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Causal Models, Prediction, and Extrapolation in Cell Line Perturbation Experiments
Applications · Submitted 20 Jul 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We show that causal models such as Cellbox, while requiring more assumptions, enable extrapolation in ways that non-causal regression models cannot.
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The abstract, sentence by sentence
In cell line perturbation experiments, a collection of cells is perturbed with external agents (e.g. drugs) and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational (in silico) models which can predict cellular responses to perturbations. Perturbations with clinically interesting predicted responses can be prioritized for in vitro testing. In this work, we compare causal and non-causal regression models for perturbation response prediction in a Melanoma cancer cell line. The current best performing method on this data set is Cellbox which models how proteins causally effect each other using a system of ordinary differential equations (ODEs). We derive a closed form solution to the Cellbox system of ODEs in the linear case. These analytic results facilitate comparison of Cellbox to regression approaches. We show that causal models such as Cellbox, while requiring more assumptions, enable extrapolation in ways that non-causal regression models cannot. For example, causal models can predict responses for never before tested drugs. We illustrate these strengths and weaknesses in simulations. In an application to the Melanoma cell line data, we find that regression models outperform the Cellbox causal model.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In cell line perturbation experiments, a collection of cells is perturbed with external agents (e.g. drugs) and responses such as protein expression measured. | background 1.00 | 0.06 | 0.02 | asserted 0.99 |
| s1 | Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. | background 0.98 | 0.05 | 0.01 | asserted 1.00 |
| s2 | This has led to the development of computational (in silico) models which can predict cellular responses to perturbations. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s3 | Perturbations with clinically interesting predicted responses can be prioritized for in vitro testing. | background 0.94 | 0.06 | 0.03 | asserted 0.81 |
| s4 | In this work, we compare causal and non-causal regression models for perturbation response prediction in a Melanoma cancer cell line. | contribution 0.98 | 0.90 | 0.04 | asserted 0.90 |
| s5 | The current best performing method on this data set is Cellbox which models how proteins causally effect each other using a system of ordinary differential equations (ODEs). | background 0.94 | 0.08 | 0.05 | asserted 1.00 |
| s6 | We derive a closed form solution to the Cellbox system of ODEs in the linear case. | contribution 0.55 | 0.96 | 0.03 | asserted 1.00 |
| s7 | These analytic results facilitate comparison of Cellbox to regression approaches. | method 0.58 | 0.77 | 0.04 | asserted 0.78 |
| s8 | We show that causal models such as Cellbox, while requiring more assumptions, enable extrapolation in ways that non-causal regression models cannot. | result 0.58 | 0.78 | 0.56 | asserted 1.00 |
| s9 | For example, causal models can predict responses for never before tested drugs. | interpretation 0.81 | 0.22 | 0.11 | hedged possibility 0.62 |
| s10 | We illustrate these strengths and weaknesses in simulations. | method 0.77 | 0.92 | 0.04 | asserted 0.97 |
| s11 | In an application to the Melanoma cell line data, we find that regression models outperform the Cellbox causal model. | result 1.00 | 0.96 | 0.41 | asserted 1.00 |
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Original abstract
In cell line perturbation experiments, a collection of cells is perturbed with external agents (e.g. drugs) and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational (in silico) models which can predict cellular responses to perturbations. Perturbations with clinically interesting predicted responses can be prioritized for in vitro testing. In this work, we compare causal and non-causal regression models for perturbation response prediction in a Melanoma cancer cell line. The current best performing method on this data set is Cellbox which models how proteins causally effect each other using a system of ordinary differential equations (ODEs). We derive a closed form solution to the Cellbox system of ODEs in the linear case. These analytic results facilitate comparison of Cellbox to regression approaches. We show that causal models such as Cellbox, while requiring more assumptions, enable extrapolation in ways that non-causal regression models cannot. For example, causal models can predict responses for never before tested drugs. We illustrate these strengths and weaknesses in simulations. In an application to the Melanoma cell line data, we find that regression models outperform the Cellbox causal model.
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One class of perturbation prediction methods uses the training data to construct a causal model specifying how response variables such as gene or protein expression influence each other [ref] .
Causal Imputation via Synthetic Interventions · Introduction · 0.95
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