprocess design requires substantial resources during the experimental investigation of the options for each bioprocess step. This is both time-consuming and expensive. The amount of data available has increased exponentially since the expansion of new biological drug development. Data are heterogeneous, sometimes inconsistent and incomplete, making them difficult to be systematically utilised for analysis for any new bioprocess design. In this paper, we report a novel computational method that harnesses the bioprocess experimental data to assist design decision making, and perhaps identify further needed experiments. First, we develop a new data representation structure to capture the experimental data systematically. Then the ontology for modelling the relationship of data properties is created. A computational system has been developed to search relevant data, or to predict required process conditions, or to suggest a new set of experiments for process development. A prototype for harnessing centrifugation experimental data has been built, and is then used to illustrate the method and demonstrate the type of results that can be obtained. Evaluations show that such a system has significant potential to mine the relevant experimental data to assist new drug bioprocess development, which should reduce process development time and cost. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
