Combination of Dynamical Systems Theory and Machine Learning Predicts Cell Fate Governing Equations


Yan Zhang

Ivet Bahar

Jianhua Xing

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (, which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Figure 1. Overview of dynamo computational framework for analyzing time-resolved single-cell data.
Upper left: Dynamo generates time-resolved RNA velocity from metabolically labeled single cell RNA-seq data based on a biophysical model of gene expression.
Upper right: The transcriptomic vector field reconstructed from discrete RNA velocity samples using a machine learning method encodes essential gene-gene interaction information in the underlying gene regulatory network, which can be extracted from various differential geometrical quantities of the vector field.
Lower left: Dynamo reveals the underlying biological mechanism for the fast appearance of Megakaryocytes in a hematopoiesis dataset.
Lower right: Dynamo predicts cell type transitions by mapping out the most probable path and cell fate changes under in silico perturbations.

Whitehead Institute News Article

Qiu X, Zhang Y, Martin-Rufino JD, Weng C, Hosseinzadeh S, Yang D, Pogson AN, Hein MY, Min KH, Wang L, Grody EI, Shurtleff MJ, Yuan R, Xu S, Ma Y, Replogle JM. Lander ES, Darmanis S, Bahar I, Sankaran VG, Xing J, Weissman JS (2022) Mapping transcriptomic vector fields of single cells. Cell, in press. 

Article in The Atlantic highlights the work of the Bahar lab on MIS-C caused by SARS-CoV-2

This article highlights the work of the Bahar lab in collaboration with the Arditi lab on the molecular origin of multisystem inflammatory syndrome in children (MIS-C) observed in Covid-19 patients. The article provides an extensive perspective, including interviews with Bahar, Arditi, and other scientists.

The Bahar lab discovered a superantigenic segment on the spike glycoprotein (Cheng et al., PNAS 2020), potentially implicated in triggering MIS-C as well as the cytokine storm and hyperinflammatory immune responses observed in severe Covid-19 cases (Rivas et al J Allergy & Clinical Immunol 2021). The TCR repertoire observed in in severely infected patients (Porritt et al, J Clinical Investigation, 2021) supported the occurrence of such a superantigenic reaction. The discovery of this superantigenic segment, which shares sequence and structure similarities with Staphyloccocal enterotoxin B led to the identification of a monoclonal antibody (6D3), which proved to reduce SARS-CoV-2 infection in experiments conducted at Cedars Sinai (UCLA) (Cheng et al., Structure 2021).

Lee Lab Publishes in Science Advances


A myriad of inflammatory cytokines regulate signaling pathways to maintain cellular homeostasis. The IkB kinase (IKK) complex is an integration hub for cytokines that govern nuclear factor kB (NF-kB) signaling. In response to inflammation, IKK is activated through recruitment to receptor-associated protein assemblies. How and what information IKK complexes transmit about the milieu are open questions. In this paper, the Lee Lab track dynamics of IKK complexes and nuclear NF-kB to identify upstream signaling features that determine same-cell responses. Experiments and modeling of single complexes reveal their size, number, and timing relays cytokine-specific control over shared signaling mechanisms with feedback regulation that is independent of transcription. Their results provide evidence for variable-gain stochastic pooling, a noise-reducing motif that enables cytokine-specific regulation and parsimonious information transfer. They propose that emergent properties of stochastic pooling are general principles of receptor signaling that have evolved for constructive information transmission in noisy molecular environments.

Cruz JA*, Mokashi CS*, Kowalczyk GJ, Guo Y, Zhang Q, Gupta S, Schipper DL, Smeal SW, Lee REC. A variable-gain stochastic pooling motif mediates information transfer from receptor assemblies into NF-kB. Sci. Adv. 7, eabi9410 (2021)