Inferring a network from dynamical signals at its nodes

Weistuch, Corey; Agozzino, Luca; Mujica‐Parodi, Lilianne R.; Dill, Ken A.

doi:10.1371/journal.pcbi.1008435

Cited by 9 publications

(10 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Covariability of components within cellular processes is typically analyzed using statistical associations [ 7 – 13 ] because accurate mechanistic modelling of biochemical reactions is challenging for complex systems due to the large number of unknown parameters and interactions [ 14 ]. However, molecular abundances are set by underlying physical interactions that affect each component’s rate of production and degradation rather than its instantaneous concentration.…”

Section: Introductionmentioning

confidence: 99%

“…This approach exploits a local qualitative understanding of network interactions through probability balance equations [ 18 ] that must be satisfied as long as we know how one component is made and degraded. In contrast to existing work [ 13 , 19 , 20 ], our approach does not require temporal information, experimental perturbations, or complete observation of all components within an interaction network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks

2022

View full text Add to dashboard Cite

Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks

2022

View full text Add to dashboard Cite

show abstract

“…First, as opposed to other modeling approaches, Max Ent makes minimal assumptions that are not warranted by the data itself [12]. Second, Max Ent is a widely and successfully utilized modeling framework for complex biological systems [13][14][15][16][17][18]. We provide theory and practical demonstrations of our new approach in the present work.…”

Section: Introductionmentioning

confidence: 99%

The Maximum Entropy Principle For Compositional Data

Weistuch

Zhu

Deasy

et al. 2022

Preprint

View full text Add to dashboard Cite

In this work, we provide a general method for inferring the stochastic behavior of compositional systems. Our approach is guided by the principle of maximum entropy, a data-driven modeling technique. In particular, we show that our method can accurately capture stochastic, inter-species relationships with minimal model parameters. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.Author summaryCompositional systems, represented as proportions of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations. We provide a general and data-driven modeling tool for compositional systems to resolve both of these issues. We achieve this through the principle of maximum entropy, which requires only minimal assumptions and limited experimental data in contrast to current alternatives. We show that our approach provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.

show abstract

“…A popular alternative is to derive approximate top-down probabilistic models and train those models on the data. Over the past two decades, the maximum entropy (max ent) method [4] has emerged as perhaps the only candidate for building approximate generative models across a variety of contexts [5][6][7][8][9][10][11][12]. Briefly, amongst all probability distributions (models) that are consistent with user-specified constraints, max ent chooses the least biased one; the max ent distribution does not disfavor any outcome unless warranted by the imposed constraints.…”

Section: Introductionmentioning

confidence: 99%

SiGMoiD: A super-statistical generative model for binary data

2021

View full text Add to dashboard Cite

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using several datasets spanning several time- and length-scales.

show abstract

Inferring a network from dynamical signals at its nodes

Cited by 9 publications

References 56 publications

Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks

Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks

The Maximum Entropy Principle For Compositional Data

SiGMoiD: A super-statistical generative model for binary data

Contact Info

Product

Resources

About