Special Issue "Modeling and Analysis of Signal Transduction Networks"

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A special issue of Processes (ISSN 2227-9717).

Deadline for manuscript submissions: closed (31 July 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Juergen Hahn (Website)

Department of Biomedical Engineering, Department of Chemical & Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, New York, NY 12180, USA
Phone: +1 518 276 2138
Fax: +1 518 276 3035
Interests: systems biology; parameter estimation; experimental design; modeling

Special Issue Information

Dear Colleagues,

Signal transduction pathways play a key role in systems biology. These pathways are responsible for relaying cellular information and are also involved in regulating cellular responses. An understanding of signal transduction mechanisms may enable the improvement of treatment options for diseases that are affected by signal transduction mechanisms. One approach for elucidating the dynamics of signal transduction pathways is the derivation of models describing said pathways. However, deriving an accurate signal transduction pathway is difficult. The mechanisms tend to involve many components and there is cross-talk among different pathways. Also, measurements of signal transduction pathway components tend to be sparse and noisy such that the system will have a large degree of uncertainty with respect to both its structure and parameter values. Consequently, validating and refining signal transduction pathway models is crucial.

This Special Issue solicits contributions concerning the modeling and analysis of signal transduction pathways. Contributions involving the modeling and simulation of pathways are of interest. Also welcome are papers concerning methodologies used for modeling and analyzing pathways (e.g., sensitivity analysis, experimental design, parameter estimation, inverse problems or experimental techniques).

Professor Juergen Hahn
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


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Keywords

  • signal transduction
  • modeling
  • sensitivity analysis
  • parameter estimation
  • experimental design
  • cross-talk

Published Papers (10 papers)

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Editorial

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Open AccessEditorial Special Issue on “Modeling and Analysis of Signal Transduction Networks” in the Journal Processes
Processes 2015, 3(3), 540; doi:10.3390/pr3030540
Received: 10 July 2015 / Accepted: 10 July 2015 / Published: 13 July 2015
PDF Full-text (228 KB) | HTML Full-text | XML Full-text
Abstract Biological pathways, such as signaling networks, are a key component of the biological systems of each living cell. [...] Full article

Research

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Open AccessArticle Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells
Processes 2015, 3(2), 286-308; doi:10.3390/pr3020286
Received: 15 December 2014 / Revised: 29 March 2015 / Accepted: 24 April 2015 / Published: 4 May 2015
PDF Full-text (1413 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The fate choice of human embryonic stem cells (hESCs) is controlled by complex signaling milieu synthesized by diverse chemical factors in the growth media. Prevalence of crosstalks and interactions between parallel pathways renders any analysis probing the process of fate transition of [...] Read more.
The fate choice of human embryonic stem cells (hESCs) is controlled by complex signaling milieu synthesized by diverse chemical factors in the growth media. Prevalence of crosstalks and interactions between parallel pathways renders any analysis probing the process of fate transition of hESCs elusive. This work presents an important step in the evaluation of network level interactions between signaling molecules controlling endoderm lineage specification from hESCs using a statistical network identification algorithm. Network analysis was performed on detailed signaling dynamics of key molecules from TGF-β/SMAD, PI3K/AKT and MAPK/ERK pathways under two common endoderm induction conditions. The results show the existence of significant crosstalk interactions during endoderm signaling and they identify differences in network connectivity between the induction conditions in the early and late phases of signaling dynamics. Predicted networks elucidate the significant effect of modulation of AKT mediated crosstalk leading to the success of PI3K inhibition in inducing efficient endoderm from hESCs in combination with TGF-β/SMAD signaling. Full article
Figures

Open AccessArticle Dynamic Modeling of the Human Coagulation Cascade Using Reduced Order Effective Kinetic Models
Processes 2015, 3(1), 178-203; doi:10.3390/pr3010178
Received: 29 October 2014 / Revised: 3 March 2015 / Accepted: 10 March 2015 / Published: 16 March 2015
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Abstract
In this study, we present a novel modeling approach which combines ordinary differential equation (ODE) modeling with logical rules to simulate an archetype biochemical network, the human coagulation cascade. The model consisted of five differential equations augmented with several logical rules describing [...] Read more.
In this study, we present a novel modeling approach which combines ordinary differential equation (ODE) modeling with logical rules to simulate an archetype biochemical network, the human coagulation cascade. The model consisted of five differential equations augmented with several logical rules describing regulatory connections between model components, and unmodeled interactions in the network. This formulation was more than an order of magnitude smaller than current coagulation models, because many of the mechanistic details of coagulation were encoded as logical rules. We estimated an ensemble of likely model parameters (N = 20) from in vitro extrinsic coagulation data sets, with and without inhibitors, by minimizing the residual between model simulations and experimental measurements using particle swarm optimization (PSO). Each parameter set in our ensemble corresponded to a unique particle in the PSO. We then validated the model ensemble using thrombin data sets that were not used during training. The ensemble predicted thrombin trajectories for conditions not used for model training, including thrombin generation for normal and hemophilic coagulation in the presence of platelets (a significant unmodeled component). We then used flux analysis to understand how the network operated in a variety of conditions, and global sensitivity analysis to identify which parameters controlled the performance of the network. Taken together, the hybrid approach produced a surprisingly predictive model given its small size, suggesting the proposed framework could also be used to dynamically model other biochemical networks, including intracellular metabolic networks, gene expression programs or potentially even cell free metabolic systems. Full article
Open AccessArticle A Computational Study of the Effects of Syk Activity on B Cell Receptor Signaling Dynamics
Processes 2015, 3(1), 75-97; doi:10.3390/pr3010075
Received: 14 October 2014 / Revised: 20 January 2015 / Accepted: 28 January 2015 / Published: 11 February 2015
PDF Full-text (423 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The kinase Syk is intricately involved in early signaling events in B cells and isrequired for proper response when antigens bind to B cell receptors (BCRs). Experimentsusing an analog-sensitive version of Syk (Syk-AQL) have better elucidated its role, buthave not completely characterized [...] Read more.
The kinase Syk is intricately involved in early signaling events in B cells and isrequired for proper response when antigens bind to B cell receptors (BCRs). Experimentsusing an analog-sensitive version of Syk (Syk-AQL) have better elucidated its role, buthave not completely characterized its behavior. We present a computational model for BCRsignaling, using dynamical systems, which incorporates both wild-type Syk and Syk-AQL.Following the use of sensitivity analysis to identify significant reaction parameters, we screenfor parameter vectors that produced graded responses to BCR stimulation as is observedexperimentally. We demonstrate qualitative agreement between the model and dose responsedata for both mutant and wild-type kinases. Analysis of our model suggests that the level of NF-KB activation, which is reduced in Syk-AQL cells relative to wild-type, is more sensitiveto small reductions in kinase activity than Erkp activation, which is essentially unchanged.Since this profile of high Erkp and reduced NF-KB is consistent with anergy, this implies thatanergy is particularly sensitive to small changes in catalytic activity. Also, under a range offorward and reverse ligand binding rates, our model of Erkp and NF-KB activation displaysa dependence on a power law affinity: the ratio of the forward rate to a non-unit power of thereverse rate. This dependence implies that B cells may respond to certain details of bindingand unbinding rates for ligands rather than simple affinity alone. Full article
Open AccessArticle Modeling the Dynamics of Acute Phase Protein Expression in Human Hepatoma Cells Stimulated by IL-6
Processes 2015, 3(1), 50-70; doi:10.3390/pr3010050
Received: 22 August 2014 / Accepted: 9 December 2014 / Published: 14 January 2015
Cited by 1 | PDF Full-text (2825 KB) | HTML Full-text | XML Full-text
Abstract
Interleukin-6 (IL-6) is a systemic inflammatory mediator that triggers the human body’s acute phase response to trauma or inflammation. Although mathematical models for IL-6 signaling pathways have previously been developed, reactions that describe the expression of acute phase proteins were not included. [...] Read more.
Interleukin-6 (IL-6) is a systemic inflammatory mediator that triggers the human body’s acute phase response to trauma or inflammation. Although mathematical models for IL-6 signaling pathways have previously been developed, reactions that describe the expression of acute phase proteins were not included. To address this deficiency, a recent model of IL-6 signaling was extended to predict the dynamics of acute phase protein expression in IL-6-stimulated HepG2 cells (a human hepatoma cell line). This included reactions that describe the regulation of haptoglobin, fibrinogen, and albumin secretion by nuclear transcription factors STAT3 dimer and C/EBPβ. This new extended model was validated against two different sets of experimental data. Using the validated model, a sensitivity analysis was performed to identify seven potential drug targets to regulate the secretion of haptoglobin, fibrinogen, and albumin. The drug-target binding kinetics for these seven targets was then integrated with the IL-6 kinetic model to rank them based upon the influence of their pairing with drugs on acute phase protein dynamics. It was found that gp80, JAK, and gp130 were the three most promising drug targets and that it was possible to reduce the therapeutic dosage by combining drugs aimed at the top three targets in a cocktail. These findings suggest hypotheses for further experimental investigation. Full article
Open AccessArticle Mathematical Modeling of Pro- and Anti-Inflammatory Signaling in Macrophages
Processes 2015, 3(1), 1-18; doi:10.3390/pr3010001
Received: 30 September 2014 / Accepted: 25 November 2014 / Published: 26 December 2014
Cited by 1 | PDF Full-text (1753 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Inflammation is a beneficial mechanism that is usually triggered by injury or infection and is designed to return the body to homeostasis. However, uncontrolled or sustained inflammation can be deleterious and has been shown to be involved in the etiology of several [...] Read more.
Inflammation is a beneficial mechanism that is usually triggered by injury or infection and is designed to return the body to homeostasis. However, uncontrolled or sustained inflammation can be deleterious and has been shown to be involved in the etiology of several diseases, including inflammatory bowel disorder and asthma. Therefore, effective anti-inflammatory signaling is important in the maintenance of homeostasis in the body. However, the inter-play between pro- and anti-inflammatory signaling is not fully understood. In the present study, we develop a mathematical model to describe integrated pro- and anti-inflammatory signaling in macrophages. The model incorporates the feedback effects of de novo synthesized pro-inflammatory (tumor necrosis factor α; TNF-α) and anti-inflammatory (interleukin-10; IL-10) cytokines on the activation of the transcription factor nuclear factor κB (NF-κB) under continuous lipopolysaccharide (LPS) stimulation (mimicking bacterial infection). In the model, IL-10 upregulates its own production (positive feedback) and also downregulates TNF-α production through NF-κB (negative feedback). In addition, TNF-α upregulates its own production through NF-κB (positive feedback). Eight model parameters are selected for estimation involving sensitivity analysis and clustering techniques. We validate the mathematical model predictions by measuring phosphorylated NF-κB, de novo synthesized TNF-α and IL-10 in RAW 264.7 macrophages exposed to LPS. This integrated model represents a first step towards modeling the interaction between pro- and anti-inflammatory signaling. Full article
Figures

Open AccessArticle Resolving Early Signaling Events in T-Cell Activation Leading to IL-2 and FOXP3 Transcription
Processes 2014, 2(4), 867-900; doi:10.3390/pr2040867
Received: 29 September 2014 / Revised: 10 November 2014 / Accepted: 13 November 2014 / Published: 25 November 2014
Cited by 1 | PDF Full-text (2522 KB) | HTML Full-text | XML Full-text
Abstract
Signal intensity and feedback regulation are known to be major factors in the signaling events stemming from the T-cell receptor (TCR) and its various coreceptors, but the exact nature of these relationships remains in question. We present a mathematical model of the [...] Read more.
Signal intensity and feedback regulation are known to be major factors in the signaling events stemming from the T-cell receptor (TCR) and its various coreceptors, but the exact nature of these relationships remains in question. We present a mathematical model of the complex signaling network involved in T-cell activation with cross-talk between the Erk, calcium, PKC and mTOR signaling pathways. The model parameters are adjusted to fit new and published data on TCR trafficking, Zap70, calcium, Erk and Isignaling. The regulation of the early signaling events by phosphatases, CD45 and SHP1, and the TCR dynamics are critical to determining the behavior of the model. Additional model corroboration is provided through quantitative and qualitative agreement with experimental data collected under different stimulating and knockout conditions. The resulting model is analyzed to investigate how signal intensity and feedback regulation affect TCR- and coreceptor-mediated signal transduction and their downstream transcriptional profiles to predict the outcome for a variety of stimulatory and knockdown experiments. Analysis of the model shows that: (1) SHP1 negative feedback is necessary for preventing hyperactivity in TCR signaling; (2) CD45 is required for TCR signaling, but also partially suppresses it at high expression levels; and (3) elevated FOXP3 and reduced IL-2 signaling, an expression profile often associated with T regulatory cells (Tregs), is observed when the system is subjected to weak TCR and CD28 costimulation or a severe reduction in CD45 activity. Full article
Open AccessArticle Integrated Computational Model of Intracellular Signaling and microRNA Regulation Predicts the Network Balances and Timing Constraints Critical to the Hepatic Stellate Cell Activation Process
Processes 2014, 2(4), 773-794; doi:10.3390/pr2040773
Received: 29 August 2014 / Revised: 26 September 2014 / Accepted: 8 October 2014 / Published: 17 October 2014
Cited by 2 | PDF Full-text (1750 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Activation and deactivation of hepatic stellate cells (HSCs) is an important mechanism contributing to both healthy liver function and development of liver diseases, which relies on the interplay between numerous signaling pathways. There is accumulating evidence for the regulatory role of microRNAs [...] Read more.
Activation and deactivation of hepatic stellate cells (HSCs) is an important mechanism contributing to both healthy liver function and development of liver diseases, which relies on the interplay between numerous signaling pathways. There is accumulating evidence for the regulatory role of microRNAs that are downstream from these pathways in HSC activation. However, the relative contribution of these pathways and interacting microRNA regulators to the activation process is unknown. We pursued a computational modeling approach to explore the timing and regulatory balances that are critical to HSC activation and quiescence. We developed an integrated model incorporating three signaling pathways with crosstalk (NF-κB, STAT3 and TGF-β) and two microRNAs (miR-146a, miR-21) that are differentially regulated by these pathways. Simulations demonstrated that TGF-β-mediated regulation of microRNAs is critical to drive the HSC phenotypic switch from quiescence (miR-146ahigh miR-21low) to an activated state (miR-146alow miR-21high). We found that the relative timing between peak NF-κB and STAT3 activation plays a key role driving the initial dynamics of miR-146a. We observed re-quiescence from the activated HSC state upon termination of cytokine stimuli. Our integrated model of signaling and microRNA regulation provides a new computational platform for investigating the mechanisms driving HSC molecular state phenotypes in normal and pathological liver physiology. Full article
Figures

Open AccessArticle Mathematical Modeling and Analysis of Crosstalk between MAPK Pathway and Smad-Dependent TGF-β Signal Transduction
Processes 2014, 2(3), 570-595; doi:10.3390/pr2030570
Received: 6 June 2014 / Revised: 15 July 2014 / Accepted: 21 July 2014 / Published: 4 August 2014
Cited by 1 | PDF Full-text (1251 KB) | HTML Full-text | XML Full-text
Abstract
Broad evidence exists for cross talk between the Mitogen-activated protein kinases (MAPK) pathway and Smad-dependent TGF-β signal transduction. A variety of studies, oftentimes involving different cell types, have identified several potential mechanisms for the crosstalk. However, there is no clear consensus on [...] Read more.
Broad evidence exists for cross talk between the Mitogen-activated protein kinases (MAPK) pathway and Smad-dependent TGF-β signal transduction. A variety of studies, oftentimes involving different cell types, have identified several potential mechanisms for the crosstalk. However, there is no clear consensus on the actual mechanism(s) responsible for the crosstalk. This work develops a model of the pathway, including several hypothesized crosstalk mechanisms, and discusses which of the potential mechanisms can appropriately describe observed behaviors. Simulation results show a good agreement of the findings with results reported in the literature. Full article

Review

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Open AccessReview A Quantitative Systems Pharmacology Perspective on Cancer Immunology
Processes 2015, 3(2), 235-256; doi:10.3390/pr3020235
Received: 7 December 2014 / Revised: 2 April 2015 / Accepted: 13 April 2015 / Published: 22 April 2015
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Abstract
The return on investment within the pharmaceutical industry has exhibited an exponential decline over the last several decades. Contemporary analysis suggests that the rate-limiting step associated with the drug discovery and development process is our limited understanding of the disease pathophysiology in [...] Read more.
The return on investment within the pharmaceutical industry has exhibited an exponential decline over the last several decades. Contemporary analysis suggests that the rate-limiting step associated with the drug discovery and development process is our limited understanding of the disease pathophysiology in humans that is targeted by a drug. Similar to other industries, mechanistic modeling and simulation has been proposed as an enabling quantitative tool to help address this problem. Moreover, immunotherapies are transforming the clinical treatment of cure cancer and are becoming a major segment of the pharmaceutical research and development pipeline. As the clinical benefit of these immunotherapies seems to be limited to subset of the patient population, identifying the specific defect in the complex network of interactions associated with host immunity to a malignancy is a major challenge for expanding the clinical benefit. Understanding the interaction between malignant and immune cells is inherently a systems problem, where an engineering perspective may be helpful. The objective of this manuscript is to summarize this quantitative systems perspective, particularly with respect to developing immunotherapies for the treatment of cancer. Full article

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