Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

Dixit, Purushottam D.; Lyashenko, Eugenia; Niepel, Mario; Vitkup, Dennis

doi:10.1016/j.cels.2019.11.010

Cited by 32 publications

(36 citation statements)

References 66 publications

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“…The application of mechanistic models often requires exploration of a high dimensional parameter space, and data-guided methods have been developed to both enable efficient characterization of this space and facilitate virtual population development [56,[102][103][104][105]. Highly efficient parameter exploration and model calibration strategies are a fourth area where ML strategies may further assist QSP.…”

Section: Machine Learning-assisted Qsp For Heart Failurementioning

confidence: 99%

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Cheng

Qiu

Schmidt

et al. 2021

J Pharmacokinet Pharmacodyn

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Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.

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Section: Machine Learning-assisted Qsp For Heart Failurementioning

confidence: 99%

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Cheng

Qiu

Schmidt

et al. 2021

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

show abstract

“…The old MaxEnt was sufficiently versatile for providing the foundations to equilibrium statistical mechanics [ 2 ] and to find application in a wide variety of fields such as economics [ 14 ], ecology [ 15 , 16 ], cellular biology [ 17 , 18 ], network science [ 19 , 20 ], and opinion dynamics [ 21 , 22 ]. As is the case with thermodynamics, all these applications are essentially static.…”

Section: Introductionmentioning

confidence: 99%

Entropic Dynamics on Gibbs Statistical Manifolds

Pessoa

Costa

Caticha

2021

Entropy

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Entropic dynamics is a framework in which the laws of dynamics are derived as an application of entropic methods of inference. Its successes include the derivation of quantum mechanics and quantum field theory from probabilistic principles. Here, we develop the entropic dynamics of a system, the state of which is described by a probability distribution. Thus, the dynamics unfolds on a statistical manifold that is automatically endowed by a metric structure provided by information geometry. The curvature of the manifold has a significant influence. We focus our dynamics on the statistical manifold of Gibbs distributions (also known as canonical distributions or the exponential family). The model includes an “entropic” notion of time that is tailored to the system under study; the system is its own clock. As one might expect that entropic time is intrinsically directional; there is a natural arrow of time that is led by entropic considerations. As illustrative examples, we discuss dynamics on a space of Gaussians and the discrete three-state system.

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“…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

Self Cite

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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.

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Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

Abstract: Highlights d Efficient approach to infer signaling parameter distributions from single-cell data d Nonparametric inference of parameter distributions using maximum entropy principle d Investigation of population heterogeneity in phosphorylation cascades

Cited by 32 publications

References 66 publications

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Entropic Dynamics on Gibbs Statistical Manifolds

SiGMoiD: A super-statistical generative model for binary data

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