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Conte L, Gonella F, Giansanti A, Kleidon A, Romano A. Modeling cell populations metabolism and competition under maximum power constraints. PLoS Comput Biol 2023; 19:e1011607. [PMID: 37939139 PMCID: PMC10659174 DOI: 10.1371/journal.pcbi.1011607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 11/20/2023] [Accepted: 10/16/2023] [Indexed: 11/10/2023] Open
Abstract
Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to model cell metabolisms and competition under energetic constraints. As a proof-of-concept, we apply this general formulation to study the dynamics of the onset of a specific blood cancer-called Multiple Myeloma. We show that a minimal model describing antagonist cell populations competing for limited resources, as regulated by microenvironmental factors and internal cellular structures, reproduces patterns of Multiple Myeloma evolution, due to the uncontrolled proliferation of cancerous plasma cells within the bone marrow. The model is characterized by a class of regime shifts to more dissipative states for selectively advantaged malignant plasma cells, reflecting a breakdown of self-regulation in the bone marrow. The transition times obtained from the simulations range from years to decades consistently with clinical observations of survival times of patients. This irreversible dynamical behavior represents a possible description of the incurable nature of myelomas based on the ecological interactions between plasma cells and the microenvironment, embedded in a larger complex system. The use of ATP equivalent energy units in defining stocks and flows is a key to constructing an ecological model which reproduces the onset of myelomas as transitions between states of a system which reflects the energetics of plasma cells. This work provides a basis to construct more complex models representing myelomas, which can be compared with model ecosystems.
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Affiliation(s)
- Luigi Conte
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venezia Mestre, Italy
- Department of Physics, Sapienza University of Rome, Roma, Italy
- Centre for the Study of the Systemic Dynamics of Complex Diseases, Venezia Mestre, Italy
| | - Francesco Gonella
- Centre for the Study of the Systemic Dynamics of Complex Diseases, Venezia Mestre, Italy
- Department of Molecular Sciences and Nanosystems, Ca’ Foscari University of Venice, Venezia Mestre, Italy
- THE NEW INSTITUTE Centre for Environmental Humanities (NICHE), Venezia, Italy
| | - Andrea Giansanti
- Department of Physics, Sapienza University of Rome, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Roma, Italy
| | - Axel Kleidon
- Biospheric Theory and Modeling Group, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Alessandra Romano
- Centre for the Study of the Systemic Dynamics of Complex Diseases, Venezia Mestre, Italy
- Department of General Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy
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2
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Khan SA, Lehmann R, Martinez-de-Morentin X, Maillo A, Lagani V, Kiani NA, Gomez-Cabrero D, Tegner J. scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences. PLoS One 2023; 18:e0281315. [PMID: 36735690 PMCID: PMC9897517 DOI: 10.1371/journal.pone.0281315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.
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Affiliation(s)
- Sumeer Ahmad Khan
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Robert Lehmann
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xabier Martinez-de-Morentin
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Alberto Maillo
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Vincenzo Lagani
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Narsis A. Kiani
- Department of Oncology and Pathology, Algorithmic Dynamic Lab, Karolinska Institute, Stockholm, Sweden
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - David Gomez-Cabrero
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
- Mucosal and Salivary Biology Division, King’s College London Dental Institute, London, United Kingdom
| | - Jesper Tegner
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Science for Life Laboratory, Solna, Sweden
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Zenil H, Marshall JAR, Tegnér J. Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results. Front Comput Neurosci 2023; 16:956074. [PMID: 36761393 PMCID: PMC9904762 DOI: 10.3389/fncom.2022.956074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 11/29/2022] [Indexed: 01/26/2023] Open
Abstract
Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett's Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats' behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the "internal" decision process in humans and animals.
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Affiliation(s)
- Hector Zenil
- Machine Learning Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
- Kellogg College, University of Oxford, Oxford, United Kingdom
- Oxford Immune Algorithmics Ltd., Oxford, United Kingdom
| | - James A. R. Marshall
- Complex Systems Modelling Research Group, Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Jesper Tegnér
- Living Systems Laboratory, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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4
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Identification of perturbed pathways rendering susceptibility to tuberculosis in type 2 diabetes mellitus patients using BioNSi simulation of integrated networks of implicated human genes. J Biosci 2022. [DOI: 10.1007/s12038-022-00309-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Ye J, Calvo IA, Cenzano I, Vilas A, Martinez-de-Morentin X, Lasaga M, Alignani D, Paiva B, Viñado AC, San Martin-Uriz P, Romero JP, Quilez Agreda D, Miñana Barrios M, Sancho-González I, Todisco G, Malcovati L, Planell N, Saez B, Tegner JN, Prosper F, Gomez-Cabrero D. Deconvolution of the hematopoietic stem cell microenvironment reveals a high degree of specialization and conservation. iScience 2022; 25:104225. [PMID: 35494238 PMCID: PMC9046238 DOI: 10.1016/j.isci.2022.104225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/14/2022] [Accepted: 04/05/2022] [Indexed: 11/28/2022] Open
Abstract
Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggest a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows inferring conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology is a stepping-stone toward a comprehensive cell atlas of the BM microenvironment.
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Affiliation(s)
- Jin Ye
- Bioscience Program, Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
| | - Isabel A. Calvo
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Itziar Cenzano
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
| | - Amaia Vilas
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Xabier Martinez-de-Morentin
- Navarrabiomed, ComplejoHospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, 31008 Navarra, Spain
| | - Miren Lasaga
- Navarrabiomed, ComplejoHospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, 31008 Navarra, Spain
| | - Diego Alignani
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Bruno Paiva
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Ana C. Viñado
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Patxi San Martin-Uriz
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
| | - Juan P. Romero
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
| | | | | | | | - Gabriele Todisco
- Department of Molecular Medicine, University of Pavia & Unit of Precision Hematology Oncology, IRCCS S. Matteo Hospital Foundation, 27100 Pavia, Italy
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Luca Malcovati
- Department of Molecular Medicine, University of Pavia & Unit of Precision Hematology Oncology, IRCCS S. Matteo Hospital Foundation, 27100 Pavia, Italy
| | - Nuria Planell
- Navarrabiomed, ComplejoHospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, 31008 Navarra, Spain
| | - Borja Saez
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
| | - Jesper N. Tegner
- Bioscience Program, Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
- Department of Medicine, Centre for Molecular Medicine, Karolinska Institutet, 17177 Stockholm, Stockholm, Sweden
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
- Bioengineering Program, Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
| | - Felipe Prosper
- Universidad de Navarra, CIMA, Hematology-Oncology Program, Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Navarra, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain
- Service of Hematology and Cell Therapy, Clínica Universidad de Navarra; CCUN, Pamplona, Navarra, 31008; Spain
| | - David Gomez-Cabrero
- Bioscience Program, Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
- Navarrabiomed, ComplejoHospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, 31008 Navarra, Spain
- Department of Medicine, Centre for Molecular Medicine, Karolinska Institutet, 17177 Stockholm, Stockholm, Sweden
- Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College, London WC2R 2LS, UK
- Bioengineering Program, Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23955, Saudi Arabia
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Tegner JN, Gomez-Cabrero D. Data-driven bioinformatics to disentangle cells within a tissue microenvironment. Trends Cell Biol 2022; 32:467-469. [DOI: 10.1016/j.tcb.2022.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022]
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Gomez-Cabrero D, Walter S, Abugessaisa I, Miñambres-Herraiz R, Palomares LB, Butcher L, Erusalimsky JD, Garcia-Garcia FJ, Carnicero J, Hardman TC, Mischak H, Zürbig P, Hackl M, Grillari J, Fiorillo E, Cucca F, Cesari M, Carrie I, Colpo M, Bandinelli S, Feart C, Peres K, Dartigues JF, Helmer C, Viña J, Olaso G, García-Palmero I, Martínez JG, Jansen-Dürr P, Grune T, Weber D, Lippi G, Bonaguri C, Sinclair AJ, Tegner J, Rodriguez-Mañas L. A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. GeroScience 2021; 43:1317-1329. [PMID: 33599920 PMCID: PMC8190217 DOI: 10.1007/s11357-021-00334-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.
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Affiliation(s)
- David Gomez-Cabrero
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Stefan Walter
- Dept. of Medicine and Public Health, Rey Juan Carlos University, Alcorcon, Spain
| | | | | | | | - Lee Butcher
- Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Jorge D Erusalimsky
- Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK
| | | | - José Carnicero
- Dept. of Geriatric Medicine, Complejo Hospitalario Universitario de Toledo (CHUT), Toledo, Spain
| | | | - Harald Mischak
- Mosaiques Diagnostics GmbH, Rotenburger Str. 20, 30659, Hannover, Germany
| | - Petra Zürbig
- Mosaiques Diagnostics GmbH, Rotenburger Str. 20, 30659, Hannover, Germany
| | - Matthias Hackl
- Evercyte GmbH; BOKU-University of Natural Resources and Life Sciences Vienna, Department of Biotechnology, Ludwig Boltzmann Institute of Experimental and Clinical Traumatology, Vienna, Austria
| | - Johannes Grillari
- Evercyte GmbH; BOKU-University of Natural Resources and Life Sciences Vienna, Department of Biotechnology, Ludwig Boltzmann Institute of Experimental and Clinical Traumatology, Vienna, Austria
| | - Edoardo Fiorillo
- Instituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Francesco Cucca
- Instituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Matteo Cesari
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | | | | | | | - Catherine Feart
- Univ. Bordeaux, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Karine Peres
- Univ. Bordeaux, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Jean-François Dartigues
- Univ. Bordeaux, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - Catherine Helmer
- Univ. Bordeaux, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France
| | - José Viña
- Freshage, University of Valencia, Valencia, Spain
| | - Gloria Olaso
- Freshage, University of Valencia, Valencia, Spain
| | | | | | - Pidder Jansen-Dürr
- Research Institute for Biomedical Aging Research, University of Innsbruck, Innsbruck, Austria
| | - Tilman Grune
- German Institute for Human Nutrition, Potsdam, Germany
| | - Daniela Weber
- German Institute for Human Nutrition, Potsdam, Germany
| | - Giuseppe Lippi
- Clinical Biochemistry and Molecular Biology, Universita di Verona, Verona, Italy
| | - Chiara Bonaguri
- Laboratoy Medicine Technical Sciences, Parma University, Parma, Italy
| | | | - Jesper Tegner
- Dept. of Medicine, Karolinska Institute, Stockholm, Sweden
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Leocadio Rodriguez-Mañas
- CIBER of Frailty and Healthy Aging, Madrid, Spain.
- Dept. of Geriatric Medicine, Getafe University Hospital, Getafe, Spain.
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Romano A, Casazza M, Gonella F. Addressing Non-linear System Dynamics of Single-Strand RNA Virus-Host Interaction. Front Microbiol 2021; 11:600254. [PMID: 33519741 PMCID: PMC7843927 DOI: 10.3389/fmicb.2020.600254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 12/09/2020] [Indexed: 12/27/2022] Open
Abstract
Positive single-strand ribonucleic acid [(+)ssRNA] viruses can cause multiple outbreaks, for which comprehensive tailored therapeutic strategies are still missing. Virus and host cell dynamics are tightly connected, generating a complex dynamics that conveys in virion assembly to ensure virus spread in the body. Starting from the knowledge of relevant processes in (+ss)RNA virus replication, transcription, translation, virions budding and shedding, and their respective energy costs, we built up a systems thinking (ST)-based diagram of the virus-host interaction, comprehensive of stocks, flows, and processes as well-described in literature. In ST approach, stocks and flows are expressed by a proxy of the energy embedded and transmitted, respectively, whereas processes are referred to the energy required for the system functioning. In this perspective, healthiness is just a particular configuration, in which stocks relevant for the system (equivalent but not limited to proteins, RNA, DNA, and all metabolites required for the survival) are constant, and the system behavior is stationary. At time of infection, the presence of additional stocks (e.g., viral protein and RNA and all metabolites required for virion assembly and spread) confers a complex network of feedbacks leading to new configurations, which can evolve to maximize the virions stock, thus changing the system structure, output, and purpose. The dynamic trajectories will evolve to achieve a new stationary status, a phenomenon described in microbiology as integration and symbiosis when the system is resilient enough to the changes, or the system may stop functioning and die. Application of external driving forces, acting on processes, can affect the dynamic trajectories adding a further degree of complexity, which can be captured by ST approach, used to address these new configurations. Investigation of system configurations in response to external driving forces acting is developed by computational analysis based on ST diagrams, with the aim at designing novel therapeutic approaches.
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Affiliation(s)
- Alessandra Romano
- Sezione di Ematologia, Dipartimento di Chirurgia Generale e Specialità Medico Chirurgiche (CHIRMED), Università degli Studi di Catania, Catania, Italy
- Division of Hematology, U.O.C di Ematologia, Azienda Ospedaliero Universitaria Policlinico “G.Rodolico - San Marco”, Catania, Italy
| | - Marco Casazza
- Division of Hematology, U.O.C di Ematologia, Azienda Ospedaliero Universitaria Policlinico “G.Rodolico - San Marco”, Catania, Italy
| | - Francesco Gonella
- Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca’ Foscari Venezia, Venezia, Italy
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Zenil H, Kiani NA, Marabita F, Deng Y, Elias S, Schmidt A, Ball G, Tegnér J. An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems. iScience 2019; 19:1160-1172. [PMID: 31541920 PMCID: PMC6831824 DOI: 10.1016/j.isci.2019.07.043] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 04/27/2019] [Accepted: 07/26/2019] [Indexed: 12/26/2022] Open
Abstract
We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network data.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden; Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Oxford Immune Algorithmics, Reading RG1 3EU, UK; Science for Life Laboratory, Solna 171 65, Sweden; Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris 75006, France.
| | - Narsis A Kiani
- Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden; Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden; Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris 75006, France
| | - Francesco Marabita
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Yue Deng
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden
| | - Szabolcs Elias
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Angelika Schmidt
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Gordon Ball
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden; Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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10
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Abstract
In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
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11
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Gomez-Cabrero D, Marabita F, Tarazona S, Cano I, Roca J, Conesa A, Sabatier P, Tegnér J. Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology. Cell Syst 2017; 5:168-175. [PMID: 28843483 DOI: 10.1016/j.cels.2017.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 02/21/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022]
Abstract
Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there.
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Affiliation(s)
- David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK.
| | - Francesco Marabita
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden
| | - Sonia Tarazona
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camí de Vera, 46022 Valencia, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Ana Conesa
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Philippe Sabatier
- TIMC-IMAG Laboratory, UMR 5525, Centre National de la Recherche Scientifique, Vetagro Sup, Université Grenoble-Alpes, 38400 Saint-Martin-d'Hères, France
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
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12
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Tegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2016.0144. [PMID: 27698038 PMCID: PMC5052728 DOI: 10.1098/rsta.2016.0144] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/15/2016] [Indexed: 05/06/2023]
Abstract
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.
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Affiliation(s)
- Jesper Tegnér
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Hector Zenil
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Narsis A Kiani
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Gordon Ball
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - David Gomez-Cabrero
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK
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13
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Novel Approaches in Astrocyte Protection: from Experimental Methods to Computational Approaches. J Mol Neurosci 2016; 58:483-92. [DOI: 10.1007/s12031-016-0719-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 01/13/2016] [Indexed: 12/21/2022]
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14
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Roca J, Cano I, Gomez-Cabrero D, Tegnér J. From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD. Methods Mol Biol 2016; 1386:283-303. [PMID: 26677188 DOI: 10.1007/978-1-4939-3283-2_13] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.
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Affiliation(s)
- Josep Roca
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands.
| | - Isaac Cano
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. .,L8:05 Karolinska University Hospital, Stockholm, 17176, Sweden.
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15
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Hofmann-Apitius M, Ball G, Gebel S, Bagewadi S, de Bono B, Schneider R, Page M, Kodamullil AT, Younesi E, Ebeling C, Tegnér J, Canard L. Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. Int J Mol Sci 2015; 16:29179-206. [PMID: 26690135 PMCID: PMC4691095 DOI: 10.3390/ijms161226148] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 11/10/2015] [Accepted: 11/12/2015] [Indexed: 12/22/2022] Open
Abstract
Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
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Affiliation(s)
- Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Gordon Ball
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Shweta Bagewadi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Bernard de Bono
- Institute of Health Informatics, University College London, London NW1 2DA, UK.
- Auckland Bioengineering Institute, University of Auckland, Symmonds Street, Auckland 1142, New Zealand.
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Matt Page
- Translational Bioinformatics, UCB Pharma, 216 Bath Rd, Slough SL1 3WE, UK.
| | - Alpha Tom Kodamullil
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Christian Ebeling
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Luc Canard
- Translational Science Unit, SANOFI Recherche & Développement, 1 Avenue Pierre Brossolette, Chilly-Mazarin Cedex 91385, France.
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16
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Dammann O, Gray P, Gressens P, Wolkenhauer O, Leviton A. Systems Epidemiology: What's in a Name? Online J Public Health Inform 2014; 6:e198. [PMID: 25598870 PMCID: PMC4292535 DOI: 10.5210/ojphi.v6i3.5571] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Systems biology is an interdisciplinary effort to integrate molecular, cellular, tissue, organ, and organism levels of function into computational models that facilitate the identification of general principles. Systems medicine adds a disease focus. Systems epidemiology adds yet another level consisting of antecedents that might contribute to the disease process in populations. In etiologic and prevention research, systems-type thinking about multiple levels of causation will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. In public health, systems epidemiology will contribute to the improvement of syndromic surveillance methods. We encourage the creation of computational simulation models that integrate information about disease etiology, pathogenetic data, and the expertise of investigators from different disciplines.
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Affiliation(s)
- O. Dammann
- Dept of Public Health and Community Medicine, Tufts
University School of Medicine, Boston, MA
- Perinatal Epidemiology Unit, Dept. of Gynecology and
Obstetrics, Hannover Medical School, Hannover, Germany
| | - P. Gray
- Dept of Public Health and Community Medicine, Tufts
University School of Medicine, Boston, MA
| | - P. Gressens
- Inserm, U676, Paris, France
- Department of Perinatal Imaging and Health,
Department of Division of Imaging Sciences and Biomedical Engineering,
King’s College London, King’s Health Partners, St. Thomas’
Hospital, London, United Kingdom
| | - O. Wolkenhauer
- Department of Systems Biology and Bioinformatics,
University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS),
Stellenbosch, South Africa
| | - A. Leviton
- Neuroepidemiology Unit, Children’s Hospital,
Boston, MA
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17
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Kotelnikova E, Bernardo-Faura M, Silberberg G, Kiani NA, Messinis D, Melas IN, Artigas L, Schwartz E, Mazo I, Masso M, Alexopoulos LG, Mas JM, Olsson T, Tegner J, Martin R, Zamora A, Paul F, Saez-Rodriguez J, Villoslada P. Signaling networks in MS: a systems-based approach to developing new pharmacological therapies. Mult Scler 2014; 21:138-46. [PMID: 25112814 DOI: 10.1177/1352458514543339] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
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Affiliation(s)
- Ekaterina Kotelnikova
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
| | | | - Gilad Silberberg
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | - Ioannis N Melas
- European Molecular Biology Laboratory, European Bioinformatics Institute, UK/ProtATonce Ltd, Greece/National Technical University of Athens, Greece
| | | | | | | | | | | | | | | | - Jesper Tegner
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | | | - Friedemann Paul
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Germany
| | | | - Pablo Villoslada
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
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18
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Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. Optimal experiment design for model selection in biochemical networks. BMC SYSTEMS BIOLOGY 2014; 8:20. [PMID: 24555498 PMCID: PMC3946009 DOI: 10.1186/1752-0509-8-20] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 02/13/2014] [Indexed: 01/06/2023]
Abstract
Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
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Affiliation(s)
- Joep Vanlier
- Eindhoven University of Technology, Department of Biomedical Engineering, PO Box 513, Eindhoven, 5600 MB, The Netherlands.
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19
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Younesi E, Hofmann-Apitius M. From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. EPMA J 2013; 4:23. [PMID: 24195840 PMCID: PMC3832251 DOI: 10.1186/1878-5085-4-23] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Accepted: 10/21/2013] [Indexed: 01/08/2023]
Abstract
With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each 'P' in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine.
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Affiliation(s)
- Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
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20
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Fluck J, Hofmann-Apitius M. Text mining for systems biology. Drug Discov Today 2013; 19:140-4. [PMID: 24070668 DOI: 10.1016/j.drudis.2013.09.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 09/05/2013] [Accepted: 09/12/2013] [Indexed: 01/08/2023]
Abstract
Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future.
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Affiliation(s)
- Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany; Bonn-Aachen International Center for Information Technology (B-IT), Dahlmannstraβe 2, 53113 Bonn, Germany.
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21
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Somvanshi PR, Venkatesh KV. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. SYSTEMS AND SYNTHETIC BIOLOGY 2013; 8:99-116. [PMID: 24592295 DOI: 10.1007/s11693-013-9125-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/10/2013] [Indexed: 12/28/2022]
Abstract
Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.
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Affiliation(s)
- Pramod Rajaram Somvanshi
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
| | - K V Venkatesh
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
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Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O. The road from systems biology to systems medicine. Pediatr Res 2013; 73:502-7. [PMID: 23314297 DOI: 10.1038/pr.2013.4] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
As research institutions prepare roadmaps for "systems medicine," we ask how this differs from applications of systems biology approaches in medicine and what we (should) have learned from about one decade of funding in systems biology. After surveying the area, we conclude that systems medicine is the logical next step and necessary extension of systems biology, and we focus on clinically relevant applications. We specifically discuss three related notions. First, more interdisciplinary collaborations are needed to face the challenges of integrating basic research and clinical practice: integration, analysis, and interpretation of clinical and nonclinical data for diagnosis, prognosis, and therapy require advanced statistical, computational, and mathematical tools. Second, strategies are required to (i) develop and maintain computational platforms for the integration of clinical and nonclinical data, (ii) further develop technologies for quantitative and time-resolved tracking of changes in gene expression, cell signaling, and metabolism in relation to environmental and lifestyle influences, and (iii) develop methodologies for mathematical and statistical analyses of integrated data sets and multilevel models. Third, interdisciplinary collaborations represent a major challenge and are difficult to implement. For an efficient and successful initiation of interdisciplinary systems medicine programs, we argue that epistemological, ontological, and sociological aspects require attention.
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Affiliation(s)
- Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
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Roberts PD, Spiros A, Geerts H. Simulations of symptomatic treatments for Alzheimer's disease: computational analysis of pathology and mechanisms of drug action. ALZHEIMERS RESEARCH & THERAPY 2012. [PMID: 23181523 PMCID: PMC3580459 DOI: 10.1186/alzrt153] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Introduction A substantial number of therapeutic drugs for Alzheimer's disease (AD) have failed in late-stage trials, highlighting the translational disconnect with pathology-based animal models. Methods To bridge the gap between preclinical animal models and clinical outcomes, we implemented a conductance-based computational model of cortical circuitry to simulate working memory as a measure for cognitive function. The model was initially calibrated using preclinical data on receptor pharmacology of catecholamine and cholinergic neurotransmitters. The pathology of AD was subsequently implemented as synaptic and neuronal loss and a decrease in cholinergic tone. The model was further calibrated with clinical Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog) results on acetylcholinesterase inhibitors and 5-HT6 antagonists to improve the model's prediction of clinical outcomes. Results As an independent validation, we reproduced clinical data for apolipoprotein E (APOE) genotypes showing that the ApoE4 genotype reduces the network performance much more in mild cognitive impairment conditions than at later stages of AD pathology. We then demonstrated the differential effect of memantine, an N-Methyl-D-aspartic acid (NMDA) subunit selective weak inhibitor, in early and late AD pathology, and show that inhibition of the NMDA receptor NR2C/NR2D subunits located on inhibitory interneurons compensates for the greater excitatory decline observed with pathology. Conclusions This quantitative systems pharmacology approach is shown to be complementary to traditional animal models, with the potential to assess potential off-target effects, the consequences of pharmacologically active human metabolites, the effect of comedications, and the impact of a small number of well described genotypes.
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Affiliation(s)
- Patrick D Roberts
- Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR 97239 USA ; In Silico Biosciences, Inc., 405 Waltham Street, Lexington, MA 02421 USA
| | - Athan Spiros
- In Silico Biosciences, Inc., 405 Waltham Street, Lexington, MA 02421 USA
| | - Hugo Geerts
- In Silico Biosciences, Inc., 405 Waltham Street, Lexington, MA 02421 USA
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Cedersund G. Conclusions via unique predictions obtained despite unidentifiability--new definitions and a general method. FEBS J 2012; 279:3513-27. [PMID: 22846178 DOI: 10.1111/j.1742-4658.2012.08725.x] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
It is often predicted that model-based data analysis will revolutionize biology, just as it has physics and engineering. A widely used tool within such analysis is hypothesis testing, which focuses on model rejections. However, the fact that a systems biology model is non-rejected is often a relatively weak statement, as such models usually are highly over-parametrized with respect to the available data, and both parameters and predictions may therefore be arbitrarily uncertain. For this reason, we formally define and analyse the concept of a core prediction. A core prediction is a uniquely identified property that must be fulfilled if the given model structure is to explain the data, even if the individual parameters are non-uniquely identified. It is shown that such a prediction is as strong a conclusion as a rejection. Furthermore, a new method for core prediction analysis is introduced, which is beneficial for the uncertainty of specific model properties, as the method only characterizes the space of acceptable parameters in the relevant directions. This avoids the curse of dimensionality associated with the generic characterizations used by previously proposed methods. Analysis on examples shows that the new method is comparable to profile likelihood with regard to practical identifiability, and thus generalizes profile likelihood to the more general problem of observability. If used, the concepts and methods presented herein make it possible to distinguish between a conclusion and a mere suggestion, which hopefully will contribute to a more justified confidence in systems biology analyses.
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Affiliation(s)
- Gunnar Cedersund
- Department of Clinical and Experimental Medicine, Linköping University, Sweden.
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Dong Y, Chbat NW, Gupta A, Hadzikadic M, Gajic O. Systems modeling and simulation applications for critical care medicine. Ann Intensive Care 2012; 2:18. [PMID: 22703718 PMCID: PMC3464892 DOI: 10.1186/2110-5820-2-18] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 06/15/2012] [Indexed: 12/27/2022] Open
Abstract
Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area.
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Affiliation(s)
- Yue Dong
- Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
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Derry JMJ, Mangravite LM, Suver C, Furia MD, Henderson D, Schildwachter X, Bot B, Izant J, Sieberts SK, Kellen MR, Friend SH. Developing predictive molecular maps of human disease through community-based modeling. Nat Genet 2012; 44:127-30. [PMID: 22281773 DOI: 10.1038/ng.1089] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Eriksson O, Andersson T, Zhou Y, Tegnér J. Decoding complex biological networks - tracing essential and modulatory parameters in complex and simplified models of the cell cycle. BMC SYSTEMS BIOLOGY 2011; 5:123. [PMID: 21819620 PMCID: PMC3176200 DOI: 10.1186/1752-0509-5-123] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2010] [Accepted: 08/07/2011] [Indexed: 12/20/2022]
Abstract
Background One of the most well described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent we can dissect the relationship between model parameters and system dynamics. Here we combine two strategies to enable an exploration of parameter space in relation to model output. A simplified, piecewise linear approximation of the original model is combined with a sensitivity analysis of the same system, to obtain and validate analytical expressions describing the dynamical role of different model parameters. Results We considered two different output responses to parameter perturbations. One was qualitative and described whether the system was still working, i.e. whether there were oscillations. We call parameters that correspond to such qualitative change in system response essential. The other response pattern was quantitative and measured changes in cell size, corresponding to perturbations of modulatory parameters. Analytical predictions from the simplified model concerning the impact of different parameters were compared to a sensitivity analysis of the original model, thus evaluating the predictions from the simplified model. The comparison showed that the predictions on essential and modulatory parameters were satisfactory for small perturbations, but more discrepancies were seen for larger perturbations. Furthermore, for this particular cell cycle model, we found that most parameters were either essential or modulatory. Essential parameters required large perturbations for identification, whereas modulatory parameters were more easily identified with small perturbations. Finally, we used the simplified model to make predictions on critical combinations of parameter perturbations. Conclusions The parameter characterizations of the simplified model are in large consistent with the original model and the simplified model can give predictions on critical combinations of parameter perturbations. We believe that the distinction between essential and modulatory perturbation responses will be of use for sensitivity analysis, and in discussions of robustness and during the model simplification process.
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Affiliation(s)
- Olivia Eriksson
- Division of Mathematical Statistics, Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden.
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Gomez-Cabrero D, Compte A, Tegner J. Workflow for generating competing hypothesis from models with parameter uncertainty. Interface Focus 2011; 1:438-49. [PMID: 22670212 DOI: 10.1098/rsfs.2011.0015] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2011] [Accepted: 03/07/2011] [Indexed: 01/07/2023] Open
Abstract
Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.
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Affiliation(s)
- David Gomez-Cabrero
- Department of Medicine, Karolinska Institutet , Unit of Computational Medicine, Centre for Molecular Medicine , Solna, Stockholm , Sweden
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