The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Biological Sciences
Abstract
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Organisations
| Description | Mathematical research methods were updated by our collaborators in the Exeter part of the award, using rapid prototyping methods in proprietary software. Our team kept abreast of their progress and provided biological information, example models and explained appropriate modelling conditions. The Edinburgh team, as proposed, has made the key parts of this software publicly available in open-source, non-proprietary forms that are commonly used in the modelling community, specifically refactoring and benchmarking the NMMSO optimiser in the python language, as "pynmmso", Niching Migratory Multi-Swarm Optimiser for Python. The tool is packaged for ease of integration with biological modelling environments such as the worked example that we provide, using the python bindings for libRoadRunner (http://libroadrunner.org/). libRoadRunner is a high performance and portable simulation engine for systems and synthetic biology and can simulate models in SBML format, from the Sauro lab, U. of Washington. |
| Exploitation Route | This mathematical method from Exeter is highly relevant to the increasingly challenging task of calibrating larger biological models to data. However, it will also apply to other models that produce complex optimisation landscapes, in other words where the model works well with several (or many) different parameter sets and it is challenging to find the best set. |
| Sectors | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Pharmaceuticals and Medical Biotechnology |
| URL | https://github.com/EPCCed/pynmmso |
| Description | Interdisciplinary projects such as this one between Biology, Computer Science and Mathematics, provide challenging training for already highly-skilled research personnel. In this award, biologist Millar and computer scientist Hume extended their understanding of model optimisation to the novel mathematical methods provided by our collaborators in Exeter, and software developers Neelofer and Wood gained their first exposure to biological modelling. Training highly-skilled people is a key output of discovery research, as they go on to impact society throughout their lives. |
| First Year Of Impact | 2019 |
| Title | Niching Migratory Multi-Swarm Optimiser for Python, pynmmso |
| Description | pynmmso is a python implementation of the Niching Migratory Multi-Swarm Optimiser, described in: "Running Up Those Hills: Multi-Modal Search with the Niching Migratory Multi-Swarm Optimiser" by Jonathan E. Fieldsend published in Proceedings of the IEEE Congress on Evolutionary Computation, pages 2593-2600, 2014 (http://hdl.handle.net/10871/15247). Examples of the method in action can be obtained from: https://github.com/EPCCed/pynmmso-examples Code to run benchmark problems using pynmmso can be found at: https://github.com/EPCCed/pynmmso-benchmarking The Python implementation of NMMSO requires Python 3 and Numpy (https://www.numpy.org/). One of the examples illustrates the optimisation of an SBML model of the Neurospora circadian oscillator, using simulations by libRoadRunner from the Sauro group. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | The software has just been released. |
| URL | https://github.com/EPCCed/pynmmso |