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Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
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Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
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Calzone L, Noël V, Barillot E, Kroemer G, Stoll G. Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells. Comput Struct Biotechnol J 2022; 20:5661-5671. [PMID: 36284705 PMCID: PMC9582792 DOI: 10.1016/j.csbj.2022.10.003] [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: 07/05/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.
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Affiliation(s)
- Laurence Calzone
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France,Corresponding authors.
| | - Vincent Noël
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France,INSERM, U900, F-75005 Paris, France,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Europén Georges Pompidou, AP-HP, Paris, France
| | - Gautier Stoll
- Centre de Recherche des Cordeliers, Equipe labellisé par la Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France,Corresponding authors.
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Hérault L, Poplineau M, Remy E, Duprez E. Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging. Cells 2022; 11:cells11193125. [PMID: 36231086 PMCID: PMC9563410 DOI: 10.3390/cells11193125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/15/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.
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Affiliation(s)
- Léonard Hérault
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
| | - Elisabeth Remy
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
- Correspondence:
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Xiao W, Yuan Y, Wang S, Liao Z, Cai P, Chen B, Zhang R, Wang F, Zeng Z, Gao Y. Neoadjuvant PD-1 Blockade Combined With Chemotherapy Followed by Concurrent Immunoradiotherapy in Locally Advanced Anal Canal Squamous Cell Carcinoma Patients: Antitumor Efficacy, Safety and Biomarker Analysis. Front Immunol 2022; 12:798451. [PMID: 35095878 PMCID: PMC8794813 DOI: 10.3389/fimmu.2021.798451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 11/17/2022] Open
Abstract
Background Anal canal squamous cell carcinoma (ACSCC) is an exceedingly rare malignant neoplasm with challenges in sphincter preservation, treatment toxicities and long-term survival. Little is known concerning the activity of PD-1 antibodies in locally advanced ACSCC. This study reports on the efficacy and toxicities of a neoadjuvant PD-1 blockade combined with chemotherapy followed by concurrent immunoradiotherapy in ACSCC patients, and describes biomarkers expression and mutation signatures. Methods In this cohort study, patients were treated as planned, including four cycles of neoadjuvant PD-1 antibody toripalimab combined with docetaxol and cisplatin, followed by radiotherapy and two cycles of concurrent toripalimab. Multiplex immunofluorescence staining (mIHC) with PD-L1, CD8, CD163, Pan-Keratin and DAPI was performed with the pretreatment tumor tissue. Whole exome sequencing was performed for the primary tumor and peripheral blood mononuclear cells. The primary endpoint was the complete clinical response (cCR) rate at 3 months after overall treatment. Acute and late toxicities graded were assessed prospectively. Results Five female patients with a median age of 50 years old (range, 43-65 years old), finished treatment as planned. One patient had grade 3 immune related dermatitis. Two patients had grade 3 myelosuppression during neoadjuvant treatment. No severe radiation-related toxicities were noted. Four patients with PD-L1 expression >1% achieved a cCR after neoadjuvant treatment. and the other patient with negative PD-L1 expression also achieved a cCR at 3 months after radiotherapy. All the patients were alive and free from disease and had a normal quality of life, with 19.6-24 months follow up. Inconsistent expression of PD-L1 and CD163 was detected in 3 and 5 patients, respectively. TTN, POLE, MGAM2 were the top mutation frequencies, and 80 significant driver genes were identified. Pathway analysis showed enrichment of apoptosis, Rap1, Ras, and pathways in cancer signaling pathways. Eight significantly deleted regions were identified. Conclusions This small cohort of locally advanced ACSCC patients had quite satisfactory cCR and sphincter preservation rate, after neoadjuvant PD-1 antibody toripalimab combined with chemotherapy followed by concurrent immunoradiotherapy, with mild acute and long-term toxicities.
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Affiliation(s)
- WeiWei Xiao
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yan Yuan
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - SuiHai Wang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Zhidong Liao
- Department of Pathology, Meizhou Hospital of Traditional Chinese Medicine, Meizhou, China
| | - PeiQiang Cai
- Departments of Medical Imaging and Interventional Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - BaoQing Chen
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- Department of Endoscopy, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Fang Wang
- Department of Molecular Diagnosis, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - ZhiFan Zeng
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - YuanHong Gao
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
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Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
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Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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Werle SD, Schwab JD, Tatura M, Kirchhoff S, Szekely R, Diels R, Ikonomi N, Sipos B, Sperveslage J, Gress TM, Buchholz M, Kestler HA. Unraveling the Molecular Tumor-Promoting Regulation of Cofilin-1 in Pancreatic Cancer. Cancers (Basel) 2021; 13:725. [PMID: 33578795 PMCID: PMC7916621 DOI: 10.3390/cancers13040725] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/26/2021] [Accepted: 02/07/2021] [Indexed: 12/24/2022] Open
Abstract
Cofilin-1 (CFL1) overexpression in pancreatic cancer correlates with high invasiveness and shorter survival. Besides a well-documented role in actin remodeling, additional cellular functions of CFL1 remain poorly understood. Here, we unraveled molecular tumor-promoting functions of CFL1 in pancreatic cancer. For this purpose, we first show that a knockdown of CFL1 results in reduced growth and proliferation rates in vitro and in vivo, while apoptosis is not induced. By mechanistic modeling we were able to predict the underlying regulation. Model simulations indicate that an imbalance in actin remodeling induces overexpression and activation of CFL1 by acting on transcription factor 7-like 2 (TCF7L2) and aurora kinase A (AURKA). Moreover, we could predict that CFL1 impacts proliferation and apoptosis via the signal transducer and activator of transcription 3 (STAT3). These initial model-based regulations could be substantiated by studying protein levels in pancreatic cancer cell lines and human datasets. Finally, we identified the surface protein CD44 as a promising therapeutic target for pancreatic cancer patients with high CFL1 expression.
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Affiliation(s)
- Silke D. Werle
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Julian D. Schwab
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Marina Tatura
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Sandra Kirchhoff
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Robin Szekely
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Ramona Diels
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
| | - Bence Sipos
- Institute of Pathology, University of Tübingen, 72076 Tübingen, Germany; (B.S.); (J.S.)
| | - Jan Sperveslage
- Institute of Pathology, University of Tübingen, 72076 Tübingen, Germany; (B.S.); (J.S.)
| | - Thomas M. Gress
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Malte Buchholz
- Department of Gastroenterology, Endocrinology and Metabolism, Philipps-University Marburg, 35043 Marburg, Germany; (M.T.); (S.K.); (R.D.); (T.M.G.); (M.B.)
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany; (S.D.W.); (J.D.S.); (R.S.); (N.I.)
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