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Castagnino N, Maffei M, Tortolina L, Zoppoli G, Piras D, Nencioni A, Ballestrero A, Patrone F, Parodi S. Transcription Factors Synergistically Activated at the Crossing of the Restriction Point between G1 and S Cell Cycle Phases. Pathologic Gate Opening during Multi-Hit Malignant Transformation. NUCLEAR RECEPTOR RESEARCH 2016. [DOI: 10.11131/2016/101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nicoletta Castagnino
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Massimo Maffei
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Lorenzo Tortolina
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Gabriele Zoppoli
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Daniela Piras
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Alessio Nencioni
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Franco Patrone
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
| | - Silvio Parodi
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy; IRCCS Azienda Ospedaliera Universitaria San Martino - IST, Genoa, Italy
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Castagnino N, Maffei M, Tortolina L, Zoppoli G, Piras D, Nencioni A, Moran E, Ballestrero A, Patrone F, Parodi S. Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 8:314-36. [PMID: 27240214 PMCID: PMC6680205 DOI: 10.1002/wsbm.1342] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Revised: 03/24/2016] [Accepted: 04/06/2016] [Indexed: 12/18/2022]
Abstract
Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro‐RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1‐S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical‐trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314–336. doi: 10.1002/wsbm.1342 This article is categorized under:
Biological Mechanisms > Cell Signaling Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Translational Medicine
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Affiliation(s)
- Nicoletta Castagnino
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Massimo Maffei
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Lorenzo Tortolina
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Gabriele Zoppoli
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Daniela Piras
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Alessio Nencioni
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Eva Moran
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Franco Patrone
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
| | - Silvio Parodi
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.,IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
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Parodi S, Riccardi G, Castagnino N, Tortolina L, Maffei M, Zoppoli G, Nencioni A, Ballestrero A, Patrone F. Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems. Methods Mol Biol 2016; 1386:181-219. [PMID: 26677185 DOI: 10.1007/978-1-4939-3283-2_10] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.
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Affiliation(s)
- Silvio Parodi
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
| | - Giuseppe Riccardi
- Signals and Interactive Systems lab, Department of Engineering and Information Science, Trento University, Trento, Italy
| | - Nicoletta Castagnino
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Lorenzo Tortolina
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Massimo Maffei
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Gabriele Zoppoli
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Alessio Nencioni
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
| | - Franco Patrone
- Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy
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Tortolina L, Duffy DJ, Maffei M, Castagnino N, Carmody AM, Kolch W, Kholodenko BN, Ambrosi CD, Barla A, Biganzoli EM, Nencioni A, Patrone F, Ballestrero A, Zoppoli G, Verri A, Parodi S. Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies. Oncotarget 2015; 6:5041-58. [PMID: 25671297 PMCID: PMC4467132 DOI: 10.18632/oncotarget.3238] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 12/28/2014] [Indexed: 12/22/2022] Open
Abstract
The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.
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Affiliation(s)
- Lorenzo Tortolina
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy
| | - David J. Duffy
- Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Massimo Maffei
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
| | - Nicoletta Castagnino
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
| | - Aimée M. Carmody
- Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Boris N. Kholodenko
- Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Cristina De Ambrosi
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy
| | - Annalisa Barla
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy
| | - Elia M. Biganzoli
- Unit of Medical Statistics, Biometry and Bioinformatics “Giulio A. Maccacaro”, Department of Clinical Sciences and Community Health, University of Milan, Italy
| | - Alessio Nencioni
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
- Istituto a Carattere di Ricerca Clinic - Scientifico (IRCCS), Azienda Ospedaliera Universitaria San Martino, Istituto Nazionale Tumori (IST), Genoa, Italy
| | - Franco Patrone
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
- Istituto a Carattere di Ricerca Clinic - Scientifico (IRCCS), Azienda Ospedaliera Universitaria San Martino, Istituto Nazionale Tumori (IST), Genoa, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
- Istituto a Carattere di Ricerca Clinic - Scientifico (IRCCS), Azienda Ospedaliera Universitaria San Martino, Istituto Nazionale Tumori (IST), Genoa, Italy
| | - Gabriele Zoppoli
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
- Istituto a Carattere di Ricerca Clinic - Scientifico (IRCCS), Azienda Ospedaliera Universitaria San Martino, Istituto Nazionale Tumori (IST), Genoa, Italy
| | - Alessandro Verri
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy
| | - Silvio Parodi
- Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
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De Ambrosi C, Barla A, Tortolina L, Castagnino N, Pesenti R, Verri A, Ballestrero A, Patrone F, Parodi S. Parameter space exploration within dynamic simulations of signaling networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2013; 10:103-120. [PMID: 23311364 DOI: 10.3934/mbe.2013.10.103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.
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Affiliation(s)
- Cristina De Ambrosi
- DIBRIS Department of Informatics, Bioengineering, Robotics and Systems Engineering, Universita degli Studi di Genova - Via Balbi, 5 - 16126 Genova, Italy.
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Luna A, Karac EI, Sunshine M, Chang L, Nussinov R, Aladjem MI, Kohn KW. A formal MIM specification and tools for the common exchange of MIM diagrams: an XML-Based format, an API, and a validation method. BMC Bioinformatics 2011; 12:167. [PMID: 21586134 PMCID: PMC3118169 DOI: 10.1186/1471-2105-12-167] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Accepted: 05/17/2011] [Indexed: 01/15/2023] Open
Abstract
Background The Molecular Interaction Map (MIM) notation offers a standard set of symbols and rules on their usage for the depiction of cellular signaling network diagrams. Such diagrams are essential for disseminating biological information in a concise manner. A lack of software tools for the notation restricts wider usage of the notation. Development of software is facilitated by a more detailed specification regarding software requirements than has previously existed for the MIM notation. Results A formal implementation of the MIM notation was developed based on a core set of previously defined glyphs. This implementation provides a detailed specification of the properties of the elements of the MIM notation. Building upon this specification, a machine-readable format is provided as a standardized mechanism for the storage and exchange of MIM diagrams. This new format is accompanied by a Java-based application programming interface to help software developers to integrate MIM support into software projects. A validation mechanism is also provided to determine whether MIM datasets are in accordance with syntax rules provided by the new specification. Conclusions The work presented here provides key foundational components to promote software development for the MIM notation. These components will speed up the development of interoperable tools supporting the MIM notation and will aid in the translation of data stored in MIM diagrams to other standardized formats. Several projects utilizing this implementation of the notation are outlined herein. The MIM specification is available as an additional file to this publication. Source code, libraries, documentation, and examples are available at http://discover.nci.nih.gov/mim.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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