======================== Perturbation in biology ======================== .. contents:: :local: :depth: 2 What is perturbation in biology? ================================ Perturbation in biology is an experimental or natural alteration imposed on a biological system (in our case, a cell) to observe how it responds. Two main types of experimental perturbations are genetic and drug (small molecule). Genetic Perturbation: Perturbation sequencing (Perturb-seq) =========================================================== Perturbation sequencing (Perturb-seq) is a type of single-cell RNA-sequencing (scRNA-seq) that can analyze the transcriptional level shifts given a genetic perturbation (i.e., CRISPR-Cas based genetic modification) at a single-cell resolution. In this work, we focus on two types of genetic edits: CRISPRi for genetic downregulation and CRISPRa for genetic upregulation. .. figure:: images/Figure1A.png :scale: 30 % :alt: Schematic of the Perturb-seq experiment. :align: center *Schematic of Perturb-seq.* This figure shows how genetic perturbations are introduced into cells and how the transcriptional response is measured using single-cell RNA sequencing. Each cell receives a unique guide RNA (gRNA) that targets a specific gene for perturbation delivered through a lentiviral vector. The cells are then sequenced to measure changes in gene expression levels, allowing researchers to understand the effects of the genetic perturbation on the cellular transcriptome. Drug Perturbation =============================== Measuring the cellular response in single-cell level under specific drug conditions is also another theme of perturbation biology. We utilized Tahoe100M – a large scale drug perturbation dataset. *In-silico* modeling (virtual cell) =========================================================== *In-silico* modeling is required to resolve the two main challenges. Mainly the cost of sequencing gets exponentially higher and infeasible to test all the genetic or drug combinations. On top of this, the weak signal from the perturbation is challenging to be differentiated from the background noise signal coming from the experimental conditions (batch effect). To resolve this problem various computational models were developed including CPA, GEARS, AttentionPert, CellOT, etc.