---
# System prepended metadata

title: Modelling single cell RNA with optimal control

---

# Modelling single cell RNA with optimal control


### Intro
The starting idea considers cells, microglia and neurons, that are healthy or diseased and the question is how to model the transciptomics in order to go from a state to another. This idea will get updated so stay tuned! \
The current way to go now is to check some literature and then look into current methods so we can have a couple of baselines to compare with and then we implement the optimal control framework.

**Links:** 
- [Weekly meetings notes](https://hackmd.io/wLAgMJAXTVmgNdbtS3kpyw?both) 
- [https://wandb.ai/ionelia/TranscriptomicsOptimalControl?workspace=user-ionelia](https://wandb.ai/ionelia/TranscriptomicsOptimalControl?workspace=user-ionelia) 
- [Overleaf]()

### Literature:
- [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463) 
- [Simulating multiple faceted variability in single cell
RNA sequencing](https://www.nature.com/articles/s41467-019-10500-w.pdf) 
- [GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods](https://www.researchgate.net/publication/51242143_GeneNetWeaver_In_silico_benchmark_generation_and_performance_profiling_of_network_inference_methods)
- [Understanding how T helper cells learn to coordinate effective immune responses through the lens of reinforcement learning](https://arxiv.org/pdf/1904.05581.pdf)
- [Is T Cell Negative Selection a Learning Algorithm?](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140671/)
- [SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks (2020)](https://www.sciencedirect.com/science/article/pii/S2405471220302878) 
- [Generalizing RNA velocity to transient cell states through dynamical modeling, Bergen et al.,2020, Nature Biotechnology](https://www.nature.com/articles/s41587-020-0591-3)
- [DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (EBI 2017)](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1189-z) 
- [Deep reinforcement learning for de novo drug design](https://advances.sciencemag.org/content/4/7/eaap7885?intcmp=trendmd-adv) 
- [Optimality principles in sensorimotor control](https://www.nature.com/articles/nn1309)
- [Splatter: simulation of single-cell RNA sequencing data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0) 
- [Xiuwei et al 2019 - Simulating multiple faceted variability in single cell RNA sequencing](https://www.nature.com/articles/s41467-019-10500-w)
- [Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework](https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-122414-033625) 
- [Multiscale relevance and informative encoding in neuronal spike trains](https://link.springer.com/article/10.1007/s10827-020-00740-x)
- [Statistical criticality arises in most informative representations](https://iopscience.iop.org/article/10.1088/1742-5468/ab16c8)
- [Epigenetic regulation of brain region-specific microglia clearance activity](https://www.nature.com/articles/s41593-018-0192-3) 
- [Epigenetics Control Microglia Plasticity - review of the one above](https://www.frontiersin.org/articles/10.3389/fncel.2018.00243/full) 

### Literature on optimal control 
- [Optimal Control of Gene Regulatory Networks with
Unknown Cost Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8431514)
- [Control of Gene Regulatory Networks With Noisy Measurements and Uncertain Inputs](https://ieeexplore.ieee.org/abstract/document/8017565?casa_token=bu2XloeveT0AAAAA:3WIhdmp8OKrr8B3QyH92sxIjF7pZGiwJaXe8DJvotCsFmghRPtodoxJj5Ta81kcFbJkYUb_8-aj1)
- [Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8350274?casa_token=sAi0abbaG4UAAAAA:JpFKInOkC4lB5LJUNnH9_IBjs4ukWIzDMX3mbjgQWiRCZHweKIIkAHQPxRwkZHr6PlgZuvEPbS0f)
- [Point-Based Methodology to Monitor and Control Gene Regulatory Networks via Noisy Measurements](https://ieeexplore.ieee.org/abstract/document/8265205?casa_token=K35YaaFWb2YAAAAA:jTOlvtjmUXfr8-mJugMpwQQzEqtfCLP6nxCroMgs9XSvm7j0osKiMuPr9ODPtXwrle-OIkBsArv2)
- [Gene regulatory network state estimation from arbitrary correlated measurements](https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0543-y)
- [Optimal Finite-Horizon Perturbation Policy for Inference of Gene Regulatory Networks](https://ieeexplore.ieee.org/abstract/document/9171420?casa_token=gXw4u7y3500AAAAA:gxm6hdOU0VzzdxuvQ460Df1_idg7SCTfSL407qNe8hv4l13jPmX_uAWFAiWm_MNzKn6M3D-pGYUR)
- [State-feedback control of Partially-Observed Boolean Dynamical Systems using RNA-seq time series data](https://ieeexplore.ieee.org/abstract/document/7524920?casa_token=4MwHk1_7zHgAAAAA:lP87L56T6GF9dSbK3ZRULmNawUQtTfePon_myAydANZbtG5Bf6H7fuQ3mhDc0vDNntFg5VBmNJPHrA) 
- [Regulating gene expression using optimal control theory](https://ieeexplore.ieee.org/document/1188968) 
- [Understanding adaptive immune system as reinforcement learning](https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.3.013222)
- [EteRNA-RL: Using reinforcement learning to design RNA secondary structures](https://web.stanford.edu/class/cs234/CS234Win2018/past_projects/2017/2017_Kauvar_Richman_Allen_EteRNA-RL_Paper.pdf)
- [Learning to Design RNA](https://arxiv.org/abs/1812.11951)
- [Optimal control of gene mutation in DNA replication](https://pubmed.ncbi.nlm.nih.gov/22454557/)
- [Optimal control of an HIV immunology model](https://onlinelibrary.wiley.com/doi/pdf/10.1002/oca.710)
- [Generative ODE Modeling with Known Unknowns](https://arxiv.org/abs/2003.10775)
- [Optimal infinite horizon control for probabilistic Boolean networks](https://ieeexplore.ieee.org/document/1655433)
- [Revealing strengths and weaknesses of methods for gene network inference](https://www.pnas.org/content/107/14/6286)
- [MODEL-BASED REINFORCEMENT LEARNING FOR BIOLOGICAL SEQUENCE DESIGN](https://research.google/pubs/pub49138/)

### Literature about pseudo cell trajectories
- [Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut](https://pubmed.ncbi.nlm.nih.gov/29153838/)
- [Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4772-0)
- [Splatter: simulation of single-cell RNA sequencing data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0) 

### Literature generative frameworks
- [Deep generative modeling for single-cell transcriptomics (Nature Methods 2018)](https://www.nature.com/articles/s41592-018-0229-2) 
- [Interpretable dimensionality reduction of single cell transcriptome data with deep generative models(2018)](https://www.nature.com/articles/s41467-018-04368-5)
- [VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder, 2017](https://www.biorxiv.org/content/early/2017/10/06/199315) 
- [Single cell RNA-seq denoising using a deep count autoencoder (2018)](https://www.biorxiv.org/content/early/2018/04/13/300681) 
- [Grønbech, C. H. et al. scVAE: variational auto-encoders for single-cell gene expression data (2018)](https://www.biorxiv.org/content/early/2018/05/16/318295)




### Transcriptomics mouse data
- [Hammond brain microglia single cell RNA sequencing](https://seafile.ist.ac.at/d/e81321fa768b447ea293/)
- [O'Koren retinal microglia single cell RNA sequencing](https://seafile.ist.ac.at/d/8c997da0c83841549ee4/)
- [Tran retinal ganglion cell single cell RNA sequencing](https://seafile.ist.ac.at/d/e9e198c2b7764dbfbdd7/)
- [Peng retinal ganglion cell optic nerve crush ATAC-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147844)


### Other transcriptomics
c.elegans transcriptomics (the worm is much simpler than other organisms):
- [Molecular topography of an entire nervous system, Taylor at al., 2021, Cell](https://www.sciencedirect.com/science/article/pii/S0092867421007583?via%3Dihub)



















