1
|
Ramirez DA, Lu M. Dissecting reversible and irreversible single cell state transitions from gene regulatory networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.30.610498. [PMID: 39257745 PMCID: PMC11384016 DOI: 10.1101/2024.08.30.610498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with systems' dynamical behaviors, STICCC reveals how network interactions influence reversible and irreversible state transitions. Compared to existing methods that infer pseudotime and RNA velocity, STICCC provides complementary insights into the gene regulation of cell state transitions.
Collapse
Affiliation(s)
- Daniel A Ramirez
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Mingyang Lu
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| |
Collapse
|
2
|
Katebi A, Chen X, Ramirez D, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. NPJ Syst Biol Appl 2024; 10:38. [PMID: 38594351 PMCID: PMC11003984 DOI: 10.1038/s41540-024-00366-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
Collapse
Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Daniel Ramirez
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA.
- The Jackson Laboratory Cancer Center, Bar Harbor, ME, USA.
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA.
| |
Collapse
|
3
|
Tang R, He X, Wang R. Constructing maps between distinct cell fates and parametric conditions by systematic perturbations. Bioinformatics 2023; 39:btad624. [PMID: 37831895 PMCID: PMC10603594 DOI: 10.1093/bioinformatics/btad624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 09/10/2023] [Accepted: 10/12/2023] [Indexed: 10/15/2023] Open
Abstract
MOTIVATION Cell fate transitions are common in many developmental processes. Therefore, identifying the mechanisms behind them is crucial. Traditionally, due to complexity of networks and existence of plenty of kinetic parameters, dynamical analysis of biomolecular networks can only be performed by simultaneously perturbing a small number of parameters. Although many efforts have focused on how cell states change under specific perturbations, conversely, how to infer parametric conditions underlying distinct cell fates by systematic perturbations is less clear and needs to be further investigated. RESULTS In this article, we present a general computational method by integrating systematic perturbations, unsupervised clustering, principal component analysis, and fitting analysis. The method can be used to to construct maps between distinct cell fates and parametric conditions by systematic perturbations. In particular, there are no needs of accurate parameter measurements and occurrence of bifurcations to establish the maps. To validate feasibility and inference performance of the method, we use toggle switch, inner cell mass, and epithelial mesenchymal transition as model systems to show how the maps are constructed and how system parameters encode essential information on cell fates. The maps tell us how systematic perturbations drive cell fate decisions and transitions, and allow us to purposefully predict, manipulate, and even control cell states. The approach is especially helpful in understanding crucial roles of certain parameter combinations during fate transitions. We hope that the approach can provide us valuable information on parametric or perturbation conditions so some specific targets, e.g. directional differentiation, can be realized. AVAILABILITY AND IMPLEMENTATION No public data are used. The data we used are generated by randomly chosen values of model parameters in certain ranges, and the corresponding parameters are already attached in supplementary materials.
Collapse
Affiliation(s)
- Ruoyu Tang
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Xinyu He
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, Shanghai 200444, China
- Newtouch Center for Mathematics of Shanghai University, Shanghai University, Shanghai 200444, China
| |
Collapse
|
4
|
Katebi A, Chen X, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.29.551111. [PMID: 37577526 PMCID: PMC10418072 DOI: 10.1101/2023.07.29.551111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
Collapse
Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| |
Collapse
|
5
|
Chen M, Wang R. Computational analysis of synergism in small networks with different logic. J Biol Phys 2023; 49:1-27. [PMID: 36580168 PMCID: PMC9958226 DOI: 10.1007/s10867-022-09620-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/12/2022] [Indexed: 12/30/2022] Open
Abstract
Cell fate decision processes are regulated by networks which contain different molecules and interactions. Different network topologies may exhibit synergistic or antagonistic effects on cellular functions. Here, we analyze six most common small networks with regulatory logic AND or OR, trying to clarify the relationship between network topologies and synergism (or antagonism) related to cell fate decisions. We systematically examine the contribution of both network topologies and regulatory logic to the cell fate synergism by bifurcation and combinatorial perturbation analysis. Initially, under a single set of parameters, the synergism of three types of networks with AND and OR logic is compared. Furthermore, to consider whether these results depend on the choices of parameter values, statistics on the synergism of five hundred parameter sets is performed. It is shown that the results are not sensitive to parameter variations, indicating that the synergy or antagonism mainly depends on the network topologies rather than the choices of parameter values. The results indicate that the topology with "Dual Inhibition" shows good synergism, while the topology with "Dual Promotion" or "Hybrid" shows antagonism. The results presented here may help us to design synergistic networks based on network structure and regulation combinations, which has promising implications for cell fate decisions and drug combinations.
Collapse
Affiliation(s)
- Menghan Chen
- Department of Mathematics, Shanghai University, Shanghai, 200444, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, Shanghai, 200444, China.
| |
Collapse
|
6
|
Clauss B, Lu M. A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions. iScience 2023; 26:106029. [PMID: 36824273 PMCID: PMC9941213 DOI: 10.1016/j.isci.2023.106029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 12/19/2022] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
One of the major challenges in biology is to understand how gene interactions collaborate to determine overall functions of biological systems. Here, we present a new computational framework that enables systematic, high-throughput, and quantitative evaluation of how small transcriptional regulatory circuit motifs, and their coupling, contribute to functions of a dynamical biological system. We illustrate how this approach can be applied to identify four-node gene circuits, circuit motifs, and motif coupling responsible for various gene expression state distributions, including those derived from single-cell RNA sequencing data. We also identify seven major classes of four-node circuits from clustering analysis of state distributions. The method is applied to establish phenomenological models of gene circuits driving human neuron differentiation, revealing important biologically relevant regulatory interactions. Our study will shed light on a better understanding of gene regulatory mechanisms in creating and maintaining cellular states.
Collapse
Affiliation(s)
- Benjamin Clauss
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA,Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA,The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA,Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA,The Jackson Laboratory, Bar Harbor, ME 04609, USA,Corresponding author
| |
Collapse
|
7
|
Caranica C, Lu M. A data-driven optimization method for coarse-graining gene regulatory networks. iScience 2023; 26:105927. [PMID: 36698721 PMCID: PMC9868542 DOI: 10.1016/j.isci.2023.105927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.
Collapse
Affiliation(s)
- Cristian Caranica
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA,The Jackson Laboratory, Bar Harbor, ME 04609, USA,Corresponding author
| |
Collapse
|
8
|
Su K, Katebi A, Kohar V, Clauss B, Gordin D, Qin ZS, Karuturi RKM, Li S, Lu M. NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 PMCID: PMC9793520 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
Collapse
Affiliation(s)
- Kenong Su
- Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA
| | - Ataur Katebi
- Department of Bioengineering|, Northeastern University, Boston, MA, 02115, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA
| | - Vivek Kohar
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Benjamin Clauss
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA
- Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA, 02111, USA
| | - Danya Gordin
- Department of Bioengineering|, Northeastern University, Boston, MA, 02115, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - R Krishna M Karuturi
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
- Graduate School of Biological Sciences & Eng., University of Maine, Orono, ME, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Mingyang Lu
- Department of Bioengineering|, Northeastern University, Boston, MA, 02115, USA.
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA.
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA.
- Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA, 02111, USA.
| |
Collapse
|
9
|
Huang L, Clauss B, Lu M. What Makes a Functional Gene Regulatory Network? A Circuit Motif Analysis. J Phys Chem B 2022; 126:10374-10383. [PMID: 36471236 PMCID: PMC9896654 DOI: 10.1021/acs.jpcb.2c05412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
One of the key questions in systems biology is to understand the roles of gene regulatory circuits in determining cellular states and their functions. In previous studies, some researchers have inferred large gene networks from genome wide genomics/transcriptomics data using the top-down approach, while others have modeled core gene circuits of small sizes using the bottom-up approach. Despite many existing systems biology studies, there is still no general rule on what sizes of gene networks and what types of circuit motifs a system would need to achieve robust biological functions. Here, we adopt a gene circuit motif analysis to discover four-node circuits responsible for multiplicity (rich in dynamical behavior), flexibility (versatile to alter gene expression), or both. We identify the most reoccurring two-node circuit motifs and the co-occurring motif pairs. Furthermore, we investigate the contributing factors of multiplicity and flexibility for large gene networks of different types and sizes. We find that gene networks of intermediate sizes tend to have combined high levels of multiplicity and flexibility. Our study will contribute to a better understanding of the dynamical mechanisms of gene regulatory circuits and provide insights into rational designs of robust gene circuits in synthetic and systems biology.
Collapse
Affiliation(s)
- Lijia Huang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Benjamin Clauss
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
- Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Mingyang Lu
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
- Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| |
Collapse
|
10
|
Sangeet S, Sarkar R, Mohanty SK, Roy S. Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method. J Phys Chem B 2022; 126:7895-7905. [PMID: 36178371 PMCID: PMC9534491 DOI: 10.1021/acs.jpcb.2c04574] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/16/2022] [Indexed: 02/07/2023]
Abstract
The emergence of SARS-CoV-2 and its variants that critically affect global public health requires characterization of mutations and their evolutionary pattern from specific Variants of Interest (VOIs) to Variants of Concern (VOCs). Leveraging the concept of equilibrium statistical mechanics, we introduce a new responsive quantity defined as "Mutational Response Function (MRF)" aptly quantifying domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2 based on its evolutionary database. As the evolution transits from a specific variant to VOC, we find that the evolutionary crossover is accompanied by a dramatic change in MRF, upholding the characteristic of a dynamic phase transition. With this entropic information, we have developed an ancestral-based machine learning method that helps predict future domain-specific mutations. The feedforward binary classification model pinpoints possible residues prone to future mutations that have implications for enhanced fusogenicity and pathogenicity of the virus. We believe such MRF analyses followed by a statistical mechanics augmented ML approach could help track different evolutionary stages of such species and identify a critical evolutionary transition that is alarming.
Collapse
Affiliation(s)
- Satyam Sangeet
- Department of Chemical Sciences, Indian Institute of Science
Education and Research Kolkata, Kolkata, West Bengal741246,
India
| | - Raju Sarkar
- Department of Chemical Sciences, Indian Institute of Science
Education and Research Kolkata, Kolkata, West Bengal741246,
India
| | - Saswat K. Mohanty
- Department of Chemical Sciences, Indian Institute of Science
Education and Research Kolkata, Kolkata, West Bengal741246,
India
| | - Susmita Roy
- Department of Chemical Sciences, Indian Institute of Science
Education and Research Kolkata, Kolkata, West Bengal741246,
India
| |
Collapse
|
11
|
Kohar V, Gordin D, Katebi A, Levine H, Onuchic JN, Lu M. Gene Circuit Explorer (GeneEx): an interactive web-app for visualizing, simulating and analyzing gene regulatory circuits. Bioinformatics 2021; 37:1327-1329. [PMID: 33279968 PMCID: PMC8599779 DOI: 10.1093/bioinformatics/btaa785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 06/08/2020] [Accepted: 09/04/2020] [Indexed: 11/12/2022] Open
Abstract
SUMMARY GeneEx is an interactive web-app that uses an ODE-based mathematical modeling approach to simulate, visualize and analyze gene regulatory circuits (GRCs) for an explicit kinetic parameter set or for a large ensemble of random parameter sets. GeneEx offers users the freedom to modify many aspects of the simulation such as the parameter ranges, the levels of gene expression noise and the GRC network topology itself. This degree of flexibility allows users to explore a variety of hypotheses by providing insight into the number and stability of attractors for a given GRC. Moreover, users have the option to upload, and subsequently compare, experimental gene expression data to simulated data generated from the analysis of a built or uploaded custom circuit. Finally, GeneEx offers a curated database that contains circuit motifs and known biological GRCs to facilitate further inquiry into these. Overall, GeneEx enables users to investigate the effects of parameter variation, stochasticity and/or topological changes on gene expression for GRCs using a systems-biology approach. AVAILABILITY AND IMPLEMENTATION GeneEx is available at https://geneex.jax.org. This web-app is released under the MIT license and is free and open to all users and there is no mandatory login requirement. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Vivek Kohar
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Danya Gordin
- The Jackson Laboratory, Bar Harbor, ME 04609, USA.,Center for Theoretical Biological Physics.,Department of Bioengineering
| | - Ataur Katebi
- The Jackson Laboratory, Bar Harbor, ME 04609, USA.,Center for Theoretical Biological Physics.,Department of Bioengineering
| | - Herbert Levine
- Center for Theoretical Biological Physics.,Department of Bioengineering.,Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - José N Onuchic
- Center for Theoretical Biological Physics.,Department of Physics and Astronomy.,Department of Chemistry.,Department of Biosciences, Rice University, Houston, TX 77005, USA
| | - Mingyang Lu
- The Jackson Laboratory, Bar Harbor, ME 04609, USA.,Center for Theoretical Biological Physics.,Department of Bioengineering
| |
Collapse
|