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Lai X, Keller C, Santos G, Schaft N, Dörrie J, Vera J. Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization. Front Cell Dev Biol 2022; 9:746359. [PMID: 35186943 PMCID: PMC8847669 DOI: 10.3389/fcell.2021.746359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/23/2021] [Indexed: 01/18/2023] Open
Abstract
Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy – the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs’ maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for the activation of key molecular circuits within DCs, the bio-distribution of DCs in the body, and the interaction between DCs and T cells. We showed how such a data-driven model can be exploited in combination with sensitivity analysis and model simulations to identify targets for enhancing anti-cancer DC vaccination. Since other previous works show how modeling improves therapy schedules and DC dosage, we here focused on the molecular optimization of the therapy. In line with this, we simulated the effect in DC vaccination of the concerted modulation of combined intracellular regulatory processes and proposed several possibilities that can enhance DC-mediated immunogenicity. Taken together, we present a comprehensive time-resolved multi-level model for studying DC vaccination in melanoma. Although the model is not intended for personalized patient therapy, it could be used as a tool for identifying molecular targets for optimizing DC-based therapy for cancer, which ultimately should be tested in in vitro and in vivo experiments.
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Affiliation(s)
- Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- *Correspondence: Xin Lai, ; Julio Vera,
| | - Christine Keller
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Guido Santos
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Departament of Biochemistry, Microbiology, Cell Biology and Genetics, Faculty of Sciences, University of La Laguna, San Cristóbal de La Laguna, Spain
| | - Niels Schaft
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- RNA Group, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jan Dörrie
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- RNA Group, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- *Correspondence: Xin Lai, ; Julio Vera,
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2
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Logotheti S, Richter C, Murr N, Spitschak A, Marquardt S, Pützer BM. Mechanisms of Functional Pleiotropy of p73 in Cancer and Beyond. Front Cell Dev Biol 2021; 9:737735. [PMID: 34650986 PMCID: PMC8506118 DOI: 10.3389/fcell.2021.737735] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/10/2021] [Indexed: 01/21/2023] Open
Abstract
The transcription factor p73 is a structural and functional homolog of TP53, the most famous and frequently mutated tumor-suppressor gene. The TP73 gene can synthesize an overwhelming number of isoforms via splicing events in 5′ and 3′ ends and alternative promoter usage. Although it originally came into the spotlight due to the potential of several of these isoforms to mimic p53 functions, it is now clear that TP73 has its own unique identity as a master regulator of multifaceted processes in embryonic development, tissue homeostasis, and cancer. This remarkable functional pleiotropy is supported by a high degree of mechanistic heterogeneity, which extends far-beyond the typical mode of action by transactivation and largely relies on the ability of p73 isoforms to form protein–protein interactions (PPIs) with a variety of nuclear and cytoplasmic proteins. Importantly, each p73 isoform carries a unique combination of functional domains and residues that facilitates the establishment of PPIs in a highly selective manner. Herein, we summarize the expanding functional repertoire of TP73 in physiological and oncogenic processes. We emphasize how TP73’s ability to control neurodevelopment and neurodifferentiation is co-opted in cancer cells toward neoneurogenesis, an emerging cancer hallmark, whereby tumors promote their own innervation. By further exploring the canonical and non-canonical mechanistic patterns of p73, we apprehend its functional diversity as the result of a sophisticated and coordinated interplay of: (a) the type of p73 isoforms (b) the presence of p73 interaction partners in the cell milieu, and (c) the architecture of target gene promoters. We suppose that dysregulation of one or more of these parameters in tumors may lead to cancer initiation and progression by reactivating p73 isoforms and/or p73-regulated differentiation programs thereof in a spatiotemporally inappropriate manner. A thorough understanding of the mechanisms supporting p73 functional diversity is of paramount importance for the efficient and precise p73 targeting not only in cancer, but also in other pathological conditions where TP73 dysregulation is causally involved.
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Affiliation(s)
- Stella Logotheti
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Christin Richter
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Nico Murr
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Alf Spitschak
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Stephan Marquardt
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany.,Department Life, Light & Matter, University of Rostock, Rostock, Germany
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3
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He Z, Sun J. Ultimate boundedness of discrete stochastic time-delay systems with logic impulses. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04054-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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4
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Naderi-Meshkin H, Lai X, Amirkhah R, Vera J, Rasko JEJ, Schmitz U. Exosomal lncRNAs and cancer: connecting the missing links. Bioinformatics 2019; 35:352-360. [PMID: 30649349 DOI: 10.1093/bioinformatics/bty527] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 06/28/2018] [Indexed: 12/13/2022] Open
Abstract
Motivation Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs). Results This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hojjat Naderi-Meshkin
- Stem Cells & Regenerative Medicine Research Group, Academic Center for Education, Culture Research (ACECR), Khorasan Razavi Branch, Mashhad, Iran.,Nastaran Center for Cancer Prevention, Mashhad, Iran
| | - Xin Lai
- Laboratory of Systems Tumour Immunology, Department of Dermatology, Friedrich-Alexander-University of Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Raheleh Amirkhah
- Nastaran Center for Cancer Prevention, Mashhad, Iran.,Reza Institute of Cancer Bioinformatics and Personalized Medicine, Mashhad, Iran
| | - Julio Vera
- Laboratory of Systems Tumour Immunology, Department of Dermatology, Friedrich-Alexander-University of Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - John E J Rasko
- Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia.,Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, Australia
| | - Ulf Schmitz
- Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, Australia.,Sydney Medical School, University of Sydney, Camperdown, Australia
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Abstract
During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.
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6
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Integrative workflows for network analysis. Essays Biochem 2018; 62:549-561. [DOI: 10.1042/ebc20180005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 09/10/2018] [Accepted: 09/14/2018] [Indexed: 01/17/2023]
Abstract
Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus–response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial–mesenchymal transition from the E2F1 molecular interaction map.
In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.
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Khan FM, Sadeghi M, Gupta SK, Wolkenhauer O. A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression. Methods Mol Biol 2018; 1702:247-276. [PMID: 29119509 DOI: 10.1007/978-1-4939-7456-6_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.
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Affiliation(s)
- Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Mehdi Sadeghi
- Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany.,Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany. .,Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India. .,Stellenbosch Institute of Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa.
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8
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Lott SC, Wolfien M, Riege K, Bagnacani A, Wolkenhauer O, Hoffmann S, Hess WR. Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments. J Biotechnol 2017; 261:85-96. [DOI: 10.1016/j.jbiotec.2017.06.1203] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 06/22/2017] [Accepted: 06/26/2017] [Indexed: 12/14/2022]
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9
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(Re-)programming of subtype specific cardiomyocytes. Adv Drug Deliv Rev 2017; 120:142-167. [PMID: 28916499 DOI: 10.1016/j.addr.2017.09.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/29/2017] [Accepted: 09/07/2017] [Indexed: 01/10/2023]
Abstract
Adult cardiomyocytes (CMs) possess a highly restricted intrinsic regenerative potential - a major barrier to the effective treatment of a range of chronic degenerative cardiac disorders characterized by cellular loss and/or irreversible dysfunction and which underlies the majority of deaths in developed countries. Both stem cell programming and direct cell reprogramming hold promise as novel, potentially curative approaches to address this therapeutic challenge. The advent of induced pluripotent stem cells (iPSCs) has introduced a second pluripotent stem cell source besides embryonic stem cells (ESCs), enabling even autologous cardiomyocyte production. In addition, the recent achievement of directly reprogramming somatic cells into cardiomyocytes is likely to become of great importance. In either case, different clinical scenarios will require the generation of highly pure, specific cardiac cellular-subtypes. In this review, we discuss these themes as related to the cardiovascular stem cell and programming field, including a focus on the emergent topic of pacemaker cell generation for the development of biological pacemakers and in vitro drug testing.
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10
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Cantone M, Santos G, Wentker P, Lai X, Vera J. Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection. Front Physiol 2017; 8:645. [PMID: 28912729 PMCID: PMC5582318 DOI: 10.3389/fphys.2017.00645] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022] Open
Abstract
Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.
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Affiliation(s)
| | | | | | | | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander University Erlangen-Nürnberg and Universitätsklinikum ErlangenErlangen, Germany
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11
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Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures. Nat Commun 2017; 8:198. [PMID: 28775339 PMCID: PMC5543083 DOI: 10.1038/s41467-017-00268-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 06/15/2017] [Indexed: 12/18/2022] Open
Abstract
Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.
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12
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Kerkhofs J, Leijten J, Bolander J, Luyten FP, Post JN, Geris L. A Qualitative Model of the Differentiation Network in Chondrocyte Maturation: A Holistic View of Chondrocyte Hypertrophy. PLoS One 2016; 11:e0162052. [PMID: 27579819 PMCID: PMC5007039 DOI: 10.1371/journal.pone.0162052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 07/18/2016] [Indexed: 01/15/2023] Open
Abstract
Differentiation of chondrocytes towards hypertrophy is a natural process whose control is essential in endochondral bone formation. It is additionally thought to play a role in several pathophysiological processes, with osteoarthritis being a prominent example. We perform a dynamic analysis of a qualitative mathematical model of the regulatory network that directs this phenotypic switch to investigate the influence of the individual factors holistically. To estimate the stability of a SOX9 positive state (associated with resting/proliferation chondrocytes) versus a RUNX2 positive one (associated with hypertrophy) we employ two measures. The robustness of the state in canalisation (size of the attractor basin) is assessed by a Monte Carlo analysis and the sensitivity to perturbations is assessed by a perturbational analysis of the attractor. Through qualitative predictions, these measures allow for an in silico screening of the effect of the modelled factors on chondrocyte maintenance and hypertrophy. We show how discrepancies between experimental data and the model’s results can be resolved by evaluating the dynamic plausibility of alternative network topologies. The findings are further supported by a literature study of proposed therapeutic targets in the case of osteoarthritis.
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Affiliation(s)
- Johan Kerkhofs
- Biomechanics Research Unit, University of Liège, Liège, Belgium
- Biomechanics section, KU Leuven, Leuven, Belgium
- Prometheus, the Leuven R&D division of skeletal tissue engineering, KU Leuven, Leuven, Belgium
| | - Jeroen Leijten
- Prometheus, the Leuven R&D division of skeletal tissue engineering, KU Leuven, Leuven, Belgium
- Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | - Johanna Bolander
- Prometheus, the Leuven R&D division of skeletal tissue engineering, KU Leuven, Leuven, Belgium
- Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | - Frank P. Luyten
- Prometheus, the Leuven R&D division of skeletal tissue engineering, KU Leuven, Leuven, Belgium
- Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | - Janine N. Post
- Developmental BioEngineering, MIRA Institute for biomedical technology and technical medicine, University of Twente, Enschede, The Netherlands
| | - Liesbet Geris
- Biomechanics Research Unit, University of Liège, Liège, Belgium
- Biomechanics section, KU Leuven, Leuven, Belgium
- Prometheus, the Leuven R&D division of skeletal tissue engineering, KU Leuven, Leuven, Belgium
- * E-mail:
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13
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Chen D, Liu X, Yang Y, Yang H, Lu P. Systematic synergy modeling: understanding drug synergy from a systems biology perspective. BMC SYSTEMS BIOLOGY 2015; 9:56. [PMID: 26377814 PMCID: PMC4574089 DOI: 10.1186/s12918-015-0202-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 08/20/2015] [Indexed: 12/24/2022]
Abstract
Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.
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Affiliation(s)
- Di Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Xi Liu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Yiping Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Peng Lu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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14
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Wallace R. Poised for survival: criticality, natural selection, and excitation-transcription coupling. Int J Biochem Cell Biol 2015; 61:1-7. [PMID: 25622558 DOI: 10.1016/j.biocel.2015.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 12/16/2014] [Accepted: 01/16/2015] [Indexed: 11/27/2022]
Abstract
Neurologically-complex species utilize two intricately coupled information-processing systems to adapt to their social and natural environments. Action potentials (APs) facilitate rapid responses to the nearly continuous fluctuations in the animal's surroundings. By contrast, genetic encodings comprise a molecular memory of ancient adaptive responses expressed as cognitive, emotional, or behavioral phenotypes. The linking of the two systems via intracellular Ca(2+) networks which address transcription factors - e.g., cAMP response element-binding protein (CREB) - is an appropriate focus for the biology of human behavior. Computational modeling utilizing Boolean networks (BNs) is a suitable qualitative method, requiring no kinetic information, for eliciting the systems' architectural properties. In particular, BNs can reveal critical intracellular regimes of possible evolutionary significance. As a platform for future research, we propose that those networks sufficiently robust to attenuate damaging intracellular noise and deleterious mutations, yet sufficiently close to chaos to permit or amplify adaptive noise and favorable mutations, would be favored by natural selection. Critical regimes of this type would be, literally, "poised for survival", and would stabilize and promote the survival of their correlated cultural phenotypes.
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Affiliation(s)
- Ron Wallace
- Department of Anthropology, University of Central Florida, PO Box 25000, Orlando, FL 32816 USA.
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15
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A systems' biology approach to study microRNA-mediated gene regulatory networks. BIOMED RESEARCH INTERNATIONAL 2013; 2013:703849. [PMID: 24350286 PMCID: PMC3848080 DOI: 10.1155/2013/703849] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 09/12/2013] [Accepted: 09/17/2013] [Indexed: 12/27/2022]
Abstract
MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.
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