scholarly journals Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

2018 ◽  
Vol 115 (19) ◽  
pp. E4340-E4349 ◽  
Author(s):  
Simon J. Moore ◽  
James T. MacDonald ◽  
Sarah Wienecke ◽  
Alka Ishwarbhai ◽  
Argyro Tsipa ◽  
...  
Keyword(s):  
Time Course ◽  
Resource Competition ◽  
Regulatory Elements ◽  
Limiting Factors ◽  
Model Parameters ◽  
Modeling Framework ◽  
Experimental Conditions ◽  
Rapid Acquisition ◽  
Model Based ◽  
Model Based Analysis

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host,Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this toB. megateriumto quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.

scholarly journals Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

2013 ◽  
Author(s):  
Mehdi Maasoumy ◽  
Barzin Moridian ◽  
Meysam Razmara ◽  
Mahdi Shahbakhti ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Convective Heat Transfer Coefficient ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Modeling Framework ◽  
Parameter Adaptation ◽  
Building Model ◽  
Model Based ◽  
On Line

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Negotiating Early Reuse of Components - A Model-Based Analysis

2011 ◽  
pp. 66-79 ◽  
Author(s):  
J. A. Sykes
Keyword(s):  
Component Composition ◽  
The Other ◽  
Component Reuse ◽  
System Specification ◽  
Modeling Framework ◽  
Model Based ◽  
Model Based Analysis ◽  
Semiotic Theory

Unless existing components are considered during formulation of a system specification, the amount of component reuse that is possible may be limited. In order to increase the amount of reuse, it may be necessary to alter the functionality or performance of the system from that originally envisioned. Tension between stakeholders thus exists. Reuse of components also significantly changes the specification activity because it must now deal with component specifications as input models, which is not necessarily the case when reuse is not the goal. These issues are investigated using a modeling framework based on semiotic theory. The nature of modeling abstractions that could support the negotiation between stakeholders is also explored. Two scenarios are examined: one based on the idea of functional abstractions that can be composed and the other one using structural abstractions of the kind available in the UML as the basis of component composition. Even though at this stage, there are no good examples of functional abstractions that can be composed, it is concluded that functional abstractions are the best prospect for supporting collaboration and negotiation.

scholarly journals msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding

2014 ◽  
Author(s):  
Anil Raj ◽  
Heejung Shim ◽  
Yoav Gilad ◽  
Jonathan K Pritchard ◽  
Matthew Stephens
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Elements ◽  
Transcription Factor Binding ◽  
Dnase I ◽  
Background Model ◽  
Model Parameters ◽  
Modeling Framework ◽  
Factor Binding ◽  
Genomic Locations

Motivation: Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework underestimates the substantial variation in the DNase I cleavage profiles across factor-bound genomic locations and across replicate measurements of chromatin accessibility. Results: In this work, we adapt a multi-scale modeling framework for inhomogeneous Poisson processes to better model the underlying variation in DNase I cleavage patterns across genomic locations bound by a transcription factor. In addition to modeling variation, we also model spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-Seq peaks for those factors. Finally, we propose an extension to this framework that allows for a more flexible background model and evaluate the additional gain in accuracy achieved when the background model parameters are estimated using DNase-seq data from naked DNA. The proposed model can also be applied to paired-end ATAC-seq and DNase-seq data in a straightforward manner. Availability: msCentipede, a Python implementation of an algorithm to infer transcription factor binding using this model, is made available at https://github.com/rajanil/msCentipede

DYNAMIC MODEL-BASED CLUSTERING FOR TIME–COURSE GENE EXPRESSION DATA

2005 ◽  
Vol 03 (04) ◽  
pp. 821-836 ◽  
Author(s):  
FANG-XIANG WU ◽  
W. J. ZHANG ◽  
ANTHONY J. KUSALIK
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Dynamic Model ◽  
Gene Expression Data ◽  
Time Course ◽  
Disease Diagnosis ◽  
Model Parameters ◽  
Expression Data ◽  
Model Based Clustering ◽  
Model Based

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

scholarly journals Model Based Analysis of Signaling Pathways

2008 ◽  
Vol 18 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Jarosław Śmieja
Keyword(s):  
Signaling Pathways ◽  
Gene Transcription ◽  
Regulatory Elements ◽  
Biological Processes ◽  
Interferon Β ◽  
System Description ◽  
Model Driven ◽  
Model Based ◽  
Deterministic Modeling ◽  
Model Based Analysis

Model Based Analysis of Signaling PathwaysThe paper is concerned with application of mathematical modeling to the analysis of signaling pathways. Two issues, deterministic modeling of gene transcription and model-driven discovery of regulatory elements, are dealt with. First, the biological background is given and the importance of the stochastic nature of biological processes is addressed. The assumptions underlying deterministic modeling are presented. Special emphasis is put on describing gene transcription. A framework for including unknown processes activating gene transcription by means of first-order lag elements is introduced and discussed. Then, a particular interferon-β induced pathway is introduced, limited to early events that precede activation of gene transcription. It is shown how to simplify the system description based on the goals of modeling. Further, a computational analysis is presented, facilitating better understanding of the mechanisms underlying regulation of key components in the pathway. The analysis is illustrated by a comparison of simulation and experimental data.

scholarly journals Model-based analysis of latent factors

Web Ecology ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 153-162 ◽  
Author(s):  
Hans-Rolf Gregorius
Keyword(s):  
Model Parameters ◽  
Primary Concern ◽  
Analytic Framework ◽  
Latent Factors ◽  
Measures Of Association ◽  
Model Based ◽  
Mcmc Algorithms ◽  
Efficiency Of Algorithms ◽  
The Common ◽  
Model Based Analysis

Abstract. The detection of community or population structure through analysis of explicit cause–effect modeling of given observations has received considerable attention. The complexity of the task is mirrored by the large number of existing approaches and methods, the applicability of which heavily depends on the design of efficient algorithms of data analysis. It is occasionally even difficult to disentangle concepts and algorithms. To add more clarity to this situation, the present paper focuses on elaborating the system analytic framework that probably encompasses most of the common concepts and approaches by classifying them as model-based analyses of latent factors. Problems concerning the efficiency of algorithms are not of primary concern here. In essence, the framework suggests an input–output model system in which the inputs are provided as latent model parameters and the output is specified by the observations. There are two types of model involved, one of which organizes the inputs by assigning combinations of potentially interacting factor levels to each observed object, while the other specifies the mechanisms by which these combinations are processed to yield the observations. It is demonstrated briefly how some of the most popular methods (Structure, BAPS, Geneland) fit into the framework and how they differ conceptually from each other. Attention is drawn to the need to formulate and assess qualification criteria by which the validity of the model can be judged. One probably indispensable criterion concerns the cause–effect character of the model-based approach and suggests that measures of association between assignments of factor levels and observations be considered together with maximization of their likelihoods (or posterior probabilities). In particular the likelihood criterion is difficult to realize with commonly used estimates based on Markov chain Monte Carlo (MCMC) algorithms. Generally applicable MCMC-based alternatives that allow for approximate employment of the primary qualification criterion and the implied model validation including further descriptors of model characteristics are suggested.

A Simple Method for Estimation of Lipoxygenase and Cyclo-Oxygenase Pathways in Human Platelets - the Use of Thiobarbituric Acid Reaction

1980 ◽  
Vol 44 (02) ◽  
pp. 111-114 ◽  
Author(s):  
Hiroshi Takayama ◽  
Minoru Okuma ◽  
Haruto Uchino
Keyword(s):  
Time Course ◽  
Normal Values ◽  
Human Platelet ◽  
Thiobarbituric Acid ◽  
Human Platelets ◽  
Optimal Conditions ◽  
Experimental Conditions ◽  
Simple Method ◽  
Acid Reaction ◽  
Optimal Ph

SummaryTo develop a simple method for estimation of platelet lipoxygenase (PLO) and cyclo-oxygenase (PCO) pathways, the arachidonic acid (AA) metabolism of human platelet was investigated under various experimental conditions by the use of the thiobarbituric acid (TBA) reaction and a radioisotope technique. A TBA-reactive substance different from malondialdehyde (MDA) via PCO pathway was detected and shown to be derived from the PLO pathway. Since the optimal pH and time course of its formation were different from those of MDA formation via PCO pathway, PLO and PCO pathways were estimated by quantitating the TBA-reactive substances produced by the incubation of AA either with aspirin-treated platelets or with untreated ones, respectively, each under optimal conditions. Normal values expressed in terms of nmol MDA/108 platelets were 1.17±0.34 (M±SD, n = 31) and 0.79±0.15 (n = 31) for PLO and PCO pathways, respectively.

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