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Goekoop R, de Kleijn R. Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics. Neurosci Biobehav Rev 2023; 154:105402. [PMID: 37741517 DOI: 10.1016/j.neubiorev.2023.105402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 09/25/2023]
Abstract
Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.
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Affiliation(s)
- R Goekoop
- Free University Amsterdam, Department of Behavioral and Movement Sciences, Parnassia Academy, Parnassia Group, PsyQ, Department of Anxiety Disorders, Early Detection and Intervention Team (EDIT), Lijnbaan 4, 2512VA The Hague, the Netherlands.
| | - R de Kleijn
- Faculty of Social and Behavioral Sciences, Department of Cognitive Psychology, Pieter de la Courtgebouw, Postbus 9555, 2300 RB Leiden, the Netherlands
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2
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Marqués-Sánchez P, Martínez-Fernández MC, Leirós-Rodríguez R, Rodríguez-Nogueira Ó, Fernández-Martínez E, Benítez-Andrades JA. Leadership and contagion by COVID-19 among residence hall students: A social network analysis approach. SOCIAL NETWORKS 2023; 73:80-88. [PMID: 36628334 PMCID: PMC9816079 DOI: 10.1016/j.socnet.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
University students have changed their behaviour due to the COVID-19 pandemic. In this paper, we describe the characteristics of PCR+ and PCR- nodes, analyse the structure, and relate the structure of student leaders to pandemic contagion as determined by PCR+ in 93 residential university students. Leadership comes from the male students of social science degrees who have PCR +, with an eigenvector centrality structure, β-centrality, and who are part of the bow-tie structure. There was a significant difference in β-centrality between leaders and non-leaders and in β-centrality between PCR+ and non-leaders. Leading nodes were part of the bow-tie structure. MR-QAP results show how residence and scientific branch were the most important factors in network formation. Therefore, university leaders should consider influential leaders, as they are vectors for disseminating both positive and negative outcomes.
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Affiliation(s)
- Pilar Marqués-Sánchez
- SALBIS Research Group, Faculty of Health Sciences, University of León, Campus of Ponferrada, 24401 Ponferrada, Spain
| | | | - Raquel Leirós-Rodríguez
- SALBIS Research Group, Faculty of Health Sciences, University of León, Campus of Ponferrada, 24401 Ponferrada, Spain
| | - Óscar Rodríguez-Nogueira
- SALBIS Research Group, Faculty of Health Sciences, University of León, Campus of Ponferrada, 24401 Ponferrada, Spain
| | - Elena Fernández-Martínez
- SALBIS Research Group, Faculty of Health Sciences, University of León, Campus of Vegazana s/n, 24071 León, Spain
| | - José Alberto Benítez-Andrades
- SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, Spain
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3
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Mosbacher M, Lee SS, Yaakov G, Nadal-Ribelles M, de Nadal E, van Drogen F, Posas F, Peter M, Claassen M. Positive feedback induces switch between distributive and processive phosphorylation of Hog1. Nat Commun 2023; 14:2477. [PMID: 37120434 PMCID: PMC10148820 DOI: 10.1038/s41467-023-37430-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 03/16/2023] [Indexed: 05/01/2023] Open
Abstract
Cellular decision making often builds on ultrasensitive MAPK pathways. The phosphorylation mechanism of MAP kinase has so far been described as either distributive or processive, with distributive mechanisms generating ultrasensitivity in theoretical analyses. However, the in vivo mechanism of MAP kinase phosphorylation and its activation dynamics remain unclear. Here, we characterize the regulation of the MAP kinase Hog1 in Saccharomyces cerevisiae via topologically different ODE models, parameterized on multimodal activation data. Interestingly, our best fitting model switches between distributive and processive phosphorylation behavior regulated via a positive feedback loop composed of an affinity and a catalytic component targeting the MAP kinase-kinase Pbs2. Indeed, we show that Hog1 directly phosphorylates Pbs2 on serine 248 (S248), that cells expressing a non-phosphorylatable (S248A) or phosphomimetic (S248E) mutant show behavior that is consistent with simulations of disrupted or constitutively active affinity feedback and that Pbs2-S248E shows significantly increased affinity to Hog1 in vitro. Simulations further suggest that this mixed Hog1 activation mechanism is required for full sensitivity to stimuli and to ensure robustness to different perturbations.
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Affiliation(s)
- Maximilian Mosbacher
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Sung Sik Lee
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
- Scientific Center for Optical and Electron Microscopy, ETH Zurich, Zurich, Switzerland
| | - Gilad Yaakov
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Mariona Nadal-Ribelles
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
| | - Eulàlia de Nadal
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
| | - Frank van Drogen
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
| | - Francesc Posas
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
| | - Matthias Peter
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
| | - Manfred Claassen
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Department of Computer Science, University of Tübingen, Tübingen, Germany.
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
- Department of Internal Medicine I, Faculty of Medicine, University Hospital Tübingen, University of Tübingen, Tübingen, Germany.
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4
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Hilliard S, Mosoyan K, Branciamore S, Gogoshin G, Zhang A, Simons DL, Rockne RC, Lee PP, Rodin AS. Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design. iScience 2023; 26:106041. [PMID: 36818303 PMCID: PMC9929672 DOI: 10.1016/j.isci.2023.106041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/09/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.
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Affiliation(s)
- Seth Hilliard
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Karen Mosoyan
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Sergio Branciamore
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Grigoriy Gogoshin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Alvin Zhang
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Diana L. Simons
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Peter P. Lee
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Andrei S. Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
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5
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French-Pacheco L, Rosas-Bringas O, Segovia L, Covarrubias AA. Intrinsically disordered signaling proteins: Essential hub players in the control of stress responses in Saccharomyces cerevisiae. PLoS One 2022; 17:e0265422. [PMID: 35290420 PMCID: PMC8923507 DOI: 10.1371/journal.pone.0265422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 03/01/2022] [Indexed: 11/24/2022] Open
Abstract
Cells have developed diverse mechanisms to monitor changes in their surroundings. This allows them to establish effective responses to cope with adverse environments. Some of these mechanisms have been well characterized in the budding yeast Saccharomyces cerevisiae, an excellent experimental model to explore and elucidate some of the strategies selected in eukaryotic organisms to adjust their growth and development in stressful conditions. The relevance of structural disorder in proteins and the impact on their functions has been uncovered for proteins participating in different processes. This is the case of some transcription factors (TFs) and other signaling hub proteins, where intrinsically disordered regions (IDRs) play a critical role in their function. In this work, we present a comprehensive bioinformatic analysis to evaluate the significance of structural disorder in those TFs (170) recognized in S. cerevisiae. Our findings show that 85.2% of these TFs contain at least one IDR, whereas ~30% exhibit a higher disorder level and thus were considered as intrinsically disordered proteins (IDPs). We also found that TFs contain a higher number of IDRs compared to the rest of the yeast proteins, and that intrinsically disordered TFs (IDTFs) have a higher number of protein-protein interactions than those with low structural disorder. The analysis of different stress response pathways showed a high content of structural disorder not only in TFs but also in other signaling proteins. The propensity of yeast proteome to undergo a liquid-liquid phase separation (LLPS) was also analyzed, showing that a significant proportion of IDTFs may undergo this phenomenon. Our analysis is a starting point for future research on the importance of structural disorder in yeast stress responses.
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Affiliation(s)
- Leidys French-Pacheco
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Omar Rosas-Bringas
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Lorenzo Segovia
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Alejandra A. Covarrubias
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
- * E-mail:
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6
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Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol 2021; 40:291-305. [PMID: 34303549 DOI: 10.1016/j.tibtech.2021.06.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 12/21/2022]
Abstract
The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.
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7
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Jaemthaworn T, Kalapanulak S, Saithong T. Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava. Sci Rep 2021; 11:7872. [PMID: 33846415 PMCID: PMC8041763 DOI: 10.1038/s41598-021-86806-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 03/19/2021] [Indexed: 02/01/2023] Open
Abstract
Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.
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Affiliation(s)
- Thanakorn Jaemthaworn
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
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8
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Cararo-Lopes E, Dias MH, da Silva MS, Zeidler JD, Vessoni AT, Reis MS, Boccardo E, Armelin HA. Autophagy buffers Ras-induced genotoxic stress enabling malignant transformation in keratinocytes primed by human papillomavirus. Cell Death Dis 2021; 12:194. [PMID: 33602932 PMCID: PMC7892846 DOI: 10.1038/s41419-021-03476-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 01/31/2023]
Abstract
Malignant transformation involves an orchestrated rearrangement of cell cycle regulation mechanisms that must balance autonomic mitogenic impulses and deleterious oncogenic stress. Human papillomavirus (HPV) infection is highly prevalent in populations around the globe, whereas the incidence of cervical cancer is 0.15%. Since HPV infection primes cervical keratinocytes to undergo malignant transformation, we can assume that the balance between transforming mitogenic signals and oncogenic stress is rarely attained. We showed that highly transforming mitogenic signals triggered by HRasG12V activity in E6E7-HPV-keratinocytes generate strong replication and oxidative stresses. These stresses are counteracted by autophagy induction that buffers the rapid increase of ROS that is the main cause of genotoxic stress promoted by the oncoprotein. As a result, autophagy creates a narrow window of opportunity for malignant keratinocytes to emerge. This work shows that autophagy is crucial to allow the transition of E6E7 keratinocytes from an immortalized to a malignant state caused by HRasG12V.
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Affiliation(s)
- Eduardo Cararo-Lopes
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil.
- Department of Biochemistry, Instituto de Química, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil.
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, 08901, USA.
| | - Matheus H Dias
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil
| | - Marcelo S da Silva
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil
- Department of Chemical and Biological Sciences, Instituto de Biociência, Universidade do Estado de São Paulo, Botucatu, SP, 18618-689, Brazil
| | - Julianna D Zeidler
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil
- Kogod Aging Center, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Alexandre T Vessoni
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Marcelo S Reis
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil
| | - Enrique Boccardo
- Department of Microbiology, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP, 05508-900, Brazil
| | - Hugo A Armelin
- Center of Toxins, Immune-response and Cell Signaling, Instituto Butantan, São Paulo, SP, 05503-900, Brazil.
- Department of Biochemistry, Instituto de Química, Universidade de São Paulo, São Paulo, SP, 05508-000, Brazil.
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9
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Haggerty RA, Purvis JE. Inferring the structures of signaling motifs from paired dynamic traces of single cells. PLoS Comput Biol 2021; 17:e1008657. [PMID: 33539338 PMCID: PMC7889133 DOI: 10.1371/journal.pcbi.1008657] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 02/17/2021] [Accepted: 12/26/2020] [Indexed: 11/18/2022] Open
Abstract
Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell's upstream signal was randomly paired with another cell's downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.
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Affiliation(s)
- Raymond A. Haggerty
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Computational Medicine Program, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Curriculum for Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jeremy E. Purvis
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Computational Medicine Program, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Curriculum for Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, United States of America
- * E-mail:
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10
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Romers J, Thieme S, Münzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl 2020; 6:2. [PMID: 31934349 PMCID: PMC6954118 DOI: 10.1038/s41540-019-0120-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/20/2019] [Indexed: 11/09/2022] Open
Abstract
The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.
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Affiliation(s)
- Jesper Romers
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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11
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Singh V, Kalliolias GD, Ostaszewski M, Veyssiere M, Pilalis E, Gawron P, Mazein A, Bonnet E, Petit-Teixeira E, Niarakis A. RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis. Database (Oxford) 2020; 2020:baaa017. [PMID: 32311035 PMCID: PMC7170216 DOI: 10.1093/database/baaa017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 01/21/2020] [Accepted: 02/13/2020] [Indexed: 02/07/2023]
Abstract
Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.
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Affiliation(s)
- Vidisha Singh
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - George D Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
- Weill Cornell Medical Center, Weill Department of Medicine, 525 East 68th Street, New York, NY 10065, USA
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Maëva Veyssiere
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Eleftherios Pilalis
- eNIOS Applications P.C., R&D department, Alexandrou Pantou 25, 17671, Kallithea-Athens, Greece
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, 2 rue Gaston Crémieux, CP5706 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Elisabeth Petit-Teixeira
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Anna Niarakis
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
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12
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Davison SA, den Haan R, van Zyl WH. Identification of superior cellulase secretion phenotypes in haploids derived from natural Saccharomyces cerevisiae isolates. FEMS Yeast Res 2019; 19:5154912. [PMID: 30388213 DOI: 10.1093/femsyr/foy117] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/31/2018] [Indexed: 01/11/2023] Open
Abstract
The yeast Saccharomyces cerevisiae is considered an important host for consolidated bioprocessing and the production of high titres of recombinant cellulases is required for efficient hydrolysis of lignocellulosic substrates to fermentable sugars. Since recombinant protein secretion profiles vary highly among different strain backgrounds, careful selection of robust strains with optimal secretion profiles is of crucial importance. Here, we construct and screen sets of haploid derivatives, derived from natural strain isolates YI13, FINI and YI59, for improved general cellulase secretion. This report details a novel approach that combines secretion profiles of strains and phenotypic responses to stresses known to influence the secretion pathway for the development of a phenotypic screen to isolate strains with improved secretory capacities. A clear distinction was observed between the YI13 haploid derivatives and industrial and laboratory counterparts, Ethanol Red and S288c, respectively. By using sub-lethal concentrations of the secretion stressor tunicamycin and cell wall stressor Congo Red, YI13 haploid derivative strains demonstrated tolerance profiles related to their heterologous secretion profiles. Our results demonstrated that a new screening technique combined with a targeted mating approach could produce a pool of novel strains capable of high cellulase secretion.
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Affiliation(s)
- Steffi A Davison
- Department of Microbiology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
| | - Riaan den Haan
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa
| | - Willem Heber van Zyl
- Department of Microbiology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
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13
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Berchtold E, Csaba G, Zimmer R. YESdb: integrative analysis of environmental stress in yeast. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5367260. [PMID: 30821814 PMCID: PMC6396637 DOI: 10.1093/database/baz023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 01/30/2019] [Accepted: 01/31/2019] [Indexed: 01/22/2023]
Abstract
The stress response in the model organisms Saccharomyces cerevisiae is a well-studied system for which many data sets are available. Already in 2000, it was discovered that yeast cells trigger a similar transcriptional response when different types of stress are applied. However, the exact regulatory mechanisms and differences between the different types of stress are still not understood. Here, we present the Yeast Environmental Stress database (YESdb), a database containing all high-throughput experiments measuring various kinds of stress in yeast. The goal of the database is to allow the user to execute complex, integrative analyses of selected data sets, e.g. the comparison of measurements of the same stress using different platforms or differences between strains, stress strengths or types of stress. The analyses can be visualized in various ways and can be compiled into interactive reports to summarize and communicate the results. The data sets are available as differential conditions (typically stressed vs control), which are grouped to time or concentration series when multiple measurements over time or concentrations are done in one experiment. An annotation ontology has been constructed to annotate the data sets with the type, duration and strength of the applied stress, the used strain and experimental platform as well as the publication date. These annotations can easily be combined to select all relevant data sets for an analysis. YESdb allows to construct and execute Petri net-based workflows to perform predefined and custom analyses. E.g. to compare two types of stress (e.g. salt vs oxidative stress), the corresponding data sets are selected from the database, the consistently changed genes are defined and combined and the shared genes are characterized by enrichment analysis. A broad collection of visualizations is available most of which are also interactive. The results of all analyses can be summarized in an interactive report. Visualizations of individual steps (transitions) of YESdb workflows can be automatically added to this report or customized visualizations as well as interpretive text can manually be added to the report. Overall, YESdb aims at making all published data sets on yeast stress immediately available and comparable for integrated analysis of data sets and sets of genes in order to identify and assess hypotheses and mechanisms.
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Affiliation(s)
- Evi Berchtold
- Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstraße, München, Germany
| | - Gergely Csaba
- Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstraße, München, Germany
| | - Ralf Zimmer
- Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstraße, München, Germany
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14
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Zhang X. Altering Indispensable Proteins in Controlling Directed Human Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:2074-2078. [PMID: 29994604 DOI: 10.1109/tcbb.2018.2796572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The numerous interconnections within complex systems enable us to control networks towards a desired state through a few suitable selected nodes, which are called driver nodes. Recent works analyzed directed human Protein-Protein Interaction (PPI) network based on structural control theory. They found that indispensable proteins, whose removal increase the number of driver nodes, are the primary targets of human viruses and drugs. However, the human PPI network is usually incomplete and may include many false-positive or false-negative interactions. That prompts us to ask whether these indispensable proteins are stable to possible structural changes. Here, we present a method to alter the type of indispensable proteins and thereby investigate the stability of indispensable proteins. By comparing the sets of indispensable proteins before and after structural changes to the network, we find that very few added or removed interactions can change the type of many indispensable nodes. Furthermore, some indispensable proteins are very sensitive to structural changes and have significantly lower interactions than the other indispensable proteins. The results indicate that indispensable proteins are sensitive to structural changes. Therefore, approaches based on structural control theory should be used with caution because of the incomplete nature of these networks.
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15
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Köksal AS, Beck K, Cronin DR, McKenna A, Camp ND, Srivastava S, MacGilvray ME, Bodík R, Wolf-Yadlin A, Fraenkel E, Fisher J, Gitter A. Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data. Cell Rep 2018; 24:3607-3618. [PMID: 30257219 PMCID: PMC6295338 DOI: 10.1016/j.celrep.2018.08.085] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 04/16/2018] [Accepted: 08/29/2018] [Indexed: 12/25/2022] Open
Abstract
We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.
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Affiliation(s)
- Ali Sinan Köksal
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Kirsten Beck
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Dylan R Cronin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, USA
| | - Aaron McKenna
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nathan D Camp
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Saurabh Srivastava
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | | | - Rastislav Bodík
- Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | | | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jasmin Fisher
- Microsoft Research, Cambridge, UK; Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Morgridge Institute for Research, Madison, WI, USA.
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16
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Li M, Gao H, Wang J, Wu FX. Control principles for complex biological networks. Brief Bioinform 2018; 20:2253-2266. [DOI: 10.1093/bib/bby088] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 08/16/2018] [Accepted: 08/18/2018] [Indexed: 11/13/2022] Open
Abstract
Abstract
Networks have been widely used to model the structure of various biological systems. Currently, a series of approaches have been developed to construct reliable biological networks. However, the ultimate understanding of a biological system is to steer its states to the desired ones by imposing signals. The control process is dominated by the intrinsic structure and the dynamic propagation. To understand the underlying mechanisms behind the life process, the control theory can be applied to biological networks with specific target requirements. In this article, we first introduce the structural controllability of complex networks and discuss its advantages and disadvantages. Then, we review the effective control to meet the specific requirements for complex biological networks. Moreover, we summarize the existing methods for finding the unique minimum set of driver nodes via the optimal control for complex networks. Finally, we discuss the relationships between biological networks and structural controllability, effective control and optimal control. Moreover, potential applications of general control principles are pointed out.
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Affiliation(s)
- Min Li
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Hao Gao
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Fang-Xiang Wu
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada
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17
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Khaluf Y, Ferrante E, Simoens P, Huepe C. Scale invariance in natural and artificial collective systems: a review. J R Soc Interface 2018; 14:rsif.2017.0662. [PMID: 29093130 DOI: 10.1098/rsif.2017.0662] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 10/09/2017] [Indexed: 01/10/2023] Open
Abstract
Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties.
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Affiliation(s)
- Yara Khaluf
- Ghent University-imec, IDLab-INTEC, Technologiepark 15, 9052 Gent, Belgium
| | - Eliseo Ferrante
- KU Leuven, Laboratory of Socioecology and Social Evolution, Naamsestraat 59, 3000 Leuven, Belgium
| | - Pieter Simoens
- Ghent University-imec, IDLab-INTEC, Technologiepark 15, 9052 Gent, Belgium
| | - Cristián Huepe
- CHuepe Labs, 814 W 19th Street 1F, Chicago, IL 60608, USA.,Northwestern Institute on Complex Systems & ESAM, Northwestern University, Evanston, IL 60208, USA
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18
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Tsutsui Y, Hays FA. A Link Between Alzheimer's and Type II Diabetes Mellitus? Ca +2 -Mediated Signal Control and Protein Localization. Bioessays 2018; 40:e1700219. [PMID: 29694668 PMCID: PMC6166406 DOI: 10.1002/bies.201700219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 03/16/2018] [Indexed: 01/28/2023]
Abstract
We propose protein localization dependent signal activation (PLDSA) as a model to describe pre-existing protein partitioning between the cytosol, and membrane surface, as a means to modulate signal activation, specificity, and robustness. We apply PLDSA to explain possible molecular links between type II diabetes mellitus (T2DM) and Alzheimer's disease (AD) by describing Ca+2 -mediated interactions between the Src non-receptor tyrosine kinase and p52Shc adaptor protein. We suggest that these interactions may serve as a contributing factor to disease development and progression. In particular, we propose that signaling response is regulated, in part, by Ca+2 -mediated partitioning of lipid-bound and soluble forms of Src and p52shc. Thus, protein-protein interactions that drive signaling in response to extracellular ligand binding are also mediated by partitioning of signaling proteins between membrane-bound and soluble populations. We propose that PLDSA effects may explain, in part, the evolutionary basis of promiscuous protein interaction domains and their importance in cellular function.
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Affiliation(s)
- Yuko Tsutsui
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, United States
| | - Franklin A. Hays
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, United States
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73104, United States
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73104, United States
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19
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Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW). NPJ Syst Biol Appl 2017; 3:26. [PMID: 28944079 PMCID: PMC5605530 DOI: 10.1038/s41540-017-0028-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 08/18/2017] [Accepted: 08/24/2017] [Indexed: 12/11/2022] Open
Abstract
Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.
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20
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Zhang X, Han J, Zhang W. An efficient algorithm for finding all possible input nodes for controlling complex networks. Sci Rep 2017; 7:10677. [PMID: 28878394 PMCID: PMC5587595 DOI: 10.1038/s41598-017-10744-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 08/14/2017] [Indexed: 11/12/2022] Open
Abstract
Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network. Finding an MIS is known to be equivalent to computing a maximum matching of the network, where the unmatched nodes constitute an MIS. However, maximum matching is often not unique for a network, and finding all possible input nodes, the union of all MISs, may provide deep insights to the controllability of the network. Here we present an efficient enumerative algorithm for the problem. The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS. The algorithm can also output a set of substituting nodes for each input node in the MIS, so that any node in the set can replace the latter. We rigorously proved the correctness of the new algorithm and evaluated its performance on synthetic and large real networks. The experimental results showed that the new algorithm ran several orders of magnitude faster than an existing method on large real networks.
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Affiliation(s)
- Xizhe Zhang
- Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang, China. .,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Jianfei Han
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Weixiong Zhang
- College of Math and Computer Science, Institute for Systems Biology, Jianghan University, Wuhan, 430056, China.,Department of Computer Science and Engineering, Washington University, Saint Louis, Missouri, USA
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21
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Abstract
What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.
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