scholarly journals Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments

2017 ◽  
Author(s):  
Tapesh Santra
Keyword(s):  
Steady State ◽  
Kinetic Models ◽  
Jacobian Matrix ◽  
Molecular Level ◽  
Mitogen Activated Protein Kinase ◽  
Response Analysis ◽  
Model Parameters ◽  
Source Codes ◽  
Mitogen Activated Protein ◽  
Modular Response Analysis

AbstractA common experimental approach for studying signal transduction networks (STNs) is to measure the steady state concentrations of their components following perturbations to individual components. Such data is frequently used to reconstruct topological models of STNs, but, are rarely used for calibrating kinetic models of these networks. This is because, existing calibration algorithms operate by assigning different sets of values to the parameters of the kinetic models, and for each set of values simulating all perturbations performed in the biochemical experiments. This process is highly computation intensive and may be infeasible when molecular level information of the perturbation experiments is unavailable. Here, I propose an algorithm which can calibrate ordinary differential equation (ODE) based kinetic models of STNs using steady-state perturbation responses (SSPRs) without simulating perturbation experiments. The proposed algorithm uses modular response analysis (MRA) to calculate the scaled Jacobian matrix of the ODE model of an STN using SSPR data. The model parameters are then calibrated to fit the scaled Jacobian matrix calculated in the above step. This procedure does not require simulating the perturbation experiments. Therefore, it is significantly less computation intensive than existing algorithms and can be implemented without molecular level knowledge of the mechanism of perturbations. It is also parallelizable, i.e. can explore multiple sets of parameter values simultaneously, and therefore is scalable. The capabilities and shortcomings of the proposed algorithm are demonstrated using both simulated and real perturbation responses of Mitogen Activated Protein Kinase (MAPK) STN.AvailabilityAll source codes and data needed to replicate the results in this manuscript are available from https://github.com/SBIUCD/MRA_SMC_ABC1

scholarly journals Steady-state analysis of glucose repression reveals hierarchical expression of proteins under Mig1p control in Saccharomyces cerevisiae

2005 ◽  
Vol 388 (3) ◽  
pp. 843-849 ◽  
Author(s):  
Malkhey VERMA ◽  
Paike J. BHAT ◽  
K. V. VENKATESH
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Steady State ◽  
Glucose Repression ◽  
Transcriptional Response ◽  
Mitogen Activated Protein Kinase ◽  
Critical Feature ◽  
Steady State Analysis ◽  
Mitogen Activated Protein ◽  
Kinase Cascade ◽  
State Analysis

Glucose repression is a global transcriptional regulatory mechanism commonly observed in micro-organisms for the repression of enzymes that are not essential for glucose metabolism. In Saccharomyces cerevisiae, Mig1p, a homologue of Wilms' tumour protein, is a global repressor protein dedicated to glucose repression. Mig1p represses genes either by binding directly to the upstream repression sequence of structural genes or by indirectly repressing a transcriptional activator, such as Gal4p. In addition, some genes are repressed by both of the above mechanisms. This raises a fundamental question regarding the physiological relevance of the varied mechanisms of repression that exist involving Mig1p. We address this issue by comparing two well-known glucose-repression systems, that is, SUC2 and GAL gene expression systems, which encompass all the above three mechanisms. We demonstrate using steady-state analysis that these mechanisms lead to a hierarchical glucose repression profile of different family of genes. This switch over from one carbon source to another is well-calibrated as a function of glucose concentration through this hierarchical transcriptional response. The mechanisms prevailing in this repression system can achieve amplification and sensitivity, as observed in the well-characterized MAPK (mitogen-activated protein kinase) cascade system, albeit through a different structure. A critical feature of repression predicted by our steady-state model for the mutant strain of S. cerevisiae lacking Gal80p agrees well with the data reported here as well as that available in the literature.

scholarly journals An Effective Model of HL-60 Differentiation

2015 ◽  
Author(s):  
Ryan Tasseff ◽  
Holly A Jensen ◽  
Johanna Congleton ◽  
Andrew Yen ◽  
Jeffrey D Varner
Keyword(s):  
Cell Fate ◽  
Positive Feedback ◽  
Effective Model ◽  
Mitogen Activated Protein Kinase ◽  
Model Parameters ◽  
Nucleotide Exchange ◽  
Induced Differentiation ◽  
All Trans Retinoic Acid ◽  
Architectural Feature ◽  
Mitogen Activated Protein

We present an effective model All-Trans Retinoic Acid (ATRA)-induced differentiation of HL-60 cells. The model describes a key architectural feature of ATRA-induced differen- tiation, positive feedback between an ATRA-inducible signalsome complex involving many proteins including Vav1, a guanine nucleotide exchange factor, and the activation of the mitogen activated protein kinase (MAPK) cascade. The model, which was developed by integrating logical rules with kinetic model- ing, was significantly smaller than previous models. However, despite its simplicity, it captured key features of ATRA induced differentiation of HL-60 cells. We identified an ensemble of effec- tive model parameters using measurements taken from ATRA- induced HL-60 cells. Using these parameters, model analysis predicted that MAPK activation was bistable as a function of ATRA exposure. Conformational experiments supported ATRA- induced bistability. These findings, combined with other literature evidence, suggest that positive feedback is central to a diversity of cell fate programs.

scholarly journals Basal and IGF-I-dependent regulation of potassium channels by MAP kinases and PI3-kinase during eccentric cardiac hypertrophy

2008 ◽  
Vol 295 (5) ◽  
pp. H1834-H1845 ◽  
Author(s):  
Leyla Y. Teos ◽  
Aiqiu Zhao ◽  
Zikiar Alvin ◽  
Graham G. Laurence ◽  
Chuanfu Li ◽  
...  
Keyword(s):  
Steady State ◽  
Potassium Channels ◽  
Cardiac Hypertrophy ◽  
P38 Mapk ◽  
Map Kinases ◽  
Mitogen Activated Protein Kinase ◽  
Resting Potential ◽  
Adult Rat ◽  
Igf I ◽  
Mitogen Activated Protein

The potassium channels IK and IK1, responsible for the action potential repolarization and resting potential respectively, are altered during cardiac hypertrophy. The activation of insulin-like growth factor-I (IGF-I) during hypertrophy may affect channel activity. The aim was to examine the modulatory effects of IGF-I on IK and IK1 through mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) pathways during hypertrophy. With the use of specific inhibitors for ERK1/2 (PD98059), p38 MAPK (SB203580) and PI3K/Akt (LY294002), Western blot and whole cell patch-clamp were conducted on sham and aorto-caval shunt-induced hypertrophy adult rat myocytes. Basal activation levels of MAPKs and Akt were increased during hypertrophy. Acute IGF-I (10−8 M) enhanced basal activation levels of these kinases in normal hearts but only those of Akt in hypertrophied ones. IK and IK1 activities were lowered by IGF-I. Inhibition of ERK1/2, p38 MAPK, or Akt reduced basal IK activity by 70, 32, or 50%, respectively, in normal cardiomyocytes vs. 53, 34, or 52% in hypertrophied ones. However, basal activity of IK1 was reduced by 45, 48, or 45% in the former vs. 63, 43, or 24% in the latter. The inhibition of either MAPKs or Akt alleviated IGF-I effects on IK and IK1. We conclude that basal IK and IK1 are positively maintained by steady-state Akt and ERK activities. K+ channels seem to be regulated in a dichotomic manner by acutely stimulated MAPKs and Akt. Eccentric cardiac hypertrophy may be associated with a change in the regulation of the steady-state basal activities of K+ channels towards MAPKs, while that of the acute IGF-I-stimulated ones toward Akt.

scholarly journals Steady-State Levels of Phosphorylated Mitogen-Activated Protein Kinase Kinase 1/2 Determined by Mortalin/HSPA9 and Protein Phosphatase 1 Alpha in KRAS and BRAF Tumor Cells

2017 ◽  
Vol 37 (18) ◽  
Author(s):  
Pui-Kei Wu ◽  
Seung-Keun Hong ◽  
Jong-In Park
Keyword(s):  
Steady State ◽  
Tumor Cells ◽  
Protein Phosphatase ◽  
Mitogen Activated Protein Kinase ◽  
Binding Assays ◽  
Mitogen Activated Protein ◽  
Binding Cavity ◽  
Steady State Levels ◽  
Erk Activity

ABSTRACT Although deregulation of MEK/extracellular signal-regulated kinase (ERK) activity is a key feature in cancer, high-magnitude MEK/ERK activity can paradoxically induce growth inhibition. Therefore, additional mechanisms may exist to modulate MEK/ERK activity in favor of tumor cell proliferation. We previously reported that mortalin/HSPA9 can facilitate proliferation of certain KRAS and BRAF tumor cells by modulating MEK/ERK activity. In this study, we demonstrated that mortalin can regulate MEK/ERK activity via protein phosphatase 1α (PP1α). We found that PP1α inhibition increases steady-state levels of phosphorylated MEK1/2 in various tumor cells expressing B-RafV600E or K-RasG12C/D. Intriguingly, coimmunoprecipitation and in vitro binding assays revealed that mortalin facilitates PP1α-mediated MEK1/2 dephosphorylation by promoting PP1α-MEK1/2 interaction in an ATP-sensitive manner. The region spanning Val482 to Glu491 in the substrate-binding cavity and the substrate lid of mortalin were necessary for these physical interactions, which is consistent with conventional heat shock protein 70 (HSP70)-client interaction mechanisms. Nevertheless, mortalin depletion did not affect cellular PP1α levels or its regulatory phosphorylation, suggesting a nonconventional role for mortalin in promoting PP1α-MEK1/2 interaction. Of note, PP1α was upregulated in human melanoma and pancreatic cancer biopsy specimens in correlation with mortalin upregulation. PP1α may therefore have a role in tumorigenesis in concert with mortalin, which affects MEK/ERK activity in tumor cells.

scholarly journals Identification of Solar Collector Dynamics Using Physical Model-Based Approach

1994 ◽  
Vol 116 (4) ◽  
pp. 755-763 ◽  
Author(s):  
B. J. Huang ◽  
S. B. Wang
Keyword(s):  
Steady State ◽  
System Dynamics ◽  
Physical Model ◽  
Transfer Functions ◽  
Linear Time ◽  
Operating Conditions ◽  
Distributed Model ◽  
Response Analysis ◽  
Linear Perturbation ◽  
Model Parameters

A system dynamics model of flat-plate solar collectors was derived and identified here. A nonlinear physical model was first derived from a two-node concept and energy conservation principle. The model was then approximated by the linear perturbation equations which were Laplace transformed and solved to lead to a distributed model in terms of the transfer functions. A model reduction was further employed to yield a linear time-invariant model with parameters as functions of steady-state operating conditions. The model parameters were identified by a dynamic test with step inputs at various operating conditions using frequency response analysis and model fitting in frequency domain. The identified parameters were then fitted to a function of steady-state mass flowrate mw. Thus, the model can describe the system dynamics behavior under various operating conditions through the identified parameters. The simulations using the model were shown to agree very well with the test results.

scholarly journals Deoxynivalenol Modulates the Viability, ROS Production and Apoptosis in Prostate Cancer Cells

Toxins ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 265 ◽  
Author(s):  
Dominika Ewa Habrowska-Górczyńska ◽  
Karolina Kowalska ◽  
Kinga Anna Urbanek ◽  
Kamila Domińska ◽  
Agata Sakowicz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Level ◽  
Mitogen Activated Protein Kinase ◽  
High Dose ◽  
Ros Production ◽  
Single Exposure ◽  
Androgen Sensitivity ◽  
Mitogen Activated Protein ◽  
High Concentrations ◽  
Ribosomal Stress

Deoxynivalenol (DON), known as vomitoxin, a type B trichothecene, is produced by Fusarium. DON frequently contaminates cereal grains such as wheat, maize, oats, barley, rye, and rice. At the molecular level, it induces ribosomal stress, inflammation and apoptosis in eukaryotic cells. Our findings indicate that DON modulates the viability of prostate cancer (PCa) cells and that the response to a single high dose of DON is dependent on the androgen-sensitivity of cells. DON appears to increase reactive oxygen species (ROS) production in cells, induces DNA damage, and triggers apoptosis. The effects of DON application in PCa cells are influenced by the mitogen-activated protein kinase (MAPK) and NFΚB- HIF-1α signaling pathways. Our results indicate that p53 is a crucial factor in DON-associated apoptosis in PCa cells. Taken together, our findings show that a single exposure to high concentrations of DON (2–5 µM) modulates the progression of PCa.

scholarly journals Bayesian inference of metabolic kinetics from genome-scale multiomics data

2018 ◽  
Author(s):  
Peter C. St. John ◽  
Jonathan Strutz ◽  
Linda J. Broadbelt ◽  
Keith E.J. Tyo ◽  
Yannick J. Bomble
Keyword(s):  
Steady State ◽  
Bayesian Inference ◽  
Kinetic Models ◽  
Large Scale ◽  
Biological Data ◽  
Model Parameters ◽  
Enzymatic Reactions ◽  
Rate Laws ◽  
Wide Range ◽  
Computationally Intensive

SummaryModern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

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