scholarly journals Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

2019 ◽  
Vol 15 (12) ◽  
pp. e1007311 ◽  
Author(s):  
Shu Wang ◽  
Jia-Ren Lin ◽  
Eduardo D. Sontag ◽  
Peter K. Sorger
Keyword(s):  
Single Cell ◽  
Systems Theory ◽  
Network Structure ◽  
Reaction Network

scholarly journals Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

2019 ◽  
Author(s):  
Shu Wang ◽  
Jia-Ren Lin ◽  
Eduardo D. Sontag ◽  
Peter K. Sorger
Keyword(s):  
Single Cell ◽  
Chemical Reaction ◽  
Network Structure ◽  
Statistical Models ◽  
Control Cell ◽  
Toric Varieties ◽  
Chemical Reaction Networks ◽  
Data Driven ◽  
Cell Subpopulation ◽  
Reaction Networks

AbstractThe goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of effective stoichiometric space (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence.Author summaryWe introduce a new notion, which we call the effective stoichiometric space (ESS), that elucidates network structure from the covariances of single-cell multiplexed data. The ESS approach differs from methods that are based on purely statistical models of data: it allows a completely new and data-driven translation of the theory of toric varieties in geometry and specifically their role in chemical reaction networks (CRN). In the process, we reframe certain aspects of the theory of CRN. As illustrations of our approach, we find stoichiometry in different single-cell datasets, and pinpoint dose-dependence of network perturbations in drug-treated cells.

scholarly journals Phenotyping single-cell motility in microfluidic confinement

2021 ◽  
Author(s):  
Samuel A Bentley ◽  
Vasileios Anagnostidis ◽  
Hannah Laeverenz Schlogelhofer ◽  
Fabrice Gielen ◽  
Kirsty Y Wan
Keyword(s):  
Single Cell ◽  
Genetic Basis ◽  
Marine Alga ◽  
Reaction Network ◽  
Droplet Microfluidics ◽  
Model Species ◽  
Active Systems ◽  
Locomotor Behaviour ◽  
Cellular Excitability ◽  
First Time

At all scales, the movement patterns of organisms serve as dynamic read-outs of their behaviour and physiology. We devised a novel droplet microfluidics assay to encapsulate single algal microswimmers inside closed arenas, and comprehensively studied their roaming behaviour subject to a large number of environmental stimuli. We compared two model species, Chlamydomonas reinhardtii (freshwater alga, 2 cilia), and Pyramimonas octopus (marine alga, 8 cilia), and detailed their highly-stereotyped behaviours and the emergence of a trio of macroscopic swimming states (smooth-forward, quiescent, tumbling or excitable backward). Harnessing ultralong timeseries statistics, we reconstructed the species-dependent reaction network that underlies the choice of locomotor behaviour in these aneural organisms, and discovered the presence of macroscopic non-equilibrium probability fluxes in these active systems. We also revealed for the first time how microswimmer motility changes instantaneously when a chemical is added to their microhabitat, by inducing deterministic fusion between paired droplets - one containing a trapped cell, and the other, a pharmacological agent that perturbs cellular excitability. By coupling single-cell entrapment with unprecedented tracking resolution, speed and duration, our approach offers unique and potent opportunities for diagnostics, drug-screening, and for querying the genetic basis of micro-organismal behaviour.

scholarly journals Frequency Spectra and the Color of Cellular Noise

2021 ◽  
Author(s):  
Ankit Gupta ◽  
Mustafa Khammash
Keyword(s):  
Single Cell ◽  
Cellular Networks ◽  
Frequency Spectrum ◽  
Controller Design ◽  
Reaction Network ◽  
Time Signal ◽  
Trajectory Data ◽  
Frequency Spectra ◽  
Analysis And Design ◽  
The Impact

Abstract The invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. By decomposing a (time) signal into its essential frequency components, these methods uncovered deep insights into the signal and its generating process, precipitating tremendous inventions and discoveries in many fields of engineering, technology, and physical science. In systems and synthetic biology, however, the impact of frequency methods has been far more limited despite their huge promise. This is in large part due to the difficulties encountered in connecting the underlying stochastic reaction network in the living cell, whose dynamics is typically modelled as a continuous-time Markov chain (CTMC), to the frequency content of the observed, distinctively noisy single-cell trajectories. Here we draw on stochastic process theory to develop a spectral theory and computational methodologies tailored specifically to the computation and analysis of frequency spectra of noisy cellular networks. Specifically, we develop a generic method to obtain accurate Padé approximations of the spectrum from a handful of trajectory simulations. Furthermore, for linear networks, we present a novel decomposition result that expresses the frequency spectrum in terms of its sources. Our results provide new conceptual and practical methods for the analysis and design of noisy cellular networks based on their output frequency spectra. We illustrate this through diverse case studies in which we show that the single-cell frequency spectrum facilitates topology discrimination, synthetic oscillator optimization, cybergenetic controller design, systematic investigation of stochastic entrainment, and even parameter inference from single-cell trajectory data.

scholarly journals Constructing Local Cell Specific Networks from Single Cell Data

2021 ◽  
Author(s):  
Xuran Wang ◽  
David Choi ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Network Structure ◽  
Gene Networks ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Cellular Level ◽  
Gene Block ◽  
Local Cell ◽  
The Individual ◽  
Downstream Analysis

ABSTRACTGene co-expression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach that estimates cell-specific networks (CSN) for each cell using a method inspired by Dai et al. (7). Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of network structure, which provide better estimates of gene block structures than traditional measures. The method, called locCSN, is based on a non-parametric investigation of the joint distribution of gene expression, hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. The individual networks preserve information about the heterogeneity of the cells and having repeated estimates of network structure facilitates testing for difference in network structure between groups of cells. The original CSN algorithm showed promise; however, it had shortcomings which locCSN overcomes. Additionally, we propose new downstream analysis methods using CSNs, to utilize more fully the information contained within them. Finally, to further our understanding of autism spectrum disorder we examined the evolution of gene networks in fetal brain cells and compared the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene co-expression changes.

scholarly journals Identifiability of stochastically modelled reaction networks

2021 ◽  
pp. 1-23 ◽  
Author(s):  
GERMAN ENCISO ◽  
RADEK ERBAN ◽  
JINSU KIM
Keyword(s):  
System Dynamics ◽  
Network Structure ◽  
Network Inference ◽  
Stochastic Dynamics ◽  
Reaction Network ◽  
Stochastic Simulations ◽  
Reaction Networks ◽  
Data Types ◽  
Biochemical System ◽  
Underlying Network

Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.

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