CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Authors: Yuhui Zhang
Yuchang Su
Chenyu Wang
Tianhong Li
Zoe Wefers
Jeffrey J Nirschl
James Burgess
Daisy Ding
Alejandro Lozano
Emma Lundberg
Serena Yeung-Levy
Venue: ICML 2025 poster (2025)
Keywords: flow matching
cell image
drug discovery
generative models
Primary area: applications->health_medicine
TL;DR: CellFlux is an image-generative model that simulates cellular morphology changes from chemical and genetic perturbations using flow matching.

Abstract: Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects—a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlux enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research. Project page: https://yuhui-zh15.github.io/CellFlux/.

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Yuhui Zhang" Yuchang Su 2*
Chenyu Wang
Tianhong Li Zoe Wefers Jeffrey Nirschl'
James Burgess
Daisy Ding'
Alejandro Lozano"
Emma Lundberg Serena Yeung-Levy'
Abstract
cost and time of biomedical research by prioritizing the ex- periments most likely succeed based on the virtual cell simulation (Carpenter, 2007). Moreover, this could unlock personalized therapeutic development by building digital twins of cells from patients to simulate patient-specifte re- sponses (Katsoulakis et al., 2024).
Building a virtual cell capable of accurately sim- ulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an amage-generative model that sim- ulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux mod- els distribution-wis transformations from unper- turbed to perturbed cell states, effectively dis- tinguishing actual perturbation effects from ex- perimental artifacts such as batch effects-a ma- jor challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRxI), and com- bined perturbation (JUMP) datasets, CellFlux gen- erates biologically meaningful cell images that faithfully capture certurbation-sspecinn morpho- logical changes, achieving a 35% improvement in FID scores and a 12% increase mode-of- action prediction accuracy over existing methods. Additionally, CellFlux enables continuous inter- polation between cellular states, providing po- tential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical re- search. Project page: https: //yuhui -2h15. github. io/ Cellluxx/
Two recent advances have made creating generative virtual cell model possible. On the computational side, generative models now excel at modeling and sampling from com- plex data distributions, demonstrating remarkable success in synthesizing texts, images, videos, and biological se- quences (OpenAI, 2024; Esser et al., 2024; Kondratyuk et al., 2024; Hayes al., 2025). Concurrently, on the biotechnology side, automated high-content screening has generated massive imaging datasets reaching terabytes or petabytes - that capture how cells respond to hundreds of thousands of chemical compounds and genetic modifica- tions (Chandrasekaran et al., 2023; Fay et al., 2023).
In this work, we introduce CellFlux, an saange-ennrrtive model that simulates how cellular morphology changes in response chemical or genetic perturbations (Figure la). CellFlux's key innovation is formulating cellular morphol- ogy prediction as lem, and leveraging flow matching (Lipman et al., 2023), a state-of-the-art generative modeling technique designed for distribution-wee transformation, to solve this problem.
learning prob-
Specificallyy cell morphology data are collected through high-content microscopy screening, where images of control and perturbed cells are captured from experimental wells across different batches (Figure 1b). Control wells, which receive no drug treatment or genetic modifications, play crucial role providing prior information and serving as a reference to distinguish true perturbation effects from other sources of variation. They help calibrate non- perturbattio factors, such batch -yteettssyytttatiie biases unrelated to perturbations, including variations in color intensity, akin to distribution shifts in machine learning. Properly incorporating control wells is essential for capturing actual perturbation effects rather than artifacts, yet many existing methods overlook this aspect (Yang et al., 2021; Navidi et al., 2025; Cook et al., 2024). To address this, we frame cellu- lar morphology prediction as distribution-to-distiibbbu
1. Introduction
Building a virtual cell that simulates cellular behaviors in silico has been a longstanding dream computational biol- ogy (Slepchenko al., 2003; Johnson al., 2023; Bunne al., 2024). Such a system would revolutionize drug dis- covery by rapidly predicting how cells respond to new com- pounds or genetic modifications, significantly reducing the
Equal contribution 'Stanford University 'Tsinghua University P *MIT. Correspondence to: Yuhui Zhang <yuhuiz @ stanford.edux. Screna Yeung- Levy <syyeung stanford.edu>>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
(a)
(b)
Control Cells
g
Perturbed Celle
MD
Batch A
Batch
Batch
(c)
Control Distribution
(d)
Phentiif
CelFlux
Distribution
Perturbed
Overal
MAA
Batch
Batch
Conditio
Batch
FID
70.2
(
CellFlux
Perturbed
MoA
Distribution
Perturbed Distribution
Control Cell Images
Perturbed Cell Images
Results BBBC021
Figure 1. Overview of CellFlux. (a) Objective. CellFlux aims to predict changes in cell morphology induced by chemical or gene perturbations in silico. this example, the perturbation effect reduces the nuclear size. (b) Data. The dataset includes images from high-content screening experiments, where chemical or genetic perturbations perturbations. Control wells provide prior information to contrast with target images, cnabling the identification true perturbation effects (e.g.. reduced nucleus size) while calibrating non pentpeeruubbtinn artifacts such batch effeectsSsyttnnn perturbation (e.g.. variations in color intensity). (c) Problem formulation. We formulate the task as a distribution
applied target wells, alongside control wells without
biases unrelated to the cettrruuttio-to-iissribie to-distribution problem (many-to manny mapping). where the source distribution consists of control images, and the target distribution contains perturbed images within the same batch. (d) Flow matching. CellFlux employs flow matching, a state-of-the-art generative approach for distribution-to- distribution problems. It learns neural network approximate velocity field, continuously transforming the source distribution into the target by solving an ordinary differential equation (ODE). (e) Results. CellFlux significantly outperforms baselines in image generation quality, achieving lower Fréchet Inception Distance (FID) and higher classification accuracy for mode-of-action (MoA) predictions.
mapping problem (Figure lc), where the source distribution consists of control cell images, and the target distribution comprises perturbed cell images from the same batch.
of-action compared to existing methods (Figure 1e). Im- portantly, CellFlux maintains consistent performance across diverse experimental conditions and generalizes to held-out perturbations never seen during training, showing its broad applicability.
To address this CellFlux employs flow matching, a state-of-the-ar gen- erative modeling approach designed for distribution-wii transformations (Figure 1d). The framework continuously transforms the source distribution into the target using an ordinary differential equation (ODE) by learning neural network approximate a velocity field. This direct and native distribution transformation enabled by flow matching is intuitively more effective than previous methods, which rely on adding extra components to GANs, incorporating the source condition, or mapping between distributions and noise using diffusion models (Palma al., 2025; Hung al., 2024; Bourou et al., 2024).
problem,
Moreover, CellFlux introduces two key capabilities with sig- nificant potential for biological research (Figure 4). First, effectively corrects batch effects by conditioning on control cells from different batches. By comparing control images with generated images, it can disentangle true perturbation- induced morphological changes from experimental batch artifacts. Second, CellFlux enables bidirectional interpo- lation between cellular states due to the continuous and reversible nature of the velocity field in flow matching. This interpolation provides a means explore intermediate cel- lular morphologies and potentially gain deeper insights into dynamic perturbation responses.
We demonstrate the effectiveness of CellFlux on three datasets: BBBC021 (chemical perturbations) (Caie et al., 2010), RxRx1 (genetic modifications via CRISPR or ORF) (Sypetkowski et al., 2023), and JUMP (combined chemical and genetic perturbations) (Chandrasekaran al., images of cellular changes in response to perturbations across all datasets, im- proving FID scores by 35% over previous approaches. The generated images capture meaningful biological patterns, demonstrated by a 12% improvement in predicting mode-
In summary, by formulating cellular morphology predic- tion as a iistribution-to-diittibuuoo problem and using flow matching as solution, CellFlux enables accurate predic- tion of perturbation responses (Figure 1). CellFlux not only achieves state-of- soff-thhartt performance but unlocks new capa- bilities such handling batch effects visualizing cellular state transitions, significantly advancing the field towards virtual cell for drug discovery and personalized therapy.
2023). CellFlux generates
2

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
2. Problem Formulation
ages from thousands of perturbation conditions (Pay et al., 2023; Chandrasekaran et al., 2023).
In this section, we introduce the objective, data, and mathe- matical formulation of cellular morphology prediction.
However, the cell painting process has limitations: cell painting requires cell fixation, which is destructive, making it impossible to observe the same cells dynamically during a perturbation. This creates a fundamental constraint: we cannot obtain paired samples {(20, Ø1)} showing the exact same cell without and with treatment. Instead, we must work with unpaired data (fro): ) {a1 }), where { represents control images and { represents treated images, to learn the conditional distribution p(z) (20.c).
2.1. Objective
Let X' denote the cell image space and C the perturbation space. Let Po represent the original cell distribution and p1 represent the distribution of cells after a perturbation E C. Cellular morphology prediction aims to learn a generative model pa: X' x C + P()), which, given an unperturbed cell image Zo ~ Po and a perturbation с E C, predicts the resulting conditional distribution p c). From this distribution, new images can be sampled to simulate the effects of the perturbation, such that #1 ~ Pi (Figure la).
One solution is to leverage the distribution transformation from control cells to perturbed cells within the same batch to learn conditional generation. Control cells serve as crucial reference by providing prior information separate true perturbation effects from confounding factors such batch effects. Variations in experimental conditions across differ- ent runs (batches) introduce systematic biases unrelated to the perturbation itself. For instance, images from one batch may consistently differ in pixel intensities from those in an- other. Therefore, meaningful comparisons require analyzing treated and control samples from the same batch. As shown in Figure 1b, this approach helps distinguish true biological responses, like changes in nuclear size, from batch-specific artifacts, like changes in color.
The input space consists of multi-channel microscopy im- ages, where X' C # Here, H and W represent the image height and width, while C' denotes the number of channels, each highlighting different cellular components through specific fluorescent markers (analogous to RGB channels in natural images, but capturing biological struc- tures like mitochondria, nuclei, and cellular membranes).
The perturbation space C includes two types of biological interventions: chemical (drugs) and genetic (gene modifi- cations). Chemical perturbations involve compounds that target specific cellular processes - for example, affecting DNA replication or protein synthesis. Genetic perturbations can turn off gene expression (CRISPR) or upregulate gene expression (ORF).
2.3. Mathematical Formulation
B
C
This generative model enables in silico simulation of cellular responses, which traditionally require time-intensin and costly wet-lab experiments. Such computational modeling could revolutionize drug discovery by enabling rapid virtual drug screening and advance personalized medicine through digital cell twins for treatment optimization.
Xo
Xo
X1
Let us formalize our learning problem in light of the ex- perimental constraints described before. Our objective is to learn a conditional distribution p(z, b c) that models the cellular response to perturbation. However, due to the destructive nature of imaging, we cannot observe paired samples (1)) We propose probabilistic graphical model to address this challenge.
2.2. Data
Cell morphology data are collected through high high-content microscopy screening (Figure lb) (Perlman et al., 2004). In this process, biological samples are prepared in multi-well plates containing hundreds of independent experimental units (wells). Selected wells receive interventions ci- ther chemical compounds or genetic modifications - while control wells remain unperturbed. After designated pe- riod, cells are fixed using chemical fixatives and stained with fluorescent dyes to highlight key structures like the nucleus, cytoskeleton, and mitochondria. An automated microscope then captures multiple images per well. This process is called cell painting. Modern automated high- content screening systems have enabled large-scale data collection, resulting in datasets of terabyte to petabyte im-
In our graph, random variable B denotes the experimental batch, C' denotes the perturbation condition, X, represents the unobservable basal cell state, X, represents control cells from the same batch, and X, denotes the perturbed cell state. From our experimental setup, we have access the control distribution p( (b) from unperturbed cells and the perturbed distribution p(r) from treated cells.
We propose to leverage the distributional transition from p( b) to p(2,/c, |c. b) to learn the individual-level trajec- tory p(21 (oo c), as shown in Figure Ic. There are two key reasons. First, there exists a natural connection be-
3

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
(a) Training Process
(b) Inference Process
Xo ~ Po
Pe
A
Control Images
va(ke t, c)
t- U|0
0.0
(x)
Xo.1 + Va(Xs, 0.0, c)
Xaa = Xo.1 + Vo 0.1, c)
(1-t)xo +
Pi
Perturbed Cell images
0.9
t 0.2
Figure 2. CellFlux algorithm. (a) Training. The neural network to learns velocity field by fitting trajectories hetween control cell images (ro ~ Po) and perturhed cell images (I1 pi). At each training step, intermediate states Iy are sampled along the lincar interpolation between 2p and ø1, with predicted velocity Us (Ie, i, e) and the true velocity (x1 zo). (b) Inference. The trained velocity field vo guides the transformation of a control cell state zo into perturbed cell state æ1. This achieved by solving an ordinary differential equation iteratively, using numerical integration steps over time (e.g.. & 0.0, 0.1, 0.2, 0.9, 1.0). Each step updates the cell state using the learned velocity field.
[1 1]. The network minimizes the loss L. which measures the difference between the
tween p(an |c, b) and p(zolb) through the marginalization p(z1 (c. b) = p(z1 c)p(20 bb)xo. Second, while p(ro b) is not directly tractable, we can approximate it us- ing p(2b)b) since both the ground-truth X, distribution and control distribution X, follow the same batch-conditional distribution: z0 p(-16) and zo p(-16).
tions by constructing smooth paths between paired samples (Figure 1d). It models how a source distribution continu- ously deforms into a target distribution through time, similar to morphing one shape into another.
More formally, consider probability distributions po and Pi defined samples from these distributions, flow matching learns time-dependent velocity field using a neural network Ug X x [0, 1] + X' that describes the instantancous direc- tion and magnitude of change at each point. The transfor- mation process follows an ordinary differential equation:
metric space (X' d). Given pairs of
Our approach of learning p(z1 c) by conditioning same-batch control images improves upon existing methods that ignore control cells and learn only p(zile). Intuitively, conditioning on dn allows the model to initiate the transition from a distribution more closely aligned with the underlying To, leading to better approximation of the true distribution p(a0 (ro, c). We formalize this intuition in the following proposition, with proof provided in Appendix A:
dx. = (Fe, t)dl, 20 ~ po-
In ~ PI, LE [0,
Proposition 1. Given random variables B, C, Xo, Xo. and X, following the graphical model above with joint distribution p(b, c, To, Eo, 21), the distribution p(zı |2o. c) can be better approximated by the conditional distribution p(a1 120, e) than p(z1 (c) in expectation. Formally,
During training, we construct probability path that con- nects samples from the source (po) and target (pi distribu- tions (Figure 2a). We employ the rectified flow formulation, which yields simple straight-line path (Liu et al., 2023):
= (1 - t)æ0 + tx t ~ [0
Fo(so [Dz (zo, c))lp(x1|/0 c))l < <Pppoo.)) (p(x: ( c)(pp())(e))
This linear path has a constant velocity field v(F,, = dz:/dt = x - xp. which represents the optimal transport direction at each point. The neural network vs is trained to approximate this optimal velocity field by minimizing:
3. Method
As detailed in $2, we predict cell morphological changes by transforming distributions between control and perturbed cells under specific conditions within the same batch. In this section, we introduce CellFlux, which leverages flow match- ing, a principled framework for learning continuous trans- formations between probability distributions. We adapt flow matching with condition, noise augmentation, and classifier- free guidance to better address our problem setting.
[())
() v(x,, 1)
At inference time, given sample zo Po. we generate I1 by solving the ODE (Figure 2b), whose solution is:
I1 xo / vo( t)dt
3.1. Preliminaries
Flow matching (Lipman et al., 2023; 2024) provides a frame- work to learn transformations between probability distribu-
We employ numerical integrators like Euler method or more advanced methods such Runge- Kutta to solve the ODE,

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
(a) Main Results
BBBC021 (Chemical)
RxRxI (Genetic)
JUMP (Combined)
Method
FID,
FID:
KID.
KID.
FID.
FID:
KID,
KID:
FID.
FID.
KID.
KID:
PhenDiff (MICCAT'24)
49.5
109.2
3.10
3.18
65.9
174.4
5.19
5.29
49.3
127.3
5.09
5.17
IMPA (Nature Comm 25)
33.7
76.5
2.60
2.70
41.6
164.8
2.91
2.94
14.6
99.9
1.08
1.06
CellFlux
18.7
56.8
1.62
1.59
33.0
163.5
2.38
2.40
9.0
84.4
0.63
0.65
(b) Per Perturbation Results
Chemical Perturbations
Genetic Perturbations CRISP3
Method
Alsterpaullone
AZ138
Bryostatin
Colchicine
Mitomycin C
PP-2
ACSS1
RASD1
PhenDiff (MICCAI'24)
106.6
120.0
106.9
111.2
110.0
121.7
157.5
144.6
180.4
IMPA (Nature Comm 25)
69.6
59.9
104.3
84.4
57.0
77.3
152.6
142.7
147.1
CellFlux
41.6
44.4
47.0
72.3
42.3
64.3
140.9
125.1
140.1
Table Evaluation of CellFlux. (u) Main results. CellFlux outperforms GAN- and diffusion- baselines, achieving state-of-the-art performance in cellular morphology prediction across three chemical, genetic, and combined perturbations datasets. Metrics measure the distance between generated and ground-truth samples, with lower values indicating hetter performance. FID,, (overall FID) evaluates all images, while FID,, (conditional FID) averages results per perturhation c. KID values scaled by 100 for visualization. (b) Per perturbation results. For six representative chemical perturbations three genetic perturhations, CellFlux generates significantly more accurate images that better capture the perturbation effects than other methods, measured by the FID score.
3.2. Conditional Flow Matching
where e o This noise augmentation helps prevent overfitting to discrete samples and encourages the model learn continuous velocity field in the ambient space. The noise scale and probability Pa are hyperparam- eters that control the smoothness of the learned field.
To model perturbation conditions, we extend flow matching by conditioning on perturbations c E C. While the source distribution Po represents unperturbed cell images, the target distribution now becomes condition- -eneiiion-deeeddentt, denoted as Pı (x/c). Our goal is to learn a conditional velocity field vy : X' x (o, 1] x C + X' that captures perturbation-specifi transformations (Esser et al., 2024):
3.5. Neural Network Architecture
The velocity field uo is realized through U-Net architec- ture (Ronneberger et al., 2015). as we directly model the distribution in image pixel space X' C H # where U-Net captures both local and global features through its multi-scale structure. Time is encoded using Fourier fea- tures, and condition e C is embedded through learnable network E C + Rd. These embeddings are added to form the condition signal, which is then injected into the U-Net blocks to guide the generation process (Esser et al., 2024).
dx, = (0 L, c,dd,, 2o ~ Po. 3 ~ pi(cc)
3.3. Classifier-Free Guidance
We incorporate classifier-free guidance (Ho & Salimans, 2022: Zheng et al., 2023) to improve generation fidelity. During training, we randomly mask conditions with prob- ability Po. replacing e with null token 0. At inference time, we interpolate between conditional and unconditional predictions:
The entire CellFlux algorithm is summarized in $B.
4. Results
CFG 0g
tx,, a oo(x,, + (1 (1-a) a) vo(ss, 0)
In this section, present detailed results demonstrating CellFlux's state-of-the- tht-hhe-rtt performance in cellular morphol- ogy prediction under perturbations, outperforming existing methods across multiple datasets and evaluation metrics.
where a > controls guidance strength.
3.4. Noise Augmentation
Since Po and are both empirical distributions from datasets with limited observations, direct mapping between them may lead to bad generalization. Therefore, we propose augmenting the samples to make the learned velocity field smoother. This is done by adding random Gaussian noise to To Po with a probability Pe- Formally:
4.1. Datasets
Our experiments were conducted using three cell imag- ing perturbation datasets: BBBC021 (chemical pertur- bation) (Caie et al., 2010), RxRx1 (genetic perturba- tion) (Sypetkowski et al., 2023), and the JUMP dataset (combined perturbation) (Chandrasekaran al., 2023). We followed the preprocessing protocol from IMPA (Palma et al., 2025), which involves correcting illumination, crop-
To +
with probability Pe
To
To.
with probability Pe
5

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
(a) Mode of Action Classification
Method
MoA Accuracy
MoA Macro-F1
MoA Weighted-F1
Groundtruth Perturbed Image
72.4
69.7
72.1
PhenDiff
52.6
33.6
52.1
IMPA
63.7
40.2
64.8
CellFlux
71.1
49.0
70.7
(b)
citto--Disttibution Generalization
Method
FID,
FID:
KID,
KTD,
MoA Accuracy
MoA Macro-F1
MoA Weighted-F1
Groundtruth Perturbed Image
0.0
0.0
0.0
0.0
88.0
85.0
88.0
PhenDiff
67.7
151.6
3.45
3.71
9.6
9.3
IMPA
44.5
136.9
3.07
3.24
16.0
10.0
13.1
CellFlux
42.0
98.0
1.31
1.23
43.2
36.6
42.8
(c) Batch Effect Study
Method
FID.
FID:
KID.
KID:
MoA Accuracy
MoA Macro-Fl
MoA Weighted-FI
CellFlux w/ Other Batch Init
19.9
66.3
1.70
1.69
48.2
32,9
48.4
CellFlux
18.7
56.8
1.62
1.59
71.2
49.0
70.7
(d) Ablation Study
Method
FID.
FID;
KID,
KID₂
CellFlux w/o Condition
45.0
113.0
2.37
2.37
CellFlux w/o CFG
32.6
92.4
1,23
1.35
CellFlux w/o Noise
31.9
91.4
1.24
1.26
CellFlux
18.7
56.8
1.62
1.59
Table More evaluation and ablation of CellFlux. (a) MoA classification. On the BBBC021 dataset, train classifier predict the drug's mode of action (MoA) from cell morphology images and evaluate the accuracy/F1 of generated images, CellFlux achieves significantly higher accuracy/Fl than other methods, closely aligning with ground truth images and effectively reflecting the biological effects of perturbations. (b) Out-of distribution generalization. CellFlux maintains strong performance when generating cell morphology images for novel chemical compounds not seen during training on BBBCO21. (c) Batch effect study. CeilFlax shows improved performance when using control images from the same batch initialization, highlighting the critical role of control images in calibrating batch effects. (d) Ablation study. Removing key components degrades CellFhux's performance, emphasizing their importance.
ping images centered on nuclei to resolution of 96x96, and filtering out low-quality images. The resulting datasets include 98K. 171K, and 424K images with 3, 6, and 5 chan- nels, respectively, from 26, 1,042, and 747 perturbation types. Examples of these images are provided in Figure 3. Details of datasets are provided in SE.
Baselines. We compare our approach against two base- lines, PhenDiff (Bourou et al., 2024) and IMPA (Palma et al., 2025), the only two baselines that incorporate control images into their model design - crucial setup for dis- tinguishing true perturbation effects from artifacts such as batch effects. PhenDiff uses diffusion models to first map control images to noise and then transform the noise into target images. In contrast, IMPA employs GANs with an AdaIN layer to transfer the style of control images to tar- get images, specifically designed for paired image-to-image mappings. Our method uses flow matching, which tai- lored for baaobbuiionto-iittriuuittte more suitable solution for our problem. We reproduce these basclines with official codes.
4.2. Experimental Setup
Evaluation metrics. We evaluate methods using two types of metrics: (1) FID and KID (lower the better), which mea- sure image distribution similarity via Fréchet and kemel- based distances, computed on 5K generated images for BBBCU21 and 100 randomly selected perturbation classes for RxRx1 and JUMP; we report both overall scores across all samples and conditional scores per perturbation class. (2) Mode of Action (MoA) classification accuracy and Fl score (higher the better), which assesses biological fidelity by using trained classifier to predict a drug's effect from perturbed images and comparing it to its known MoA from the literature.
mapping, providing a
Training details. CellFlux employs UNet-based velocity field with a four-stage design. Perturbations are encoded following IMPA (Palma et al., 2025). Training is conducted for 100 epochs on A100 GPUs. Details are in $C.
6

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Mevinolin Lovastatin
Floxuridine
AZ138
Demecolcine
Mitomycin C
IT2A
ACSS1
CYP2A6
Taladegib
Control Images
PhenDiff
IMPA
CellFlux
Perturbed
Images
Figure 3. Qualitative comparisons. CellFlux generates significantly more accurate images that reflect the actual biological effects of perturbations compared to baselines. For example, Floxuridine inhibits DNA replication, leading to reduced cell density; AZ138 an Eg5 inhibitor, causing cell death and shrinkage; Demecolcine destabilizes microtubules, resulting in smaller, fragmented nuclei. Columns 1-5, 6-7, and 8-9 correspond to samples from the BBBC021, RxRx1, and JUMP datasets, respectively. More drug's 's mode-of-action $E.
4.3. Main Results
using generated cell images. MoA describes how a drug affects cellular function and can be inferred from morphol- ogy. To assess this, train image classifier real perturbed images and test it on generated ones. CellFlux achieves 71.1% % MoA accuracy, closely matching real im- ages (72.4%) and significantly surpassing other methods (best: 63.7%), demonstrating its ability to maintain biologi- fidelity across perturbations. Qualitative comparisons in Figure 3 further highlight CellFlux's 's accuracy in capturing key biological effects. For example, demecoleine produces smaller, fragmented nuclei, which other methods fail to reproduce accurately.
CellFlux generates highly realistic cell images. CellFlux outperforms existing methods in capturing cellular morphol- ogy across all datasets (Table la), achieving overall FID scores of 18.7, 33.0, and 9.0 on BBBC021, RxRx1, and JUMP, respectively improving FID by pared to previous methods. These gains in both FID and KID metrics confirm that CellFlux produces significantly more realistic cell images than prior approaches.
com-
CellFlux accurately captures perturbation-specte phological changes. As shown in Table 1a, CellFlux achieves conditional FID scores of 56.8 (a 26% improve- ment), 163.5, and 84.4 (a 16% improvement) on BBBC021, RxRx1, and JUMP, respectively. These scores are computed by measuring the distribution distance for each specific per- turbation and averaging across all perturbations. Table further highlights CellFlux's performance on six representa- tive chemical and three genetic perturbations. For chemical perturbations, CellFlux reduces FID scores by 14-55% com- pared to prior methods. The smaller improvement (5-12%) 15%% improvements) on RxRx1 is likely due to the limited number of images per perturbation type.
mor-
CellFlux generalizes to out-of-distribution (OOD) per- turbations. On BBBC021, CellFlux demonstrates strong generalization novel chemical perturbations never seen during training (Table 2b). It achieves 6%, 28%, and 170% improvements in overall/conditional FID and MoA accuracy over the best bascline. OOD generalization is critical for biological research, enabling the exploration of previously untested interventions and the design of new drugs.
Ablations highlight the importance of each component in CellFlux. Table 2d shows that removing conditional information, classifier-free guidance, noise augmentation significantly degrades performance, leading to higher FID scores. These underscore the critical role of each component in enabling CellFlux's of-te-of-heeaas performance.
CellFlux preserves biological fidelity across perturba- tion conditions. Table 2a presents mode of action (MoA) classification accuracy and F1 on the BBBC021 dataset

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
(a)
(b)
(c)
Control Images
Predicted Images
Interpolation
Perturbed Images
Figure 4. CellFlux enables new capabilities. (a. 1) Batch effect calibration. CellFlux initializes with control images, enabling batch- specific predictions. Comparing predictions from different batches highlights actual perturbation effects (smaller size) while filtering out spurious batch effects (cell density variations). (a.2) Interpolation trajectory. CellFlux's learned velocity field supports interpolation between cell states, which might provide insights into the dynamic cell trajectory. (b) Diffusion model comparison. Unlike flow matching, diffusion models that start from noise cannot calibrate batch effects support interpolation. (c) Reverse trajectory. CellFlux's reversible velocity field can predict prior cell states from perturbed images, offering potential applications such as restoring damaged cells.
4.4. New Capabilities
given source images (Figure 4a). This suggests a possi- ble approach for simulating morphological trajectories dur- ing perturbation response, which diffusion methods cannot achieve (Figure 4b). Additionally, the reversible distribu- tion transformation learned through flow matching enables CellFlux to model backward cell state reversion (Figure 4c), which could be useful for studying recovery dynamics and predicting potential treatment outcomes.
CellFlux addresses batch effects and reveals true per- turbation effects. CellFlux's distributiio-ttodisttibbuu approach effectively addresses batch effects, a significant challenge in biological experimental data collection. As shown in Figure 4a, when conditioned on distinct control images with varying cell densities from different batches, CellFlux consistently generates the expected per- turbation effect (cell shrinkage due to mevinolin) while recapitulating speciic artifacts, revealing the true per- turbation effect. Table further quantifies the importance of conditioning on the same batch. By comparing gener- ated images conditioned control images from the same or different batches against the target perturbation images, we find that same-bare batch conditioning improves conditional FID and MoA accuracy by 14% and 48%. This highlights the importance modeling control images to more accu- rately capture true perturbation effects-an aspect often overlooked by prior approaches. such as diffusion models that initialize from noise (Figure 4b).
5. Related Works
Generative models. Generative models fundamental class of machine learning approaches that learn to model and sample from probability distributions. Traditional meth- ods such as autoregressive models, normalizing flows, and GANs face limitations in generation speed, expressive- ness, or training stability (Van Den Oord et al., 2016; Pa- pamakarios al., 2021; Goodfellow et al., 2014). Re- cent score-based approaches, particularly diffusion mod- els
et al., 2015; Song & Ermon, 2019; Ho et al., 2020) and flow matching (Lipman et al., 2023; 2024; Liu et al., 2023; 2024), address these challenges by learn- ing continuous-time transformations between distributions, achieving state-of-the- performance in generating images, videos, and biological sequences (OpenAI, 2024; Esser al., 2024; Kondratyuk al., 2024; Hayes al., 2025). Unlike diffusion models, which map from Gaussian noise, flow matching directly transforms between arbitrary distributions. This property remains underexplored in machine learning due to limited application scenarios (Liu et al., 2024), yet it
CellFlux has the potential to model cellular morpho- logical change trajectories. Cell trajectories could offer valuable information about perturbation mechanisms, but capturing them with current imaging technologies remains challenging due to their destructive nature. Since CellFlux continuously transforms the source distribution into the get distribution, it can generate smooth interpolation paths between initial and final predicted cell states, producing like sequences of cellular transformation based on
8

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
is particularly well-suited for cellular morphology predic- tion, where accurately modeling the transition from unper- turbed to perturbed cell states is crucial.
Acknowledgments
This work is partially supported by the Hoffman- Yee Re- search Grants, E.L. and S.Y. are Chan Zuckerberg Biohub San Francisco Investigators.
Cellular morphology prediction. Cellular morphology serves as a powerful phenotypic readout in biological re- search, offering critical insights into cellular states (Perlman al., 2004; Loo et al., 2007). Predicting morphological changes in silico enables rapid virtual drug screening and the development of personalized therapeutic strategies, sig- nificantly accelerating biomedical discoveries (Carpenter, 2007; Bunne et al., 2024). While initial progress has been made in this direction, existing approaches face three ma- jor limitations. Some neglect control cell images, failing to capture true perturbation changes and making predic- tions vulnerable batch effects (Yang al., 2021; Navidi et al., 2025; Cook et al., 2024). Others rely on outdated generative techniques such normalizing flows and GANs, which suffer from training instability and limited image fidelity (Lamiable et al., 2023; Palma et al., 2025), Addi- tionally, some methods use suboptimal approaches to model distribution transformation, such as a two-step diffusion process (Bourou al., 2024; Hung et al., 2024). Our work addresses these challenges by reframing morphology predic- tion distribution-te toaon--tto-sstuuttiioo translation problem and leveraging flow matching, which naturally models cellular state transformations while ensuring high image quality and stable training, paving the way for constructing virtual cells for biomedical research.
Impact Statement
CellFlux introduces a novel machine learning framework for modeling cellular responses to genetic and chemical perturbations by formulating the task as distribution-to distribution transformation and solving it using a principled flow matching approach. This leads significantly im- proved predictive performance and unlocks new capabilities such as batch effect correction and perturbation interpola- tion.
By providing scalable and interpretable computational tools for modeling perturbation responses both the single-cell and population levels, CellFlux addresses critical challenges in experimental biology. It enables rapid in-silico screen- ing of compounds and perturbations, thereby accelerating therapeutic discovery and drug repurposing. In particular, it can guide follow-up experiments toward the most promising candidates, streamlining the drug repurposing pipeline and the search for novel therapeutic targets. In addition to medi- cal applications, CellFlux can accelerate basic research into cell biology processes by modeling responses to genetic chemical perturbations.
However, we acknowledge that these are early attempts to model complex and dynamic biological systems, and fu- ture research with larger and more diverse datasets will improve performance. For instance, we are limited by cur- rent datasets that focus on few cancer cell lines, which could introduce bias and may not fully represent normal physiology. Furthermore, while our method enables inter- polation between cell states, the biological validity of these interpolations remains unverified; establishing their plausi- bility will require future work involving ground-trutt data and experimental validation.
6. Conclusion
In this work, we introduce CellFlux, method that leverages flow matching to generate cell images under various pertur- bations while capturing their trajectories, paving the way for the development of a virtual cell framework for biomedical research. In future work, we plan to scale up CellFlux to process terabytes of imaging data encompassing diverse cell types and wide range of perturbations, enabling the full potential of virtual cell modeling.
Despite these limitations, CellFlux bridges machine learning and cellular biology, enabling new frontiers in virtual cell modeling, drug discovery, and systems biology research with broad implications for science and medicine.

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
References
Hung, A. Z., Zhang, C. J., Sexton, J. Z., O'Meara, M. J., and Welch, J. D. Lumie: Latent diffusion for multiplexed images of cells. bioRxiv, pp. 2024-11, 2024.
Bourou, A., Boyer, T., Gheisari, M., Daupin, K., Dubreuil, V., De Thonel, A., Mezger, V., and Genovesio, A. Phen- diff: Revealing subtle phenotypes with diffusion models in real images. In MICCAI, 2024.
Johnson, G. T., Agmon, E., Akamatsu, M., Lundberg. E., Lyons, B., Ouyang, W., Quintero-Carmona, O. A., Riel- Mehan, M., Rafelski, S., and Horwitz, R. Building the next generation of virtual cells to understand cellular biology. Biophysical Journal, 2023.
Bunne, C., Roohani, Y., Rosen, Y. Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi M., Brennan, P., Burkhardt, D. B., et al. How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell, 2024.
Katsoulakis, E., Wang, Q. Wu, H., Shahriyari, I.., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., Xiao, Y., al. Digital twins for health: scoping review. upj Digital Medicine, 2024.
Caie, D., Walls, R. E., Ingleston-Orme, A., Daya, S., Houslay, T., Eagle, R., Roberts, M. E., and Carragher, N. O. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Molecular Cancer Therapeutics, 2010.
Kondratyuk, D., Yu, L., Gu, X., Lezama, J., Huang, J., Schindler, G., Hormung. R., Birodkar, V., Yan, J., Chiu, M.-C., Somandepalli, K., Akbari. H., Alon, Y., Cheng, Y., Dillon, J. V., Gupta, A., Hahn, M., Hauth, A., Hendon, D., Martinez, A., Minnen, D., Sirotenko, M., Sohn, K., Yang, X., Adam, H., Yang, M.-H., Essa, I., Wang, H., Ross, D. A., Seybold, B., and Jiang, L. VideoPoet: large language model for zero-shot video generation. In ICML, 2024.
Carpenter, A. E. Image- based chemical screening. Nature Chemical Biology, 2007.
Chandrasekaran, S. N., Ackerman, J., Alix, E., Ando, D. M., Arevalo, J., Bennion, M., Boisseau, N., Borowa, A., Boyd, D., Brino, L.,, et al. Jump cell painting dataset: morpho- logical impact of 1 33,000 chemical and genetic perturba- tions. BioRriv, pp. 2023-02 03, 2023.
Lamiable, A., Champetier, T., Leonardi, F., Cohen, E., Som- mer, P., Hardy, D., Argy, N., Massoughodji. A., Del Nery, E., Cottrell, G., al. Revealing invisible cell phenotypes with conditional generative modeling. Nature Communi- cations, 2023.
Cook, S., Chyba, J., Gresoro, I.., Quackenbush, D., Qiu, M., Kutchukian, P., Martin, E. J., Skewes-Cox, P., and Godinez, W. J. A diffusion model conditioned on com- pound bioactivity profiles for predicting high-content im- ages. bioRxiv, pp. 2024 2024.
Lipman, Y., Chen, R. T., Ben- H., Nickel, M., and Le, M. Flow matching for generative modeling. In ICLR, 2023.
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F,, et al. Scaling rectified flow transformers for high-resolution image synthesis. ICML., 2024.
Lipman, Y., Havasi, M., Holderrieth, P., Shaul, N., Le, M., Karrer, B., Chen, R. T., Lopez-Paz, D., Ben-Hamu, H., and Gat, Flow matching guide and code, arXiv preprint arXiv:2412.06220 2024.
Fay, M. M., Kraus, O., Victors, M., Arumugam, L., Vuggu- mudi, K., Urbanik, J., Hansen, K., Celik, S., Cernek, N., Jagannathan, G., et al. Rxrx3: Phenomics map of biology. Biorxiv, pp. 2023-02, 2023.
Liu, Q., Yin, X., Yuille, A., Brown, A., and Singh, M. Flowing from words pixels: A framework for cross- modality evolution. arXiv preprint arXiv:2412.15213. 2024.
Goodfellow, I., Pouget-Abadie J., Mirza, M., Xu, B., Farde-Farley, D., Ozair, S., Courville, A., and Bengio, Generative adversarial nets. In NIPS, 2014.
Liu, X., Gong, C., and Liu, Q. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR, 2023.
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, 7.,, Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. Simulating 500 million years evolution with a language model. Science, 2025.
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nature Methods, 2012.
Ho, J. and Salimans, T. Classifier-free diffusion guidance. arXiv preprint arXiv:2207. /25.9, 2022.
Loo, L.-H,, Wu, L. F., and Altschuler, S. J. Image- based multivariate profiling of drug responses from single cells. Nature Methods, 2007.
Ho, J., Jain, A., and Abbeel, P. Denoising diffusion proba- bilistic models. In NeurlPS, 2020.
10

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Navidi, Z., Ma, J., Miglietta, E. A., Liu, L.,, Carpenter, A. E., Cimini, B. A., Haibe-Kains, B., and Wang, B. Morphodiff: Cellular morphology painting with diffusion models. In ICLR, 2025.
OpenAl. Gpt-4 technical report, 2024. URL https:/ // arxiv. org/ abs 087744
Palma, A., Theis, J., and Lotfollahi, M. Predicting cell morphological responses perturbations using genera- tive modeling. Nature Communications, 2025.
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. Normalizing flows for probabilistic modeling and inference. JMLR, 2021.
Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., and Altschuler, S. J. Multidimensional drug profiling by automated microscopy. Science, 2004.
Ronneberger, O., Fischer, P., and Brox, T. U-net: Convolu- tional networks for biomedical image segmentation. In
MICCAI, 2015.
Slepchenko, B. M., Schaff, J. C., Macara, I., and Loew, L. M. Quantitative cell biology with the virtual cell. Trends in cell biology, 2003.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. Deep unsupervised learning using nonequi- librium thermodynamies. In ICML, 2015.
Song, Y. and Ermon, S. Generative modeling by estimating gradients of the data distribution. In NeurlPS, 2019.
Sypetkowski, M., Rezanejad, M., Saberian, S., Kraus, O., Urbanik, J., Taylor, J., Mabey, B., Victors, M., Yosinski, J., Sereshkeh, A. R., et al. Rxrx1: A dataset for evaluating experimental batch correction methods. In CVPR, 2023.
Van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K. Pixel recurrent neural networks. In ICML, 2016.
Yang, K., Goldman, S., Jin, W., La, A. X., Barzilay, R., Jaakkola, T., and Uhler, C. MolZimage: improved condi- tional flow models for molecule to image synthesis. In CVPR, 2021.
Zheng, Q., Le, M., Shaul, N., Lipman, Y., Grover, A., and Chen, R. T. Guided flows for generative modeling and decision making. arXiv preprint arXiv:2311.1344. 2023.

---

Cellflux: Simulating Cellular Morphology Changes via Flow Matching
Summary of Appendix
SA presents formal proof supporting our mathematical formulation.
SB details the CellFlux algorithm.
SC provides additional experimental details.
SD offers more in-depth discussion of batch effects.
SE describes the datasets used in our study.
SF includes qualitative comparisons of CellFlux against baselines.
SG presents additional visualization of bidirectional trajectories between control images and perturbed images.
SH provides additional results comparing our method to baselines.
SI provides a table to compare our work with related works.
JJ discusses future directions for validating the interpolation trajectories generated by CellFlux.
12

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
A. Theory Proof
B
C
Xo
Xo
X1
Proposition 1. Given random variables B, C, Xo, Xu, and X1 following the graphical model above with joint distribution p(b, c, Fo, Fo, )1), the distribution p(zi)e (ro, c) can be better approximated by the conditional distribution p(a1/zo, than p(z1(e) in expectation. Formally,
Fp. [Dzz(p(z1 (ro, c))lp())lpp)))) c))l S Ep(ao.e) [Dx(((<1] c)lp
Proof. According to the definition of conditional mutual information, the term on the right-hand side can be expressed as:
c)(so.c) (p(x, (zo, (=o, c)lpp(r1|e)] = p(zo. c)p(a (ro, c) log p(z1|zo c)
doy dxode
p(ri
plc) lc) log p de
p(zo)c)p(zo(c)
= Eptc) (p( cole)l
((X1)
Based on the graphical model, we have the conditional independence X1 1 Xo Xo, C. Thus, we have p(z1 p(a1 (2o, For c). Similarly, express the term on the right-hand side conditional mutual information:
E Eo (p(x ao e) [p(2) (20, c))l Fp(so. [DRL (p(x) ( (zo, Fo. ( c))l
p(r, ao Fo, c) = f p( Eo, c)p(11 No So. c) log pl01|20,0 c)
dx doyddddd
Xo/o:
Further, based on the property conditional mutual information, we have
XolC) = () [(x; C) I(xi; Xo, C) 1(X1;
where the second equality is due to the conditional independence relationship X, Nol C, and I(X1; Xol C) = Therefore,
Frieo.e) (p(mp(( c)(lp(())))))))))) [c))] Ep( [DK. (p(ai (ma, e)( fo, c)) T(X;; XolC)
[Ka (p(m pmn, ()((p( c)]]
The inequality holds strictly when I(X;; X,(C) > 0, i..., X1 1L X C, which generally holds true when batch effect exists and variables Xo and X are associated by B according to the graphical model.
13

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
B. CellFlux Algorithm
Algorithm CellFlux Algorithm
Training Process:
input Initial distribution Po. target distribution perturbation c, neural network uh (2x, 6, c), noise injection probability Pa condition drop probability Pe, learning rate 7/, number of iterations
output Trained neural network Up ,,..., N do Sample 2i Po and x1 P1 Sample f ~ Uniform 1]
for each iteration
Inject noise 2g + + c, c ~ N I) with Pr.
Drop condition c - Φ with Pc:
Interpolate 2, + tæ + -1-ttapp
Compute true velocity v(æ,, t, c, f xı - 1o
Predict velocity using neural network (0 c) Compute loss < [1
1, Update ! using gradient descent 0 + VVL end for
Inference Process: input Initial sample 2g Po. perturbation step size At, classifier-free guidance strength a output Generated sample Pi Initialize zt + Zo
for 1. = with step size At do
Computer velocity with classifier-free guidance of (I (rog( (1 a (2,, 0)
Update F; It. Al o CR (x,, end for Output final state 21 +-
Algorithm provides detailed overview of the CellFlux algorithm, covering both training and inference. During training, the model learns to predict velocity between an initial and target distribution by interpolating between samples, applying noise and condition dropout, and optimizing L2 loss between predicted and true velocities. In inference, the trained model iteratively updates sample from the initial distribution toward the target distribution using classifier-free guidance. ultimately generating sample that aligns with the target distribution.
14

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
C. Experimental Details
Model architecture. CellFlux employs a UNet-based velocity field parameterization with input and output channels matching the dataset. It features four stages for downsampling and upsampling, with each stage halving or doubling the resolution and using hidden size of 128. This hierarchical UNet design focuses on efficient 2D spatial learning for pixel-level flow matching.
Perturbation encoding. We encode perturbations following IMPA's I approach (Palma et al., 2025). For chemical embeddings, we use 1024-dimensional Morgan Fingerprints generated with RDKit. For gene embeddings, CRISPR and ORF embeddings combine Gene2Vec with HyenaDNA-derried sequence representations, resulting in final dimensions of 328 and 456, respectively.
Training details. Models trained for 100 epochs on 4 A100 GPUs using the Adam optimizer with learning rate of le-4 and batch size of 128, requiring 8, 16, and 36 hours for BBBC021, RxRx1, and JUMP, respectively. The noise injection probability, condition drop probability, and classifier-free free guidance strength are set to 0.5, 0.2, and 1.2, respectively. Models are selected based on the lowest FID scores on the validation set.
15

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
D. Batch Effects
What Are Batch Effects Microscopy Experiments?
Batch effects refer to a form of distribution shift microscopy experiments, where non-biological variations arise due to differences in experimental conditions across imaging sessions batches. These effects be classified as type of covariate shift, where technical factors alter the distribution of input features, including:
Microscope or camera settings Variations in sensor sensitivity, illumination, resolution, or imaging modalities. Experimental procedures Differences in sample preparation, staining protocols, reagent batches, or handling by different researchers.
Environmental conditions Changes in temperature, humidity, or laboratory-specific conditions that may be difficult to control.
As result, images of biologically identical cells may appear different solely due to variations imaging conditions rather than biological differences.
2. Why Do Batch Effects Matter?
Batch effects pose major challenge to reproducible biomedical research by obscuring true biological effects of perturbations. Additionally, machine learning models may inadvertently learn batch-specific artifacts instead of meaningful biological patterns. Key issues caused by batch effects include:
Poor generalization - Models trained on batch-affected images may fail to classify new samples from different
experimental setup.
False discoveries Uncorrected batch effects can confound biological signals, leading to misleading conclusions.
Reduced reproducibility Results may not replicate across laboratories or imaging systems due unaccounted technical biases
3. Visualization of Batch Effects
Figure 5 visualizes three batches of BBBC021 images using PCA, showing that each batch forms distinct cluster. Notably, control (ctrl) and perturbed (trt) images from the same batch cluster together, rather than forming separate control and target clusters. This illustrates the batch effect-a systematic bias within each batch that is unrelated to the perturbation itself.
How CellFlux Addresses Batch Effects?
CellFlux mitigates batch effects by using control images as initialization during both training and inference, transporting them target images within the same batch. This ensures that the model learns only the relative difference between control and perturbed images. By conditioning control images from different batches, CellFlux effectively captures the true perturbation effect while preserving batch-specife artifacts. Figure 5 demonstrates this, showing that predicted images remain within the same batch cluster when given a control image from that cluster.
PCA of Images with Random Batch Selection
Bachijit
100
50
PCA2
-100
-200
-150
-100
-50
0
50
100
150
PCA1
Figure 5. Visualization of batch effects in BBBCU21 and how CellFlux addresses batch effects.
16

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
E. Datasets
As described below, all data used in this study are publicly available and utilized under their respective licenses. No new data were generated for this study.
BBBC021 dataset. We utilized the BBBC021v1 image set (Caie al., 2010), available from the Broad Bioimage Benchmark Collection (Ljosa al., 2012). The BBBC021 dataset focuses chemical perturbations in MCF-7 breast cancer cells, serving as a robust benchmark for image-based phenotypic profiling. It comprises 97,50x fluorescent microscopy images of cells treated with 1 113 small molecules across eight concentrations, targeting diverse cellular mechanisms such as actin disruption, Aurora kinase inhibition, and microtubule stabilization. Each image includes multi-channel labels for DNA, F-actin, and beta-tubulin, facilitating detailed morphological analysis. Metadata provides mechanism-of-actionn (MOA) annotations for compounds and experimental conditions, enabling applications in mechanistic prediction and phenotypic similarity analysis. Table shows MoA classes for all BBBC021 perturbations. Images were processed at resolution suitable for segmentation and deep learning tasks.
RxRx1 dataset. The RxRx1 dataset (Sypetkowski et al., 2023), available under CC-BY-NC-SA-4.0 license from Recursion Pharmaceuticals at rxrx.ai, focuses on genetic perturbations using CRISPR-mediater gene knockouts. It contains 170,943 images representing 1,042 genetic perturbations in HUVEC cells, with control conditions to address experimental variability, Images were captured across six channels, including nuclear and cytoskeletal markers, enabling high-dimensional phenotypic analysis. Preprocessing steps included segmentation, cropping, and resizing to standardize the data for computational analysis. This dataset supports tasks such as feature extraction, phenotypic clustering, and representation learning.
JUMP dataset (CPJUMPI). The JUMP dataset (Chandrasekaran et al., 2023), available under CCO 1.0 license, integrates both genetic and chemical perturbations, offering the most comprehensive image-based profiling resource to date, It includes approximately 3 million images capturing the phenotypic responses of 75 million single cells to genetic knockouts (CRISPR/ORF) and chemical perturbations. Key features include:
Chenical-genetic pairing: Perturbations targeting the same genes are tested in parallel assess phenotypic conver- gence divergence.
Imaging was standardized across cell types (U20S and A549), time points (short and extended durations), and experimental setups.
Controlled conditions:
Primary group: Forty plates profiling CRISPR knockouts and ORF overexpression.
Secondary group: Additional plates exploring extended experimental conditions.
The JUMP dataset uniquely enables the study of phenotypic relationships between genetic and chemical perturbations and supports the development of predictive models for multi-modal cellular responses. Public access to the dataset and associated analysis pipelines is available via Broad's JUMP repository.
17

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Compound
MoA
Cytochalasin B
Actin disruptors
Cytochalasin D
Actin disruptors
Latrunculin B
Actin disruptors
AZ258
Aurora kinase inhibitors
AZ841
Aurora kinase inhibitors
Mevinolin/Lovastatin
Cholesterol-lowerie
Simvastatin
Cholesterol-lowering
Chlorambucil
DNA damage
Cisplatin
DNA damage
Etoposide
DNA damage
Mitomycin
DNA damage
Camptothecin
DNA replication
Floxuridine
DNA replication
Methotrexate
DNA replication
Mitoxantrone
DNA replication
AZ138
Eg5 inhibitors
PP-2
Epithelial
Alsterpaullone
Kinase inhibitors
Bryostatin
Kinase inhibitors
PD-169316
Kinase inhibitors
Colchicine
Microtubule destabilizers
Demecolcine
Microtubule destabilizers
Nocodazole
Microtubule destabilizers
Vincristine
Microtubule destabilizers
Docetaxel
Microtubule stabilizers
Epothilone
Microtubule stabilizers
Taxol
Microtubule stabilizers
ALLN
Protein degradation
Lactacystin
Protein degradation
MG-132
Protein degradation
Proteasome inhibitor
Protein degradation
Anisomyein
Protein synthesis
Cyclohexamide
Protein synthesis
Emetine
Protein synthesis
DMSO
DMSO
Table 3. Modes of action (MoA) for compounds BBBCO21.
18

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
F. Qualitative Comparison
In this section, we present additional generated samples to further demonstrate the effectiveness of our method. Figures and 8 show qualitative comparisons on the BBBC021, RxRxI, and JUMP datasets, respectively. Our approach more accurately captures key biological effects, whereas images generated by IMPA fail reflect real biological responses, and those from PhenDiff appear blurry with significant detail loss.
Target
CellFlux
IMPA
PhenDiff
AZ258
Cisplatin
Etoposide
Methotrex
ate
PD-169316
PP-2
Figure More qualitative comparisons generated samples BBBC021.
19

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Target
CellFlux
IMPA
PhenDiff
ALG3
CCL27
DDX41
EPPK1
NAA50
TRNT1
Figure More qualitative comparisons generated samples on RxRxI.
20

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Target
CellFlux
IMPA
PhenDiff
4-CMTB
Orantinib
NS-11021
ABL1
PTGIS
GABRB2
Figure 8. More qualitative comparisons of generated samples on JUMP.
21

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
G. Trajectory
Forward interpolation. Our generation process aims to transform a control image into its corresponding perturbed image using our flow matching model. This achieved by iteratively solving an ODE, where the velocity field predicted by the model guides the transformation each timestep. As iterations progress, the image gradually evolves towards its final state at t = 1, representing the fully perturbed cell morphology.
Backward interpolation. Due to the bidirectional nature of our model, we can also perform reversible generation process by inverting the velocity direction. This allows us to start from the perturbed image and gradually recover the original control image, demonstrating the reversible capabilities of our method.
Trajectory examples. Figures 9 and 10 illustrate these bidirectional transformations. The top section of each figure depicts the forward trajectory, where the control image is progressively updated based on the learned velocity field, ultimately generating the perturbed image t 1. The bottom section shows the reverse trajectory, where the process is reversed, progressively reconstructing the original control image. This capability, which is absent in diffusion- based methods, offers promising approach for simulating morphological trajectories during perturbation responses. Moreover, CellFlux's 's reversible distribution transformation enables modeling of backward transitions in cell states, with potential applications in studying recovery dynamics and predicting treatment outcomes.
To further demonstrate our approach, we present trajectory examples for two drugs. The first, PP-2, reduces cell adhesion and disrupts actin reorganization, leading more dispersed cell distribution. In Figure 9, the forward trajectory shows cells transitioning from clustered to more diffuse state, while the reverse trajectory restores the original aggregation. The second, Chlorambucil, induces pyknosis (nuclear shrinkage). In Figure 10, the forward process shows one of the three nuclei undergoing cell death or division, leaving only two nuclei in the final state, while the reverse trajectory reconstructs the original three-nucleus configuration These results highlight our method's ability to capture biologically meaningful morphological transitions both directions.
22

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Perturbation PP-2
Centrnl
1-0 63
t-002
b-0.22
8-2.02
I+1.80
1-0
1-0.33
1+0.34
tn2.39
1-0.4
1-0
1-1100
1-0
1-0.6
b-0.9
I-0-93
t-1.00
b-3,00
1+1:00
Peturtes
Reverse Perturbation PP-2
1-11.0
2+0
:-4.05
b-0.26
t-0:27
b-0-30
I-1000
1-9.10
p-0.12
1-0.33
2-0:5
I+1.00
t-0.7
I-0.33
1+0.95
tn1.00
tw0.00
1+0.00
1-0 55
1+0
Figure (1/2) Bidirectional interpolation trajectory in BBBC021.
23

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Perturbation chlorambucil
Central
twQ
2H0 23
IH0022
1Nd OZ
1H0.02
1H0.22
10
8-0.10
1-0.33
1-0-2
t+0.00
1-0.34
1+0.20
2-0.44
1-6.54
$-2.59
t-2.05
70
b-0.74
I-0.0
1-3
1-0.00
b-0.22
b-034
I-0.00
b-1.97
Reverse Perturbation chlorambucil
Nettrbed
1:1.00
5-148
1:6.94
6:99
2.0.6
C:0.44
C:0.4
T-4.49
t-0.03
1-0.75
1-0.4
I+5.71
1-3
1H0
I+0.37
I+0.32
[+E-47
1+1.22
Tw6:22
Tei 01
1+0.01
Eoxtrol
Figure 10. (2/2) Bidirectional interpolation trajectory BBBC021.
24

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
H. More Results
Out-of-distributiin generalization. Table reports results on the out-of-distribution (OOD) set in BBBCO21, evaluating performance on perturbations absent from the training set. This highlights our method's strong generalization ability to novel chemical perturbations. The FID score measures the similarity between generated and real distributions, with lower values indicating closer match. As shown in the table, method effectively captures the biological effects of each perturbation, generating images that closely resemble real cellular responses. Robust OOD generalization is essential for biological research, enabling the exploration of untested interventions, analysis of unknown cellular responses, and the design of new drugs by simulating effects before experimental validation.
Method A7841
Cyclohexamide Cytochalasin D Ducetaxel
Epothilone B Lactacystin
Latrunculin B Simvastatin
PhenDiff
136.5
224.0
180.3
160.1
131.5
139.7
132.5
108.9
IMPA 131.7
189.9
180.6
130.6
120.7
133.7
128.5
79.7
CellFlux
84.1
99.5
129.4
81.9
93.77
106.9
97.9
90.9
Table Out-of-distribution generalization results per perturbation.
Effect of sample size on FID/KID. FID and KID are known to be sensitive to the number of samples. We evaluate performance across varying sample sizes on BBBC021 (IK-5K, limited to 6K test images) and JUMP (1OK-20K), As shown in Table 5, CellFlux consistently outperforms all baselines across all sample sizes, achieving 30-45% relative
improvement and demonstrating the robustness of its improvement regardless sample size.
Method IK FID
2.5K FID
5K FID
IOK FID
20K FID
IK KID
2.5K KID
5K KID
10K KID
20K KID
PhenDiff
71.3
64.3
49.5
47.5
46.1
2.55
3.68
3.10
4.95
5.09
IMPA
52.4
41.4
33.7
14.0
12.9
3.20
3.38
2.60
1.04
1.05
CellFlux
34.7
25.2
18.7
8.5
7.5
1.67
1.90
1.62
0.63
0.63
Table 5. FID and KID across different sample sizes.
Comparison with more baselines. Cell morphology prediction is a new task with only six baselines (Table 8). We included the only two published methods using control images (Bourou et al., 2024; Palma et al., 2025); others are unpublished (Hung al., 2024; Cook al., 2024). lack code (Yang et al., 2021; Cook al., 2024), or omit controls ( (Yang et al., 2021; Navidi al., 2025; Cook et al., 2024). We further compared Morpholifff (Navidi et al., 2025), recent Liffusion-based method, without using control images. Under setup BBBC021, CellFlux outperforms it in image quality and MoA metries.
Method
FID.
FID,
KID,
KID.
MoA Accuracy
MoA Macro-F1
MoA Weighted-FI
MorphoDiff
65.8
114.1
7.99
7.97
38.3
24.5
34.2
CellFlux
18.7
56.8
1.62
1.59
71.2
49.0
70.7
Table 6. CellFlux outperforms MorphoDiff on BBBC021 both image quality and MoA classification metries.
25

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Cross-dataset transfer. To assess whether the learned model can generalize to highly out-of-distribution settings, conduct cross-dataset transfer experiment by applying CellFlux model trained BBBC021 RxRx1 and JUMP images. Surprisingly, we observe that CellFlux successfully transfers and applies perturbation effects despite substantial domain shifts (Figure 11), highlighting its potential as a unified foundation model across diverse perturbation datasets. Note that there are no target images for RxRx1 and JUMP, as these datasets do not contain the corresponding perturbation conditions.
BBBC021
RxRx1
JUMP (New Dataset)
(Training Dataset)
(New Dataset)
AZ138
Demecolcine
AZ138
Demecolcine
AZ138
Demecolcine
Control Images
CellFlux
Target
Images
Figure 11. Cross-dataset transfer of CellFlux. Although CellFlux is trained solely on BBBCO21, it demonstrates zero-shot generalization two unseen datasets-RxRR and JUMP. Notably, it predict morphological changes induced perturbations (AZ138 and Demecolcine) that are absent from both datasets. AZ1188, an Eg5 inhibitor, leads to cell shrinkage and death, while Demecolcine disrupts microtuhules, resulting in smaller, fragmented nuclei.
CellProfiler metrics. To further evaluate whether CellFlux can capture perturbation spercffcc morphological changes, we extracted CellProfiler features related to nuclear size under three perturbations known to enlarge nuclei (taxol, vincristine, and demecolcine) using the BBBC021 dataset. As shown in Table 7 (mean and 95% confidence interval reported), CellFlux most closely matches the real perturbed morphology in terms nuclear size.
Method
taxol
vincristine
demecolcine
Control
1612.0 + 39.5
1612.0 + 39.5
1612.0 39.5 2311.0 = 136.1
Target
2296.7 190.1
2365.5 125.5
PhenDiff 1755.9 138.2
1947.8 + 70.8
2118.8 = 102.7
IMPA
2088.3 190.8
2116.9 + 107.1
2386.5 = 123.9
CellFlux
2141.0 166.6
2276.4 + 115.6
2323.8 # 121.9
Table 7. Comparison of CellProfiler nuclear size features under three compounds known enlarge nuclei.
26

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
I. Related Works
Table 8 presents a brief comparison of our work with existing methods for cellular morphology generation.
Paper
Generative Model
Use Control Image
How to Use Control Image
Mol2Image (Yang et al., 2021)
Normalizing Flows
No
PhenDiff (Bourou al., 2024)
Diffusion
Yes
Map control to noise then to target Add DINO embedding condition
LUMIC (Hung et al., 2024)
Diffusion
Yes
pDIFF (Cook et al., 2024)
Diffusion
No
MorphoDiff' (Navidi al., 2025)
Diffusion
No
IMPA (Palma al., 2025)
GAN
Yes
Add AdaIn layers to GAN Initialization source distribution
CellFlux (Ours)
Flow Matching
Yes
Table Related works on cell morphology generation.
27

---

CellFlux: Simulating Cellular Morphology Changes via Flow Matching
J. Biological Validation of Interpolation Trajectories
While CellFlux enables interpolation of perturbation trajectories, the biological relevance of the interpolated states remains unverified. Validating these trajectories is key next step. Although existing datasets lack ground-truth labels for intermediate states, future work could explore the following directions:
Dose interpolation: Some datasets include images under multiple dosage levels. We can test whether interpolation from control to high dose yields intermediate states that resemble realistic medium-dost morphologies.
Timepoint interpolation: For datasets with multiple timepoints (e.g.. day 0, day 5, day 10), we can evaluate whether interpolation from control to day 10 recovers morphologies consistent with intermediate timepoints such day 5.
Drug perturbation interpolation: Current datasets rarely include fine- tne-grained trajectories tost-treatmen. Validating such interpolations may require collecting new wet-lab data, such as live-cell imaging over time.
In addition to validation, future work could improve the biological plausibility of interpolation trajectories and avoid degenerate "shortest path in pixel space" artifacts by:
Interpolating in latent space: Performing interpolation a learned latent space (e.g., via an autoencoder) rather than pixel space may encourage trajectories to follow a more structured biological manifold.
Adding supervision from intermediate states: When datasets contain known intermediate states (e.g., medium dose), models can be trained to explicitly pass through these points during interpolation.
Adding constraints: Incorporating additional such as GAN loss-can guide interpolated images resemble real cell images, improving biological realism.
28


Reviews:
[{"review_id": "hblnIHHB8I", "reviewer": "Reviewer_Z8BU", "summary": "This work introduces CellFlow, an image-generative model designed to simulate cellular morphology changes induced by chemical and genetic perturbations. It leverages flow matching, a generative modeling technique, to learn a distribution-to-distribution map that transforms unperturbed cell states into perturbed ones. CellFlow is evaluated on chemical, genetic, and combined perturbation datasets, showing significant improvements in FID scores and mode-of-action prediction accuracy compared to existing methods. Due to the nature of flow matching, the model allows for continuous interpolation between cellular states, offering a tool for studying perturbation dynamics and also the recovery dynamics if the transformation is considered in reverse time.\n\n## update after rebuttal\nThank you for the additional experiments and clarifications in response to my feedback. The updated results addressed many of my concerns, and I appreciate the authors' efforts to improve the quality of the work based on my suggestions. As a result, I have revised my evaluation from a 3 (Weak Accept) to a 4 (Accept), reflecting my updated assessment of the paper's strengths and improvements. I also commend the authors for their commitment to incorporating all suggestions into the revised version, which will likely enhance the overall contribution of the work.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer Z8BU for recognizing the paper’s proper evaluation criteria, sound theoretical claims, well-designed experiments, comprehensive supplementary material, as well as its innovative approach, strong performance, and biological relevance. We address their concerns below:\n\n---\n\n> **Technical novelty**: *How does the work contribute technically beyond applying flow matching to a new domain?*\n\nWe thank the reviewer for this opportunity to clarify. Our key contribution lies not in modifying flow matching itself, but in **reformulating the problem of cell morphology prediction**: we propose modeling this task as a **distribution-to-distribution generation problem**, rather than the standard noise-to-distribution or single-to-single image prediction. This is a **conceptual shift** that aligns better with how perturbations affect heterogeneous cell populations, and allows for novel capabilities like batch effect correction and perturbation interpolation. Flow matching provides a principled and efficient tool to solve this reframed problem.\n\n---\n\n> **More experimental settings**: *In the absence of major technical novelty, a more extensive experimental setup is expected.*\n\nWe would like to highlight that our experimental setup is **exhaustive** compared to existing works:\n\n- **Perturbations**: chemical, genetic, and combined.\n- **Datasets**: BBBC021, RxRx1, and JUMP.\n- **Settings**: both **in-distribution (ID)** and **out-of-distribution (OOD)** generalization.\n- **Evaluation**: includes **overall FID / KID**, **conditional FID / KID**, and **MoA classification**.\n- **Capabilities**: our model supports **batch effect correction** and **perturbation interpolation**—capabilities that existing methods do not support.\n\n---\n\n> **Computational complexity**: *What are the training requirements and scalability for larger datasets or complex perturbations?*\n\nCellFlow is **computationally efficient** and scales **linearly** with dataset size. We already provided the training details in *Appendix C*: “Models are trained for 100 epochs on 4 A100 GPUs ... requiring 8, 16, and 36 hours for BBBC021, RxRx1, and JUMP, respectively”.\n\n---\n\n> **Control wells**: *How does CellFlow handle variations in control conditions across batches, and how does this impact the model's predictions?*\n\nCellFlow explicitly conditions on control images from the same batch to account for batch-specific variations *(discussed in Section 2.3 in the paper)*. This means that for each perturbed sample, the model is provided with the corresponding unperturbed (control) cells from the **same experimental batch**, enabling it to distinguish true perturbation effects from batch-induced variability. \n\nIn the table below *(an extension of Table 2d in the paper, with MoA evaluation added as suggested by Reviewer Yg4R)*, we compare generation quality and MoA classification performance when using **same-batch** versus **cross-batch** control initialization. Using controls from the same batch yields significantly better results, highlighting their importance for correcting batch effects and capturing true perturbation signals.\n\n||FID$_o$|FID$_c$|KID$_o$|KID$_c$|MoA Accuracy|MoA Macro-F1|MoA Weighted-F1|\n|-|-|-|-|-|-|-|-|\n|Condition on control wells from different batch|23.7|71.9|2.08|2.09|48.2|32.9|48.4|\n|Condition on control wells from same batch|18.7|56.8|1.62|1.59|71.2|49.0|70.7|\n|Relative improvement|+21.1%|+21.0%|+22.1%|+23.9%|+47.7%|+49.0%|+46.1%|\n\n---\n\n> **Interpolation trustworthiness**: *Can interpolation in perturbation space be trusted biologically?*\n\nWe emphasize that interpolation is a **novel and unique capability** of CellFlow, not supported by existing computational tools. Verifying intermediate cell states is inherently difficult, as current biotechnologies do not capture large-scale video-like morphological changes. Despite this limitation, domain experts have highlighted the potential verification of this feature—for example, in modeling dose-response curves (e.g., predicting medium-dose effects from high/low doses) or time-course dynamics (e.g., estimating 36h outcomes from 24h and 48h measurements). While our paper focuses on the **methodological foundation**, exploring these applications through biological validation is a promising direction for future work.\n\n---\n\n> **Cross-dataset transfer**: *Can CellFlow generalize perturbation effects across datasets?*\n\nThank you for the insightful question. We conducted a **transfer experiment** by applying a CellFlow model trained on BBBC021 to RxRx1 and JUMP images. These datasets lack ground-truth perturbed counterparts, making quantitative evaluation infeasible. However, we observed that CellFlow is able to transfer and apply perturbation effects despite substantial domain shifts, with **qualitative examples** available at https://anonymous.4open.science/r/CellFlow-Rebuttal/cross_dataset.png.\n\n---\n\nThank you again for your detailed feedback! We will include all of them in the revised paper.", "claims_and_evidence": "The usefulness of the interpolated dynamics from a biological perspective might be limited as it could be the mathematical artifacts of the Flow Matching formulation rather than a reflection of a concrete biological process.", "methods_and_evaluation_criteria": "The evaluation makes sense for conditional image generation in cell painting.", "theoretical_claims": "There is no major theoretical claim in this work. Proposition 1 makes intuitive sense and its proof in the appendix also seems correct.", "experimental_designs_or_analyses": "The considered datasets serve the purpose of this paper as they cover different types of perturbations and various cells.", "relation_to_broader_scientific_literature": "- The use of flow matching in a novel context.\n- Addressing an important problem in biology and drug discovery which is the effect of perturbations on cells.", "essential_references_not_discussed": "-", "other_strengths_and_weaknesses": "## Strengths\n- Innovative Approach: Although flow matching is relatively explored as a conditional distribution matching framework, its application in cellular morphology seems novel.\n- Performance: The model demonstrates significant improvement (35% in FID scores and 12% in mode-of-action prediction accuracy) over existing methods.\n- Biological Relevance: The generated images are biologically meaningful and capture perturbation-specific morphological changes.\nPotential Applications: The ability to interpolate between cellular states could be a valuable tool for studying perturbation dynamics even though the dynamics may not be biologically plausible.\n## Weaknesses:\n- Technical Novelty: Although the application of Flow Matching in this context may be novel, the technical novelty of the work is limited to using an existing method in a new domain. In the absence of a major technical novelty, a more extensive experimental setup is expected to show the utility of the method as an application-driven work.\n- Complexity: The computational complexity of the method is not discussed. This could bring more clarity on the scalability of the approach.\n- Control Wells: The importance of control wells is highlighted, but the paper does not detail how variations in control conditions are managed. For example, the wells without perturbations may exhibit different batch effects simply because of being in different wells.", "other_comments_or_suggestions": "-", "questions_for_authors": "- How does CellFlow handle variations in control conditions across different batches, and how does this impact the model's predictions?\n- What are the computational requirements for training CellFlow, and how does it scale with larger datasets or more complex perturbations?\n- The nature of the method that takes perturbation as input to the velocity network allows for interpolation over the perturbation axis. Given the complexity of biological systems, can such interpolation be trusted? Can authors provide the assumptions about the underlying system that justify trusting the predicted effect of unseen perturbations?\n- It is imaginable that there is some transfer learning of the effect of perturbations from one dataset to another. For example, when a perturbation is not present in a dataset, observing its effect in another dataset could be helpful to fill the gap in the target dataset. Does CellFlow allow for such transfer of knowledge across datasets?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VQ6cS86Q39", "reviewer": "Reviewer_Yg4R", "summary": "This work uses a flow-based conditional generative model applied to cellular imaging, with the goal of synthetically generate, given a reference control cell and a perturbation (either chemical, genetic, or both), a novel cell image illustrating the effects of the perturbation. Cellular morphology prediction is cast as a flow problem, transforming an initial data distribution in to a final data distribution. While this task is achieved with off-the-shelf conditional flow matching models, the authors bring up a fundamental problem, related to the destructive nature of cell imaging: training data from the initial and final distributions are *not* coupled. Lack of coupling is side-stepped by observing that sampling initial cells from the same batch is sufficient to ensure proper training. Moreover, the authors note that the proposed approach allows distinguishing true biological responses to perturbation from batch-specific artifacts.\n\nResults revolve around experiments on three datasets related to various perturbations (chemical, genetic, and both), and the proposed method is compared against two alternatives from the literature. Performance metrics are 1) image quality, 2) classification accuracy based on the mode-of-action multi-class classification problem. The proposed method improves consistently over the state of the art on both metrics. Additional ablation studies confirm the design choices made by the authors.\n\n## POST REBUTTAL\nThanks for your detailed answers to my questions, and for the new results, which in my opinion strengthen your work.\n\nI have modified my score accordingly.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer Yg4R for their appreciation of the paper’s beautiful writing and illustration, proper experimental design and evaluation, and strong relevance to the biology and cell imaging community. We address their comments below:\n\n---\n\n> **Claim 1 (effect of sample size on FID/KID):** *Could FID/KID score differences diminish with larger sample sizes?*\n\nWe added evaluations of different sample sizes on BBBC021 (1K–5K, only 6K test images available) and JUMP (10K–20K). As shown, **CellFlow consistently outperforms all baselines across sample sizes**, with 30–45% relative improvement, demonstrating robustness.\n\n||1K FID|2.5K FID|5K FID|10K FID|20K FID|1K KID|2.5K KID|5K KID|10K KID|20K KID|\n|-|-|-|-|-|-|-|-|-|-|-|\n|PhenDiff|71.3|64.3|49.5|47.5|46.1|2.55|3.68|3.10|4.95|5.09|\n|IMPA|52.4|41.4|33.7|14.0|12.9|3.20|3.38|2.60|1.04|1.05|\n|CellFlow|34.7|25.2|18.7|8.5|7.5|1.67|1.90|1.62|0.63|0.63|\n\n---\n\n> **Claim 2 (class imbalance in MoA prediction)**: *Could you report macro and weighted F1 scores in Table 2.a?*\n\nWe now report **Macro-F1 and Weighted-F1** scores for MoA classification. CellFlow outperforms all baselines not only in accuracy but also in F1 metrics, addressing class imbalance concerns.\n\n||Acc|Macro-F1|Weighted-F1|\n|-|-|-|-|\n|Groundtruth Image|72.4|69.7|72.1|\n|PhenDiff|52.6|33.6|52.1|\n|IMPA|63.7|40.2|64.8|\n|CellFlow|71.2|49.0|70.7|\n\n---\n\n> **Claim 3 (OOD generalization)**: *Why are MoA metrics not reported for OOD perturbations in Table 2.b?*\n\nWe now **include MoA metrics** for OOD perturbations. CellFlow significantly outperforms baselines in this setting as well, reinforcing the model’s generalization capability.\n\n||Acc|Macro-F1|Weighted-F1|\n|-|-|-|-|\n|Groundtruth Image|88.0|85.0|88.0|\n|PhenDiff|9.6|9.3|7.4|\n|IMPA|16.0|10.0|13.1|\n|CellFlow|43.2|36.6|42.8|\n\n---\n\n> **Claim 4 (batch effect)**: *Claim on batch correction is marginally supported.*\n\nCellFlow explicitly conditions on control images from the **same batch** to correct batch effect. In the table below (an extension of Table 2.d in the paper, with MoA evaluation added), we compare generation quality and MoA classification performance when using **same-batch** versus **other-batch** control initialization. Using controls from the same batch yields significantly better results, highlighting their importance for correcting batch effects and capturing true perturbation signals.\n\n||FID$_o$|FID$_c$|KID$_o$|KID$_c$|MoA Acc|MoA Macro-F1|MoA Weighted-F1|\n|-|-|-|-|-|-|-|-|\n|CellFlow w/ Other Batch Init|23.7|71.9|2.08|2.09|48.2|32.9|48.4|\n|CellFlow w/ Same Batch Init|18.7|56.8|1.62|1.59|71.2|49.0|70.7|\n\n---\n\n> **Methods and evaluation (alternative methods)**: *Comparison with more baselines would strengthen the claim.*\n\nCell morphology prediction is a new task with only six baselines (Table 5 in Appendix). We included the **only two published methods** using control images; others are unpublished, lack code, or omit controls. We also added MorphoDiff (ICLR 2025), a diffusion-based method. Under our setup, CellFlow outperforms it in image quality and MoA metrics.\n\n||FID$_o$|KID$_o$|FID$_c$|KID$_c$|MoA Acc|MoA Macro-F1|MoA Weighted-F1|\n|-|-|-|-|-|-|-|-|\n|MorphoDiff|65.8|7.99|114.1|7.97|38.3|24.5|34.2|\n|CellFlow|18.7|1.62|56.8|1.59|71.2|49.0|70.7|\n\n---\n\n> **Experimental designs (time effect)**: *How does the method handle issues like fixation and cell cycle variation?*\n\nWe appreciate this insightful question. Cell morphology naturally varies over time due to asynchronous cell cycles and stochastic responses. CellFlow models **distribution-level transformations** rather than single-cell alignments, enabling it to **average out temporal and biological variability and capture population-level perturbation effects**. We believe this distribution-to-distribution approach offers a principled solution to handling temporal variation in image-based profiling.\n\n---\n\n> **Claim 5 (interpolation use case)**: *Implications of bidirectional interpolation not discussed.*\n\n> **Relation to broader science (real-world impact)**: *What is the impact of this work for drug design or discovery?*\n\nWe appreciate the reviewer’s comment. Our core contribution is a **new problem formulation**—modeling perturbation effects as a distribution-to-distribution transformation—and a **principled solution** via flow matching. This leads to significantly stronger performance and unlocks several novel capabilities relevant to biology: for example, batch effect correction helps isolate true biological signals, and perturbation interpolation enables exploration of intermediate drug doses or timepoints. While full biological validation is beyond the scope of this paper, these capabilities lay the groundwork for future impact in drug design & discovery.\n\n---\n\n> **Other comments (naming)**\n\nWe will **rename our method to CellFlux** to avoid confusion. Thank you for bringing this to our attention.\n\n---\n\nThank you again for your detailed feedback! We will include all of them in the revised paper.", "claims_and_evidence": "* Claim 1: CellFlow generates high-fidelity images. This claim is partially supported by the experiments. The use of custom-made FID and KID scores is appropriate, but I am afraid that the number of generated samples (5k) is too small. Indeed, the FID score is known to have high variance when calculated on smaller sample sizes. Using only 5k images might increase the likelihood of statistical noise affecting the scores. I am afraid that the differences between methods might diminish with larger sample sizes, suggesting that the superiority of CellFlow over baselines might be less pronounced if evaluated on 50k images.\n\n* Claim 2: The generated images capture meaningful biological patterns. This claim is partially supported by the experiments. In practical terms, an image classifier (e.g.a CNN) is trained on real, experimentally observed images of perturbed cells, where the ground-truth labels are the known MoAs of the drugs applied, basically consisting in roughly 30 classes. The MoA classification accuracy is the percentage of synthetically generated images (by the CellFlow model) for which the classifier correctly predicts the MoA, compared to the known ground-truth MoA for that perturbation.\nHowever, class distribution is not properly discussed in the paper, and the problem is that it can be highly unbalanced, with certain classes being over-represented. Given this imbalance, accuracy as a metric could be misleading because it might be biased towards the majority classes. Alternative metrics such as macro-averaged and weighted F1-scores could be more appropriate.\n\n* Claim 3: CellFlow generalizes to out-of-distribution perturbations never seen during training. This claim is partially supported by Table 2.b. However, the table only report image quality metrics and not the MoA metric, giving only a partial assessment of the generalization capabilities of CellFlow.\n\n* Claim 4: CellFlow corrects batch effects by conditioning on control cells from different batches. By comparing control images with generated images, it can disentangle true perturbation-induced morphological changes from experimental batch\nartifacts. This claim is marginally supported in the paper\n\n* Claim 5: CellFlow enables bidirectional interpolation between cellular states due to the continuous and reversible nature of the velocity field in flow matching. This claim is true by construction. However, its implications are not discussed in the paper.", "methods_and_evaluation_criteria": "* Datasets: in my opinion the proposed datasets used in the experimental protocol are appropriate.\n\n* Alternative methods: authors focus only on two baselines that also take control images into account. It would have been interesting to check the performance of other methods that do not do that, which would have strengthened the results and claims.\n\n* Metrics: while I think image quality metrics to be appropriate (modulo the fact that using 5k samples might not be sufficient), MoA accuracy might not, and F1-score metrics would be preferable.", "theoretical_claims": "The proof for Proposition 1 appears correct to me.", "experimental_designs_or_analyses": "Yes, I checked and my main comment is as follows. Section 2.2 goes great lengths in discussing the coupling problem that exists in the data. The proposed solution (in section 2.3) is valid, and simply translates into building appropriate training sets where initial and final distributions come from the same batch. My biggest concern revolves around the concept of time.\n\nIn high-content imaging experiments, cells are fixed (chemically preserved) at a specific time point after the perturbation is applied. This process halts all biological activity, effectively “freezing” the cellular state. Typically, in a well-designed experiment, both control and perturbed cells are fixed at the same time to ensure that temporal evolution does not differentially affect the two groups. However, cells are dynamic living systems, and their morphology naturally changes over time due to growth, division, apoptosis, and other metabolic activities, even in control conditions. Even when control and perturbed cells are fixed at the same time, there can be issues: cells can be at different stages of the cell cycle or physiological state, perturbed cells might respond to the treatment (chemical or genetic) at different rates depending on their initial state, leading to asynchronous responses, stochasticity in biological processes cause variability in cell morphology over time.\n\nThese aspects are marginally discussed in the paper: they are fundamental, key challenges, and I wonder what is the authors position on these issues.", "relation_to_broader_scientific_literature": "This paper weights more for the biology/cell community than the machine learning community: indeed, the methodological part of the paper is based on a well-established method appeared several years ago, and there are no major contributions apart from the discussion in section 2.2 and section 2.3.\n\nFrom the biology/cell literature point of view, I think this paper might represent an interesting advancement, but the experimental protocol and results are somehow shallow in this respect: focusing only on MoA accuracy, while important (and possibly improvable), does not give a sense of the real impact of this work in a, e.g., drug design context (as per the introduction in the paper).", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "This is a beautiful paper, very clearly written, and majestically illustrated!!", "other_comments_or_suggestions": "Please, check the literature! There is another work called CellFlow, published in the MLGenX workshop associated to ICLR 2024.\n\n@inproceedings{\npalma2024cellflow,\ntitle={cellFlow: a generative flow-based model for single-cell count data},\nauthor={Alessandro Palma and Till Richter and Hanyi Zhang and Andrea Dittadi and Fabian J Theis},\nbooktitle={ICLR 2024 Workshop on Machine Learning for Genomics Explorations},\nyear={2024},\nurl={https://openreview.net/forum?id=xaLXV2j8vl}\n}", "questions_for_authors": "* Would it be possible for you to report, in Table 2.a, F1-score instead of accuracy? See comments above\n\n* Would it be possible for you to report, in Tables 1 and 2, FID and KID scores computed on more than 5k samples? Maybe 50k is too much of a computational burden, but ramping up to 20k could be feasible.\n\n* Would you mind providing your views on the considerations above on the role of \"time\"?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c1a9QqlK0P", "reviewer": "Reviewer_L3Zf", "summary": "This paper proposes CellFlow, a generative model for cell microscopy images in the presence of chemical and/or genetic perturbations. In contrast to existing methods that tackle this problem, CellFlow can explicitly take into account batch effects by learning a distribution-level mapping between unperturbed (control) images and perturbed images within the same experimental batch. This is achieved by using flow matching as a tool for unsupervised image translation. The experimental results demonstrate the importance of taking batch effects into account, and show improved image quality and generalization across the different perturbations in the data.\n\n## update after rebuttal\n\nRecommendation increased during rebuttal phase.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer L3Zf for their thoughtful comments and for recognizing the paper’s well-supported claims, appropriate methodology and evaluation, sound theoretical foundation, strong experimental results, and relevance to the cell biology domain. We address their comments below:\n\n---\n\n> **Metric validity**: *Are FID/KID meaningful for cell images?*\n\nWe agree that FID/KID were originally designed for natural images, but they remain **standard and valid metrics** in cell morphology prediction. They are used by all six existing baselines (e.g., IMPA, PhenDiff; see Table 5 in paper's Appendix). Our qualitative observations during development also confirmed that FID/KID effectively reflects image quality and guided model improvement.\n\nFurthermore, to ensure biological relevance, we **supplement FID/KID with mode-of-action (MoA) prediction**, which directly evaluates whether generated images preserve meaningful biological signals. As suggested by Reviewer Yg4R, we now include **MoA Accuracy, MoA Macro-F1, and MoA Weighted-F1** across in-distribution (Table 2a in paper), out-of-distribution (Table 2b  in paper), and batch effect correction settings (Table 2d in paper). All these metrics demonstrate that CellFlow consistently and significantly outperforms all baselines. \n\n|Table 2a (in-distribution evaluation)|FID/KID|MoA Acc|MoA Macro-F1|MoA Weighted-F1|\n|---|---|---|---|---|\n|Groundtruth Image|Reported in paper|72.4|69.7|72.1|\n|PhenDiff|…|52.6|33.6|52.1|\n|IMPA|…|63.7|40.2|64.8|\n|**CellFlow**|**…**|**71.2**|**49.0**|**70.7**|\n\n|Table 2b (out-of-distribution evaluation)|FID/KID|MoA Acc|MoA Macro-F1|MoA Weighted-F1|\n|---|---|---|---|---|\n|Groundtruth Image|Reported in paper|88.0|85.0|88.0|\n|PhenDiff|…|9.6|9.3|7.4|\n|IMPA|…|16.0|10.0|13.1|\n|**CellFlow**|**…**|**43.2**|**36.6**|**42.8**|\n\n|Table 2d (batch effect correction)|FID/KID|MoA Acc|MoA Macro-F1|MoA Weighted-F1|\n|---|---|---|---|---|\n|CellFlow w/ Other Batch Init|Reported in paper|48.2|32.9|48.4|\n|**CellFlow**|**…**|**71.2**|**49.0**|**70.7**|\n\nThis improvement stems from our **core contribution**—**new problem formulation and solution method.** We propose modeling this task as a **distribution-to-distribution generation problem**, rather than the standard noise-to-distribution or single-to-single image prediction. This is a **conceptual shift** that aligns better with how perturbations affect heterogeneous cell populations, and allows for novel capabilities like batch effect correction and perturbation interpolation. Flow matching provides a principled and efficient tool to solve this reframed problem. While biological impact is not directly tested in this paper, CellFlow’s **strong performance and new capabilities** open the door to applications in **drug response prediction**, drug discovery, and personalized medicine in future biological studies.\n\n---\n\n> **Interpolation plausibility**: *Do interpolations correspond to biologically meaningful transitions?*\n\nThank you for your question. We emphasize that interpolation is a **novel and unique capability** of CellFlow, not supported by existing computational tools. Verifying intermediate cell state transition is inherently difficult, as current biotechnologies do not capture large-scale video-like morphological changes. Despite this limitation, domain experts have highlighted the potential verification of this feature—for example, in modeling dose-response curves (e.g., predicting medium-dose effects by interpolating high/low doses) or time-course dynamics (e.g., estimating 36h outcomes by interpolating 24h and 48h measurements). While our paper focuses on the **methodological foundation**, exploring these applications through biological validation is a promising direction for future work.\n\n---\n\n> **Biological metrics**: *Could features like nucleus size be evaluated?*\n\nThank you for the suggestion. We extracted **CellProfiler features related to nuclear size** under three perturbations known to enlarge nuclei (taxol, vincristine, and demecolcine) using the BBBC021 dataset. As shown in the table below (mean and 95% confidence interval reported), CellFlow most closely matches the real perturbed morphology in terms of nuclear size.\n\n||**taxol**|**vincristine**|**demecolcine**|\n|---|---|---|---|\n|**Control** |1612.0 ± 39.5|1612.0 ± 39.5|1612.0 ± 39.5|\n|**Target**|2296.7 ± 190.1|2365.5 ± 125.5|2311.0 ± 136.1|\n|**PhenDiff**|1755.9 ± 138.2|1947.8 ± 70.8|2118.8 ± 102.7|\n|**IMPA**|2088.3 ± 190.8|2116.9 ± 107.1|2386.5 ± 123.9|\n|**CellFlow**|**2141.0 ± 166.6**|**2276.4 ± 115.6**|**2323.8 ± 121.9**|\n\n---\n\n> **Reference addition**: *Please consider citing “Guided Flows for Generative Modeling and Decision Making”, which introduces classifier-free guidance in flow matching*\n\nThank you for the suggestion. We will include this reference in the final version.\n\n---\n\nThank you again for your detailed feedback! We will include all of them in the revised paper.", "claims_and_evidence": "The claims made are as follows:\n\n1. CellFlow models distribution-wise mappings from unperturbed to perturbed images and consequently distinguishes perturbation effects from batch effects.\n\n2. CellFlow features improved image quality on three different genetic and/or chemical perturbation datasets over two baseline models.\n\n3. CellFlow generalizes to perturbations not seen during training.\n\n4. CellFlow enables continuous interpolation between unperturbed and perturbed cellular states, offering a means to study the perturbation dynamics.\n\nOverall, all claims are supported by experimental results. However, see also the comments in the experimental design or analyses section of the review.", "methods_and_evaluation_criteria": "The datasets and methods used appear suitable for the problem statement of this paper. In particular, flow matching's capabilities to map arbitrary source distributions to the target distribution aligns well with the paper's goal of generating perturbed cellular morphologies, starting from unperturbed cells of the same experimental batch.", "theoretical_claims": "I read the proof of proposition 1 in Appendix A and did not notice any issues, but I did not thoroughly check its correctness.", "experimental_designs_or_analyses": "1. I assume FID and KID scores are calculated using a model trained on ImageNet. Can we be sure that those scores are meaningful in the context of cell images, as such images are not aligned with images of natural scenes on which the model was trained?\n\n2. It is claimed that CellFlow provides a potential means to study perturbation trajectories. However, from 4.4 it remains unclear whether or not these trajectories correspond to something that is biologically meaningful. For example, in the top row of Fig. 4a, it seems that the model has learned to slowly dim the pixel values that are relatively far from the nucleus, which makes sense since this would roughly correspond to the shortest path between the unperturbed and perturbed images in pixel space. However, this is not necessarily a biologically plausible path.\n\n3. In addition to ML-enabled metrics like FID score and MoA, it could be beneficial to also add biologically relevant metrics (e.g. nucleus size) that describe the morphology of the cell, and see whether these match between the generated and true perturbed cell images.", "relation_to_broader_scientific_literature": "The paper uses the image-to-image translation capabilities of flow matching (earlier explored in e.g. [1] and [2]) to solve the problem of mapping the distribution of unperturbed cell images to the distribution of perturbed cell images. This approach is shown to improve over baseline models that attempt to model the effect of perturbations on cell morphology. So, although both the method and problem are not novel by themselves, to my knowledge this is (1) the first application of flow matching to the problem, and (2) the technology enables taking batch effects into account, which prior methods for the application could not, and the results demonstrate the benefits of doing so.\n\n[1] Improving and generalizing flow-based generative models with minibatch optimal transport. https://arxiv.org/abs/2302.00482\n\n[2] Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. https://arxiv.org/abs/2209.03003", "essential_references_not_discussed": "Classifier-Free guidance for Flow Matching has been introduced in [3], and it would be good to add the citation in Section 3.3.\n\n[3] Guided Flows for Generative Modeling and Decision Making. https://arxiv.org/abs/2311.13443", "other_strengths_and_weaknesses": "Strengths:\n\n1. The paper addresses a relevant application and is well-written.\n\n2. See the points under 'methods and evaluation criteria'\n\n\n\nWeaknesses:\n\n1. See the comments under experimental design and analyses for potential weaknesses.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please respond to the comments under experimental design or analyses in particular.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper utilizes state of the art generative modelling method for in-silico biological experiments that may have an important impact for future research in this direction. The reviewers are in agreement that even though the work has some limitations, it is overall well executed and is a valuable contribution to the field.
Decision: Accept (poster)