Process pathway inference via time series analysis

@article{Wiggins2002ProcessPI,
  title={Process pathway inference via time series analysis},
  author={Chris H Wiggins and Ilya Nemenman},
  journal={Experimental Mechanics},
  year={2002},
  volume={43},
  pages={361-370}
}
Motivated by recent experimental developments in functional genomics, we construct and test a numerical technique for inferring process pathways, in which one process calls another process, from time series data. We validate using a case in which data are readily available and we formulate an extension, appropriate for genetic regulatory networks, which exploits Bayesian inference and in which the present-day undersampling is compensated for by prior understanding of genetic regulation. 
Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data
TLDR
It is demonstrated that all connections leading to a given network node can be deduced from responses to perturbations none of which directly influences that node, e.g. using strains with knock-outs to other genes.
Algorithms for reconstructing and reasoning about chemical reaction networks
TLDR
The approach is to systematically infer the instability causing structures (ICSs) of a CRN and use machine learning techniques to relate properties of the CRN to the presence of such ICSs, which has the potential to aid in network comprehension and model simplification, by helping reduce the complexity of known bistable systems.
Reverse-engineering biological networks from large data sets
TLDR
Whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems is discussed.
Reconstruction of Metabolic Networks from High‐Throughput Metabolite Profiling Data
TLDR
ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, is used to predict metabolic interactions from data sets, and it is found that the performance of ARACNE on metabolic data is comparable to that on gene expressionData.
Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression
TLDR
An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression and is combined with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics.
Reconstructing chemical reaction networks: data mining meets system identification
TLDR
It is argued that the reconstruction algorithm can serve as an important primitive for data mining in systems biology applications as well as in model reduction and model comprehension.
ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
TLDR
This approach should enhance the ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
Discrete Dynamical System Modeling for Gene Regulatory Networks of HMF Tolerance for Ethanologenic Yeast
Composed of linear difference equations, a discrete dynamical system model was designed to reconstruct transcriptional regulations in gene regulatory networks for ethanologenic yeast Saccharomyces
Automated adaptive inference of phenomenological dynamical models
TLDR
Using simulated data, this work correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.
Reconstructing Partial Orders from Linear Extensions
Reconstructing system dynamics from sequential data traces is an important algorithmic challenge with applications in computational neuroscience, systems biology, paleontology, and physical plant
...
1
2
3
...

References

SHOWING 1-10 OF 50 REFERENCES
Context-Specific Bayesian Clustering for Gene Expression Data
TLDR
This work presents a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data and introduces a new search method that rapidly learns a model according to a Bayesian score.
Using Bayesian Networks to Analyze Expression Data
TLDR
A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Reverse engineering gene networks using singular value decomposition and robust regression
TLDR
This work proposes a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments and uses singular value decomposition to construct a family of candidate solutions and robust regression to identify the solution with the smallest number of connections as the most likely solution.
Regulatory element detection using correlation with expression
TLDR
Analysis of publicly available expression data for Saccharomyces cerevisiae reveals several new putative regulatory elements, some of which plausibly control the early, transient induction of genes during sporulation.
Computational studies of gene regulatory networks: in numero molecular biology
TLDR
The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.
Phase-independent rhythmic analysis of genome-wide expression patterns
TLDR
A model-based analysis technique for extracting and characterizing rhythmic expression profiles from genome-wide DNA microarray hybridization data, which shows how to replace the discretized phase search in the method with an exact (combinatorially precise) phase search, resulting in a faster algorithm with no complexity dependence on phase resolution.
A Test Case of Correlation Metric Construction of a Reaction Pathway from Measurements
A method for the prediction of the interactions within complex reaction networks from experimentally measured time series of the concentration of the species composing the system has been tested
Network motifs in the transcriptional regulation network of Escherichia coli
TLDR
This work applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli, and finds that much of the network is composed of repeated appearances of three highly significant motifs.
Network dynamics and cell physiology
TLDR
A new breed of theoretical molecular biologists reproduces these networks in computers and in the mathematical language of dynamical systems to understand this dance of complex assemblies of interacting proteins.
Mining genome databases to identify and understand new gene regulatory systems.
...
1
2
3
4
5
...