Atlas of Computational Cell Reprogramming

← Cellular intervention design framework

Inverse intervention explicitness

Indexed methods are classified by how completely they instantiate the intervention design template above. Explicitness refers to which mathematical object is present in the formulation, not biological accuracy, causal identifiability, or held out performance. Not every method that outputs actionable perturbations is a formal inverse design method; higher class does not mean a better method.

u^{⋆} = ar g u \in U min [D (F (P_{S}, u), P_{T}) + λ R (u)]

Idealized intervention design template (Section 2 of the companion review). Classes C0, C1, C2, and C3 differ in how much of this objective each indexed method instantiates.

Class C1 · Network-informed intervention prioritization

This page records Class C1 and the 11 indexed methods placed in this explicitness class. Search the corpus below; the class equation and definition follow.

Class equation

u^{⋆} = ar g u max H (u; G, P_{S}, P_{T})

Canonical objective for this explicitness class.

Definition

Uses source-target comparison and static network influence; still no predicted post-intervention state.

Find a method in Class C1

11 methods

Matches method names, authors, paper titles, journals, PMIDs/DOIs, explicitness class, modalities, frameworks, and audit scores.

CellNet

C1

Cahan et al. · 2014 · Cell

Canonical Level 1 method. Reconstructs cell-type-specific GRNs from expression data and prioritizes regulators whose perturbation is expected to restore the target network.

I D T

Code Repro 4/4 FAIR 3/5

Mogrify

C1

Rackham OJ et al. · 2016 · Nature genetics

Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine.

T

Code Repro N/A FAIR N/A

SeesawPred

C1

Hartmann A et al. · 2018 · Scientific reports

Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell.

D

Code Repro 4/4 FAIR 1/5

ANANSE

C1

Xu Q et al. · 2021 · Nucleic acids research

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors.

I T

Code Repro 4/4 FAIR 3/5

Taiji-reprogram

C1

Wang J et al. · 2021 · NAR genomics and bioinformatics

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies.

I T

Code Repro 2/4 FAIR 0/5

TRANSDIRE

C1

Eguchi R et al. · 2022 · Bioinformatics (Oxford, England)

MOTIVATION: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem…

T

Code Repro 3/4 FAIR 1/5

scANANSE

C1

Smits JGA et al. · 2023 · F1000Research

The recent development of single-cell techniques is essential to unravel complex biological systems.

D

Code Repro 4/4 FAIR 3/5

CANDiT

C1

Sinha S et al. · 2025 · Cell reports. Medicine

Reactivating lineage commitment to differentiate, and hence eliminate, cancer stem cells (CSCs) remains a therapeutic challenge.

P

Code Repro 2/4 FAIR 0/5

SwitchTFI

C1

Martini P et al. · 2025 · Genome biology

Many methods exist that infer cell differentiation trajectories from single-cell RNA sequencing data, but only few determine which mechanisms drive the inferred differentiation dynamics.

D

Code Repro 4/4 FAIR 4/5

TRAPT

C1

Zhang G et al. · 2025 · Nature communications

Epigenomics-first deep learning maps a query gene set to ranked upstream TR activity using large ChIP/ATAC/H3K27ac compendia.

P

Code Repro 4/4 FAIR 4/5

Atlas-guided

C1

Chung HK et al. · 2026 · Nature

CD8+ T cells differentiate into diverse states that shape immune outcomes in cancer and chronic infection1-4.

P

Code Repro 4/4 FAIR 1/5