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Raja R, Khanum S, Aboulmouna L, Maurya MR, Gupta S, Subramaniam S, Ramkrishna D. Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach. Biophys J 2024; 123:221-234. [PMID: 38102827 PMCID: PMC10808046 DOI: 10.1016/j.bpj.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/18/2023] [Accepted: 12/12/2023] [Indexed: 12/17/2023] Open
Abstract
Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.
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Affiliation(s)
- Rubesh Raja
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana
| | - Sana Khanum
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana
| | - Lina Aboulmouna
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Mano R Maurya
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Shakti Gupta
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Shankar Subramaniam
- Department of Bioengineering, University of California San Diego, La Jolla, California; Departments of Computer Science and Engineering, Cellular and Molecular Medicine, San Diego Supercomputer Center, and the Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, California.
| | - Doraiswami Ramkrishna
- The Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana.
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Jansen JE, Aschenbrenner D, Uhlig HH, Coles MC, Gaffney EA. A method for the inference of cytokine interaction networks. PLoS Comput Biol 2022; 18:e1010112. [PMID: 35731827 PMCID: PMC9216621 DOI: 10.1371/journal.pcbi.1010112] [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: 07/01/2021] [Accepted: 04/15/2022] [Indexed: 11/19/2022] Open
Abstract
Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs. Cytokines are the messenger molecules of the immune system, allowing intercellular communication and mediating effective immune responses. They are an important therapeutic target in immune mediated inflammatory diseases such as inflammatory bowel disease (IBD) and rheumatoid arthritis. Cytokines interact in a tightly regulated network and depending on the context a particular cytokine can be involved in anti-inflammatory or inflammatory activities. In order to determine which cytokines to target in specific disease types and patient subsets, it is critical to study the effects of the inhibition of one or more cytokines on the larger cytokine interaction network. We present a novel method to extract a minimal, weighted network from cytokine interaction data. Existing biological network inference methods typically consider the correlation structure of the underlying dataset and/or make further assumptions of the dataset such as the existence of a small core of regulators. This can make them poorly suited for highly connected, non-linear cytokine interaction data. We validated our method using synthetic data and applied our method to a dataset on the regulation of IL23, a cytokine implicated in IBD pathogenesis. Predictions of the extracted IL23 network were validated using additional experimental data and were used to support the view of the cytokines IL1 and IL23 as promising targets for those patients that fail to respond to TNFα inhibition, the current golden standard in IBD treatment.
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Affiliation(s)
- Joanneke E. Jansen
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
| | - Dominik Aschenbrenner
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Autoimmunity, Transplantation and Inflammation, Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Holm H. Uhlig
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Department of Paediatrics, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mark C. Coles
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
| | - Eamonn A. Gaffney
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- * E-mail:
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Karolak A, Branciamore S, McCune JS, Lee PP, Rodin AS, Rockne RC. Concepts and Applications of Information Theory to Immuno-Oncology. Trends Cancer 2021; 7:335-346. [PMID: 33618998 PMCID: PMC8156485 DOI: 10.1016/j.trecan.2020.12.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 01/27/2023]
Abstract
Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.
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Affiliation(s)
- Aleksandra Karolak
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute of City of Hope, Duarte, CA, USA; Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, CA, USA.
| | - Sergio Branciamore
- Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Jeannine S McCune
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Peter P Lee
- Department of Immuno-Oncology, Beckman Research Institute of City of Hope, CA, USA
| | - Andrei S Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, CA, USA
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Aboulmouna L, Raja R, Khanum S, Gupta S, Maurya MR, Grama A, Subramaniam S, Ramkrishna D. Cybernetic modeling of biological processes in mammalian systems. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.100660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Sai A, Kong N. Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation. BMC Bioinformatics 2019; 20:375. [PMID: 31272368 PMCID: PMC6610902 DOI: 10.1186/s12859-019-2970-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 06/26/2019] [Indexed: 12/21/2022] Open
Abstract
Background Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation. Results We undertake an information theoretic analysis of two stochastic models concerning glioma differentiation therapy, an alternative cancer treatment modality whose underlying intracellular mechanisms remain poorly understood. Discernible changes in response dynamics, as captured by summary measures, were observed at low noise levels. Mitigating certain feedback mechanisms present in the signaling network improved information transmission overall, as did targeted subsampling and clustering of response dynamics. Conclusion Computing the channel capacity of noisy signaling pathways present great probative value in uncovering the prevalent trends in noise-induced dynamics. Areas of high dynamical variation can provide concise snapshots of informative system behavior that may otherwise be overlooked. Through this approach, we can examine the delicate interplay between noise and information, from signal to response, through the observed behavior of relevant system components. Electronic supplementary material The online version of this article (10.1186/s12859-019-2970-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aditya Sai
- Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA.
| | - Nan Kong
- Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA
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Renardy M, Yi TM, Xiu D, Chou CS. Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization. PLoS Comput Biol 2018; 14:e1006181. [PMID: 29813055 PMCID: PMC5993324 DOI: 10.1371/journal.pcbi.1006181] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 06/08/2018] [Accepted: 05/07/2018] [Indexed: 11/19/2022] Open
Abstract
A common challenge in systems biology is quantifying the effects of unknown parameters and estimating parameter values from data. For many systems, this task is computationally intractable due to expensive model evaluations and large numbers of parameters. In this work, we investigate a new method for performing sensitivity analysis and parameter estimation of complex biological models using techniques from uncertainty quantification. The primary advance is a significant improvement in computational efficiency from the replacement of model simulation by evaluation of a polynomial surrogate model. We demonstrate the method on two models of mating in budding yeast: a smaller ODE model of the heterotrimeric G-protein cycle, and a larger spatial model of pheromone-induced cell polarization. A small number of model simulations are used to fit the polynomial surrogates, which are then used to calculate global parameter sensitivities. The surrogate models also allow rapid Bayesian inference of the parameters via Markov chain Monte Carlo (MCMC) by eliminating model simulations at each step. Application to the ODE model shows results consistent with published single-point estimates for the model and data, with the added benefit of calculating the correlations between pairs of parameters. On the larger PDE model, the surrogate models allowed convergence for the distribution of 15 parameters, which otherwise would have been computationally prohibitive using simulations at each MCMC step. We inferred parameter distributions that in certain cases peaked at values different from published values, and showed that a wide range of parameters would permit polarization in the model. Strikingly our results suggested different diffusion constants for active versus inactive Cdc42 to achieve good polarization, which is consistent with experimental observations in another yeast species S. pombe.
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Affiliation(s)
- Marissa Renardy
- Department of Mathematics, Ohio State University, Columbus, Ohio, United States of America
| | - Tau-Mu Yi
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, California, United States of America
| | - Dongbin Xiu
- Department of Mathematics, Ohio State University, Columbus, Ohio, United States of America
| | - Ching-Shan Chou
- Department of Mathematics, Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
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Morel PA, Lee REC, Faeder JR. Demystifying the cytokine network: Mathematical models point the way. Cytokine 2016; 98:115-123. [PMID: 27919524 DOI: 10.1016/j.cyto.2016.11.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 11/21/2016] [Indexed: 12/22/2022]
Abstract
Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.
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Affiliation(s)
- Penelope A Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, USA.
| | - Robin E C Lee
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
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Halasz M, Kholodenko BN, Kolch W, Santra T. Integrating network reconstruction with mechanistic modeling to predict cancer therapies. Sci Signal 2016; 9:ra114. [PMID: 27879396 DOI: 10.1126/scisignal.aae0535] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for intervention. As a proof of principle, we analyzed a perturbation data set targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in a panel of colorectal cancer cells. Our computational approach predicted cell line-specific network rewiring. In particular, feedback inhibition of insulin receptor substrate 1 (IRS1) by the kinase p70S6K was predicted to confer resistance to EGFR inhibition, suggesting that disrupting this feedback may restore sensitivity to EGFR inhibitors in colorectal cancer cells. We experimentally validated this prediction with colorectal cancer cell lines in culture and in a zebrafish (Danio rerio) xenograft model.
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Affiliation(s)
- Melinda Halasz
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. .,School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.,School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. .,School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
| | - Tapesh Santra
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
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