← 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 C3 · Explicit model-based inverse intervention

This page records Class C3 and the 31 indexed methods placed in this explicitness class. Search the corpus below; the class equation and definition follow.

Class equation

u^{⋆} = ar g u min D (F_{θ_{u}} (P_{S}), P_{T}) + λ R (u)

Canonical objective for this explicitness class.

Definition

Candidate interventions enter an explicit model with intervention semantics and the predicted outcome of each candidate is simulated forward.

Find a method in Class C3

31 methods

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

Cornelius et al.

Cornelius SP et al. · 2013 · Nature communications

The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response and cell reprogramming.

No code Repro N/A FAIR N/A

Crespo & Del Sol

Crespo I et al. · 2013 · Stem cells (Dayton, Ohio)

Transcription factor cross-repression is an important concept in cellular differentiation.

No code Repro N/A FAIR N/A

DEPCs (Crespo et al. 2013)

Crespo I et al. · 2013 · BMC systems biology

BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes.

I D T P

No code Repro N/A FAIR N/A

FVS (Mochizuki et al.)

Mochizuki A et al. · 2013 · Journal of theoretical biology

Modern biology provides many networks describing regulations between many species of molecules.

D P

No code Repro N/A FAIR N/A

Hopfield Neural Networks (Lang et al.)

Lang AH et al. · 2014 · PLoS computational biology

A common metaphor for describing development is a rugged "epigenetic landscape" where cell fates are represented as attracting valleys resulting from a complex regulatory network.

I D T

No code Repro N/A FAIR N/A

Stable Motifs

Zañudo JG et al. · 2015 · PLoS computational biology

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming.

D P

Code Repro 4/4 FAIR 0/5

Murrugarra et al.

Murrugarra D et al. · 2016 · BMC systems biology

BACKGROUND: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a…

Code Repro 2/4 FAIR 0/5

Toggle Switches (Okawa et al.)

Okawa S et al. · 2016 · Stem cell reports

Identification of cell-fate determinants for directing stem cell differentiation remains a challenge.

No code Repro N/A FAIR N/A

Del Vecchio et al.

Del Vecchio D et al. · 2017 · Cell systems

To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cell's phenotype.

I D P

No code Repro N/A FAIR N/A

DGC (Data Guided Control)

Ronquist S et al. · 2017 · Proceedings of the National Academy of Sciences of the United States of America

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away.

I T

No code Repro N/A FAIR N/A

Zañudo et al. (FVS control)

Zañudo JGT et al. · 2017 · Proceedings of the National Academy of Sciences of the United States of America

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that…

D P

Code Repro 4/4 FAIR 0/5

LDOI

Yang G et al. · 2018 · Frontiers in physiology

Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies.

Code Repro 4/4 FAIR 0/5

PCK (Phenotype Control Kernel)

Choo SM et al. · 2018 · BMC systems biology

BACKGROUND: Controlling complex molecular regulatory networks is getting a growing attention as it can provide a systematic way of driving any cellular state to a desired cell phenotypic state.

No code Repro N/A FAIR N/A

BRC (Boundary-Reaching Control)

Choo SM et al. · 2019 · Scientific reports

A cell phenotype can be represented by an attractor state of the underlying molecular regulatory network, to which other network states eventually converge.

No code Repro N/A FAIR N/A

Aguilar et al.

Aguilar B et al. · 2020 · Letters in biomathematics

One of the ultimate goals in systems biology is to develop control strategies to find efficient medical treatments.

Code Repro 3/4 FAIR 0/5

BoolIFFNN

Choo SM et al. · 2020 · Frontiers in physiology

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them.

D P

No code Repro N/A FAIR N/A

Sordo et al.

Sordo Vieira L et al. · 2020 · Bulletin of mathematical biology

Many problems in biology and medicine have a control component.

Code Repro 3/4 FAIR 0/5

CABEAN

Su C et al. · 2021 · Bioinformatics (Oxford, England)

SUMMARY: Direct cell reprogramming, also called transdifferentiation, has great potential for tissue engineering and regenerative medicine.

T P

Code Repro 4/4 FAIR 1/5

IRENE

Jung S et al. · 2021 · Nature communications

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies.

I D T

Code Repro 4/4 FAIR 0/5

CELLoGeNe

Andersson E et al. · 2022 · iScience

Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming.

Code Repro 3/4 FAIR 0/5

cSTAR

Rukhlenko OS et al. · 2022 · Nature

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology.

Code Repro 1/4 FAIR 0/5

NETISCE

Marazzi L et al. · 2022 · NPJ systems biology and applications

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming.

D P

Code Repro 4/4 FAIR 3/5

Tercan / PBN

Tercan B et al. · 2022 · iScience

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation.

Code Repro 4/4 FAIR 0/5

CellOracle

Kamimoto K et al. · 2023 · Nature

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1.

D P

Code Repro 4/4 FAIR 3/5

DCGS

An S et al. · 2023 · Bioinformatics (Oxford, England)

MOTIVATION: Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models.

Code Repro 4/4 FAIR 1/5

CAESAR

Kim N et al. · 2024 · Briefings in bioinformatics

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that…

D T P

Code Repro 3/4 FAIR 0/5

BoNesis + CABEAN

Chevalier S et al. · 2025 · NPJ systems biology and applications

Boolean networks provide robust, explainable, and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes.

D T

Code Repro 4/4 FAIR 3/5

pbn-STAC

pbn-STAC · 2025

Method indexed in the Atlas. Editorial one-liner pending review.

Code Repro 4/4 FAIR 1/5

PDGrapher

Gonzalez et al. · 2025 · Nature Biotechnology

Recent Level 3 frontier. Graph neural network that predicts transcriptional responses to candidate interventions and ranks perturbations by predicted reconstruction of a target state.

Code Repro 4/4 FAIR 4/5

REVERT

Shin D et al. · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany)

A cell fate change such as tumorigenesis incurs critical transition.

Code Repro 4/4 FAIR 0/5

TFcomb

Li C et al. · 2025 · Genome research

Reprogramming cell state transitions provides the potential for cell engineering and regenerative therapy.

I D T

Code Repro 4/4 FAIR 3/5