1
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Picard-Weibel A, Capson-Tojo G, Guedj B, Moscoviz R. Bayesian uncertainty quantification for anaerobic digestion models. BIORESOURCE TECHNOLOGY 2024; 394:130147. [PMID: 38049015 DOI: 10.1016/j.biortech.2023.130147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/06/2023]
Abstract
Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian procedure, called VarBUQ, ensuring a correct tradeoff between flexibility and computational cost. A benchmark against three existing methods (Fisher's information, bootstrapping and Beale's criteria) was conducted using synthetic data. This Bayesian procedure offered a good compromise between fitting ability and confidence estimation, while the other methods proved to be repeatedly overconfident. The method's performances notably benefitted from inductive bias brought by the prior distribution, although it requires careful construction. This article advocates for more systematic consideration of uncertainty for anaerobic digestion models and showcases a new, computationally efficient Bayesian method. To facilitate future implementations, a Python package called 'aduq' is made available.
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Affiliation(s)
- Antoine Picard-Weibel
- SUEZ, CIRSEE, 38 rue du Président Wilson, 78230 Le Pecq, France; Laboratoire Paul Painlevé, Univ. de Lille Cité Scientifique, F-59655 Villeneuve d'Ascq, France; MODAL, Inria 40 avenue Halley, 59650 Villeneuve d'Ascq, France.
| | | | - Benjamin Guedj
- Centre for Artificial Intelligence, UCL 90 High Holborn, WC1V 6LJ London, United Kingdom; MODAL, Inria 40 avenue Halley, 59650 Villeneuve d'Ascq, France
| | - Roman Moscoviz
- SUEZ, CIRSEE, 38 rue du Président Wilson, 78230 Le Pecq, France
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2
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Breitling R, Avbelj M, Bilyk O, Carratore F, Filisetti A, Hanko EKR, Iorio M, Redondo RP, Reyes F, Rudden M, Severi E, Slemc L, Schmidt K, Whittall DR, Donadio S, García AR, Genilloud O, Kosec G, De Lucrezia D, Petković H, Thomas G, Takano E. Synthetic biology approaches to actinomycete strain improvement. FEMS Microbiol Lett 2021; 368:6289918. [PMID: 34057181 PMCID: PMC8195692 DOI: 10.1093/femsle/fnab060] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/28/2021] [Indexed: 12/17/2022] Open
Abstract
Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.
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Affiliation(s)
- Rainer Breitling
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Martina Avbelj
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia
| | - Oksana Bilyk
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Francesco Del Carratore
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | | | - Erik K R Hanko
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | | | | | - Fernando Reyes
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Avenida del Conocimiento 34, Parque Tecnologico de Ciencias de la Salud, 18016 Armilla, Granada, Spain
| | - Michelle Rudden
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD, UK
| | | | - Lucija Slemc
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia
| | - Kamila Schmidt
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Dominic R Whittall
- Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | | | | | - Olga Genilloud
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Avenida del Conocimiento 34, Parque Tecnologico de Ciencias de la Salud, 18016 Armilla, Granada, Spain
| | - Gregor Kosec
- Acies Bio d.o.o., Tehnološki Park 21, 1000, Ljubljana, Slovenia
| | - Davide De Lucrezia
- Explora Biotech Srl, Doulix business unit, Via Torino 107, 30133 Venice, Italy
| | - Hrvoje Petković
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia
| | - Gavin Thomas
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD, UK
| | - Eriko Takano
- Corresponding author: Department of Chemistry, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. E-mail:
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3
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Kahl D, Kschischo M. Searching for Errors in Models of Complex Dynamic Systems. Front Physiol 2021; 11:612590. [PMID: 33505318 PMCID: PMC7830364 DOI: 10.3389/fphys.2020.612590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/11/2020] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples.
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Affiliation(s)
- Dominik Kahl
- Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
| | - Maik Kschischo
- Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
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4
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Bodner K, Brimacombe C, Chenery ES, Greiner A, McLeod AM, Penk SR, Vargas Soto JS. Ten simple rules for tackling your first mathematical models: A guide for graduate students by graduate students. PLoS Comput Biol 2021; 17:e1008539. [PMID: 33444343 PMCID: PMC7808623 DOI: 10.1371/journal.pcbi.1008539] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Korryn Bodner
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Ecology and Evolution, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - Chris Brimacombe
- Department of Ecology and Evolution, University of Toronto, Toronto, Ontario, Canada
| | - Emily S. Chenery
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Ariel Greiner
- Department of Ecology and Evolution, University of Toronto, Toronto, Ontario, Canada
| | - Anne M. McLeod
- Department of Biology, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
| | - Stephanie R. Penk
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Ecology and Evolution, University of Toronto, Toronto, Ontario, Canada
| | - Juan S. Vargas Soto
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Department of Ecology and Evolution, University of Toronto, Toronto, Ontario, Canada
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5
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Darlington APS, Bates DG. Architectures for Combined Transcriptional and Translational Resource Allocation Controllers. Cell Syst 2020; 11:382-392.e9. [PMID: 32937113 DOI: 10.1016/j.cels.2020.08.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/23/2020] [Accepted: 08/21/2020] [Indexed: 12/23/2022]
Abstract
Recent work on engineering synthetic cellular circuitry has shown that non-regulatory interactions mediated by competition for gene expression resources can result in degraded performance or even failure. Transcriptional and translational resource allocation controllers based on orthogonal circuit-specific gene expression machinery have separately been shown to improve modularity and circuit performance. Here, we investigate the potential advantages, challenges, and design trade-offs involved in combining transcriptional and translational controllers into a "dual resource allocation control system." We show that separately functional, translational, and transcriptional controllers cannot generally be combined without extensive redesign. We analyze candidate architectures for direct design of dual resource allocation controllers and propose modifications to improve their performance (in terms of decoupling and expression level) and robustness. We show that dual controllers can be built that are composed only of orthogonal gene expression resources and demonstrate that such designs offer both superior performance and robustness characteristics.
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Affiliation(s)
- Alexander P S Darlington
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, UK
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, UK.
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6
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Adachi T, Kainuma K, Asano K, Amagai M, Arai H, Ishii KJ, Ito K, Uchio E, Ebisawa M, Okano M, Kabashima K, Kondo K, Konno S, Saeki H, Sonobe M, Nagao M, Hizawa N, Fukushima A, Fujieda S, Matsumoto K, Morita H, Yamamoto K, Yoshimoto A, Tamari M. Strategic Outlook toward 2030: Japan's research for allergy and immunology - Secondary publication. Allergol Int 2020; 69:561-570. [PMID: 32600925 DOI: 10.1016/j.alit.2020.04.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/09/2020] [Indexed: 12/17/2022] Open
Abstract
Strategic Outlook toward 2030: Japan's Research for Allergy and Immunology (Strategy 2030) is the national research strategy based on Japan's Basic Law on Measures Against Allergic Diseases, a first of its kind worldwide. This strategy was established by a multi-disciplinary committee consisting of administrators of the Ministry of Health, Labour and Welfare of Japan, young and senior experts from various research societies and associations, and representatives of patient and public groups. Whereas the issues of transition, integration, and international collaboration have yet to be solved in this research realm in Japan, identification of unmet needs, digitization of information and transparent procedures, and strategic planning for complex problems (a process dubbed MIERUKA by the Toyota Way) are crucial to share and tackle the same vision and goals. The committee developed three specific actions focusing on preemptive treatment, interdisciplinarity and internationality, and life stage. The real success of Strategy 2030 is made by the spontaneous contributions of doctors, dentists, veterinarians, and other medical professionals; basic and clinical research scientists, research supporters, and pharmaceutical/medical device companies; manufacturers of food, healthcare, and home appliances; and patients, their families, and the public. The hope is to establish a stable society in which people can live long, healthy lives, as free as possible from allergic and immunological diseases, at each individual life stage. This article is based on a Japanese review first reported in Arerugi, introduces the developmental process and details of Strategy 2030.
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Affiliation(s)
- Takeya Adachi
- Japan Agency for Medical Research and Development (AMED), Tokyo, Japan; International Human Frontier Science Program Organization (HFSPO), Strasbourg, France; CNRS UPR 3572, Institut de Biologie Moléculaire et Cellulaire (IBMC), Université de Strasbourg, Strasbourg, France.
| | - Keigo Kainuma
- Institute for Clinical Research, National Hospital Organization, Mie National Hospital, Mie, Japan
| | - Koichiro Asano
- Division of Pulmonary Medicine, Department of Medicine, Tokai University, School of Medicine, Kanagawa, Japan
| | - Masayuki Amagai
- Department of Dermatology, Keio University School of Medicine, Tokyo, Japan
| | - Hiroyuki Arai
- Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan
| | - Ken J Ishii
- Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Komei Ito
- Department of Allergy, Aichi Children's Health and Medical Center, Aichi, Japan
| | - Eiichi Uchio
- Department of Ophthalmology, Fukuoka University School of Medicine, Fukuoka, Japan
| | - Motohiro Ebisawa
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization, Sagamihara National Hospital, Kanagawa, Japan
| | - Mitsuhiro Okano
- Department of Otorhinolaryngology, International University of Health and Welfare School of Medicine, Chiba, Japan
| | - Kenji Kabashima
- Department of Dermatology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Kondo
- Department of Otolaryngology and Head and Neck Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Satoshi Konno
- Department of Respiratory Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Hidehisa Saeki
- Department of Cutaneous and Mucosal Pathophysiology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Mariko Sonobe
- Japanese Mother's Society for Allergy Care (JMSAC), Kanagawa, Japan
| | - Mizuho Nagao
- Institute for Clinical Research, National Hospital Organization, Mie National Hospital, Mie, Japan
| | - Nobuyuki Hizawa
- Division of Respiratory Medicine, Institute of Clinical Medicine, University of Tsukuba, Ibaraki, Japan
| | | | - Shigeharu Fujieda
- Department of Otorhinolaryngology-Head and Neck Surgery, School of Medicine, University of Fukui, Fukui, Japan
| | - Kenji Matsumoto
- Department of Allergy and Clinical Immunology, National Research Institute for Child Health and Development, Tokyo, Japan
| | - Hideaki Morita
- Department of Allergy and Clinical Immunology, National Research Institute for Child Health and Development, Tokyo, Japan
| | - Kazuhiko Yamamoto
- Center for Integrative Medical Sciences, The Institute of Physical and Chemical Research (RIKEN), Kanagawa, Japan
| | | | - Mayumi Tamari
- Division of Molecular Genetics, The Jikei University School of Medicine, Research Center for Medical Science, Tokyo, Japan.
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7
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Tsigkinopoulou A, Takano E, Breitling R. Unravelling the γ-butyrolactone network in Streptomyces coelicolor by computational ensemble modelling. PLoS Comput Biol 2020; 16:e1008039. [PMID: 32649676 PMCID: PMC7384680 DOI: 10.1371/journal.pcbi.1008039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 07/27/2020] [Accepted: 06/10/2020] [Indexed: 02/06/2023] Open
Abstract
Antibiotic production is coordinated in the Streptomyces coelicolor population through the use of diffusible signaling molecules of the γ-butyrolactone (GBL) family. The GBL regulatory system involves a small, and not completely defined two-gene network which governs a potentially bi-stable switch between the “on” and “off” states of antibiotic production. The use of this circuit as a tool for synthetic biology has been hampered by a lack of mechanistic understanding of its functionality. We here present the creation and analysis of a versatile and adaptable ensemble model of the Streptomyces GBL system (detailed information on all model mechanisms and parameters is documented in http://www.systemsbiology.ls.manchester.ac.uk/wiki/index.php/Main_Page). We use the model to explore a range of previously proposed mechanistic hypotheses, including transcriptional interference, antisense RNA interactions between the mRNAs of the two genes, and various alternative regulatory activities. Our results suggest that transcriptional interference alone is not sufficient to explain the system’s behavior. Instead, antisense RNA interactions seem to be the system's driving force, combined with an aggressive scbR promoter. The computational model can be used to further challenge and refine our understanding of the system’s activity and guide future experimentation. Streptomyces species are Gram-positive soil-dwelling bacteria, which are known as a prolific source of secondary metabolites, such as antibiotics. Antibiotic production is coordinated in the bacterial population through the use of diffusible signalling molecules of the γ-butyrolactone (GBL) family. The GBL regulatory system involves a small, yet complex two-gene network, the mechanism of which has not yet been completely defined. The complete elucidation of this system could potentially lead to the ability to design reliable and sensitive engineered cellular switches. We therefore designed a versatile model of the GBL system in order to investigate the feasibility of various hypothesized mechanisms. The ensemble modelling analysis that we performed revealed that antisense RNA interactions seem to be the system’s driving force, together with an aggressive scbR promoter. Transcriptional interference is also significant; however, it is not sufficient to explain the system’s behavior by itself. Finally, the model indicates key experiments, which could completely elucidate the role of the system and the interactions of its components and potentially lead to the design of reliable and sensitive systems with significant applications as orthologous regulatory circuits in synthetic biology and biotechnology.
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Affiliation(s)
- Areti Tsigkinopoulou
- DTU Biosustain, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Manchester Institute of Biotechnology, School of Natural Sciences, University of Manchester, Manchester, United Kingdom
| | - Eriko Takano
- Manchester Institute of Biotechnology, School of Natural Sciences, University of Manchester, Manchester, United Kingdom
| | - Rainer Breitling
- Manchester Institute of Biotechnology, School of Natural Sciences, University of Manchester, Manchester, United Kingdom
- * E-mail:
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8
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Finnigan W, Citoler J, Cosgrove SC, Turner NJ. Rapid Model-Based Optimization of a Two-Enzyme System for Continuous Reductive Amination in Flow. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00075] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- William Finnigan
- Department of Chemistry, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Joan Citoler
- Department of Chemistry, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Sebastian C. Cosgrove
- Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Nicholas J. Turner
- Department of Chemistry, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
- Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom
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9
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Peskov K, Azarov I, Chu L, Voronova V, Kosinsky Y, Helmlinger G. Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology. Front Immunol 2019; 10:924. [PMID: 31134058 PMCID: PMC6524731 DOI: 10.3389/fimmu.2019.00924] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 04/10/2019] [Indexed: 12/15/2022] Open
Abstract
Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.
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Affiliation(s)
- Kirill Peskov
- M&S Decisions, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, MA, United States
| | | | | | - Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, MA, United States
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10
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Tsigkinopoulou A, Hawari A, Uttley M, Breitling R. Defining informative priors for ensemble modeling in systems biology. Nat Protoc 2019; 13:2643-2663. [PMID: 30353176 DOI: 10.1038/s41596-018-0056-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Ensemble modeling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches that sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modeling have been published, the issue of generating informative priors has not yet been addressed. Here, we present a protocol that aims to fill this gap. This protocol discusses the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessment of their plausibility, and creation of log-normal probability distributions that can be used as informative priors in ensemble modeling. Furthermore, the protocol enables sampling from the generated distributions while maintaining thermodynamic consistency. Once all parameter values have been retrieved from literature and databases, the protocol can be implemented within ~5-10 min per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modeling, especially in fields such as synthetic biology and systems medicine.
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Affiliation(s)
- Areti Tsigkinopoulou
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, United Kingdom
| | - Aliah Hawari
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, United Kingdom
| | - Megan Uttley
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, United Kingdom
| | - Rainer Breitling
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, United Kingdom.
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11
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Duran‐Frigola M, Fernández‐Torras A, Bertoni M, Aloy P. Formatting biological big data for modern machine learning in drug discovery. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1408] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Miquel Duran‐Frigola
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Adrià Fernández‐Torras
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Martino Bertoni
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Patrick Aloy
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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12
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Gómez-Schiavon M, El-Samad H. Complexity-aware simple modeling. Curr Opin Microbiol 2018; 45:47-52. [PMID: 29494832 DOI: 10.1016/j.mib.2018.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 01/07/2018] [Indexed: 11/19/2022]
Abstract
Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches.
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Affiliation(s)
- Mariana Gómez-Schiavon
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States; Chan Zuckerberg Biohub, San Francisco, CA 94158, United States.
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13
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