scholarly journals Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 136 ◽  
Author(s):  
Eugenio Cinquemani
Keyword(s):  
Reaction Network ◽  
Network Models ◽  
Stochastic Modelling ◽  
Reaction Rates ◽  
Network Reconstruction ◽  
Biochemical Network ◽  
Biochemical Reaction Networks ◽  
Reconstruction Methods ◽  
Biochemical Interaction ◽  
Reconstruction Performance

Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example.

scholarly journals A probabilistic approach to explore signal execution mechanisms with limited experimental data

2019 ◽  
Author(s):  
Michael A. Kochen ◽  
Carlos F. Lopez
Keyword(s):  
Experimental Data ◽  
Probabilistic Approach ◽  
Reaction Network ◽  
Biochemical Network ◽  
Model Parameters ◽  
Type I ◽  
Biochemical Reaction Networks ◽  
Cellular Processes ◽  
Biochemical Systems ◽  
Qualitative Dynamic

AbstractMathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that incorporates available information regarding reaction network topology and parameters, can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that in silico knockouts, represented by model subnetworks, successfully identify the most likely execution mode for specific concentrations of key molecular regulators. We then show that changes in molecular regulator concentrations alter the overall reaction flux through the network by shifting the primary route of signal flow between the direct caspase and mitochondrial pathways. Our work thus demonstrates that probabilistic approaches can be used to explore the qualitative dynamic behavior of model biochemical systems even with missing or sparse data.

Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

2014 ◽  
Vol 989-994 ◽  
pp. 3946-3951
Author(s):  
Xin Jin ◽  
Ming Feng Jiang ◽  
Jie Feng
Keyword(s):  
Compressed Sensing ◽  
Group Structure ◽  
Direct Method ◽  
Group Sparsity ◽  
Mr Image ◽  
Assignment Method ◽  
Group Assignment ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Space Data

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

scholarly journals Global predictions for astrophysics applications

2020 ◽  
Vol 13 ◽  
pp. 18
Author(s):  
P. Demetriou
Keyword(s):  
Nuclear Reaction ◽  
Cross Sections ◽  
Nuclear Reactions ◽  
Nuclear Astrophysics ◽  
Reaction Network ◽  
Reaction Rates ◽  
Reaction Cross ◽  
Hartree Fock ◽  
Microscopic Models ◽  
The Stability

Nuclear reaction rates play a crucial role in nuclear astrophysics. In the last decades there has been an enormous effort to measure reaction cross sections and extensive experimental databases have been compiled as a result. In spite of these efforts, most nuclear reaction network calculations still have to rely on theoretical predic- tions of experimentally unknown rates. In particular, in astrophysics applications such as the s-, r- and p-process nucleosynthesis involving a large number of nuclei and nuclear reactions (thousands). Moreover, most of the ingredients of the cal- culations of reaction rates have to be extrapolated to energy and/or mass regions that cannot be explored experimentally. For this reason it is important to develop global microscopic or semi-microscopic models of nuclear properties that give an accurate description of existing data and are reliable for predictions far away from the stability line. The need for more microscopic input parameters has led to new devel- opments within the Hartree-Fock-Bogoliubov method, some of which are presented in this paper.

scholarly journals Building reaction kinetic models for amiloid fibril growth

BIOMATH ◽  
2016 ◽  
Vol 5 (1) ◽  
pp. 1607311 ◽  
Author(s):  
Svetoslav Marinov Markov
Keyword(s):  
Computer Algebra ◽  
Computer Algebra System ◽  
Reaction Kinetic ◽  
Growth Models ◽  
Reaction Network ◽  
Network Models ◽  
Point Of View ◽  
Logistic Growth ◽  
Sigmoidal Functions ◽  
Methodological Aspects

In this work we  discuss some methodological aspects of the creation and formulation of mathematical  models describing the growth of species from the point of view of reaction kinetics. Our discussion is based on familiar examples of growth models such as logistic growth and enzyme kinetics. We   propose several reaction network  models  for  the amiloid fibrillation processes in the citoplasm. The solutions of the models are sigmoidal functions graphically visualized using  the computer algebra system   Mathematica.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Universal scaling across biochemical networks on Earth

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0149 ◽  
Author(s):  
Hyunju Kim ◽  
Harrison B. Smith ◽  
Cole Mathis ◽  
Jason Raymond ◽  
Sara I. Walker
Keyword(s):  
Scaling Laws ◽  
Network Size ◽  
Biochemical Networks ◽  
Biochemical Network ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Global Database ◽  
Universal Scaling ◽  
Planetary Scale ◽  
Domains Of Life

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

scholarly journals Reconstruction Techniques in IceCube using Convolutional and Generative Neural Networks

2019 ◽  
Vol 207 ◽  
pp. 05005 ◽  
Author(s):  
Mirco Huennefeld
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Reconstruction Method ◽  
Network Architectures ◽  
Reconstruction Methods ◽  
Physics Potential ◽  
Reconstruction Performance ◽  
Maximum Likelihood Methods ◽  
First Results

Reliable and accurate reconstruction methods are vital to the success of high-energy physics experiments such as IceCube. Machine learning based techniques, in particular deep neural networks, can provide a viable alternative to maximum-likelihood methods. However, most common neural network architectures were developed for other domains such as image recogntion. While these methods can enhance the reconstruction performance in IceCube, there is much potential for tailored techniques. In the typical physics use-case, many symmetries, invariances and prior knowledge exist in the data, which are not fully exploited by current network architectures. Novel and specialized deep learning based reconstruction techniques are desired which can leverage the physics potential of experiments like IceCube. A reconstruction method using convolutional neural networks is presented which can significantly increase the reconstruction accuracy while greatly reducing the runtime in comparison to standard reconstruction methods in Ice- Cube. In addition, first results are discussed for future developments based on generative neural networks.

IDENTIFYING CHEMICAL REACTION NETWORK MODELS

2007 ◽  
Vol 40 (5) ◽  
pp. 225-230 ◽  
Author(s):  
S.C. Burnham ◽  
M.J. Willis ◽  
A.R Wright
Keyword(s):  
Chemical Reaction ◽  
Reaction Network ◽  
Network Models ◽  
Chemical Reaction Network
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