Instructor, Stanford University (2017 - Present)
Honors & Awards
Travel fellowship for ISMB international conference in Boston, USA, International Society of Computational Biology (July 2010)
Postdoctoral Scholar, Stanford University (2012-2016)
Travel fellowship for ISCB international conference in Bamako, Mali, International Society of Computational Biology (December 2009)
PhD thesis: Magna cum laude, University of Regensburg, Germany (December 2011)
Boards, Advisory Committees, Professional Organizations
Member, International Society of Computational Biology (2009 - Present)
Planning member and Chair, Integrative Cancer Biology Program (ICBP) Junior Investigator Meeting (2013 - 2013)
Associate Member, American Association for Cancer Research. (2015 - Present)
Doctor of Natural Science, University of Regensburg, Germany, Statistical Bioinformatics (2011)
Master of Science, University of Hasselt, Belgium, Biostatistics (2006)
Master of Science, Limburgs Center for Statistics, Belgium, Applied Statistics (2005)
Bachelor of Science, University of Buea, Cameroon, Mathematics (2002)
Current Research and Scholarly Interests
I am passionate about using computational systems biology to study normal and disease progression as well as improve patient health by developing better diagnostic and treatment strategies using high throughput technologies.
Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer.
Special Conference on Computational and Systems Biology of Cancer
View details for DOI 10.1158/1538-7445
Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity
Cell Stem Cell
2017; 21 (1): 78 - 90.e6
View details for DOI 10.1016/j.stem.2017.06.014
Visualization and cellular hierarchy inference of single-cell data using SPADE.
2016; 11 (7): 1264-1279
High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.
View details for DOI 10.1038/nprot.2016.066
View details for PubMedID 27310265
- ARF: Connecting senescence and innate immunity for clearance AGING-US 2015; 7 (9): 613-615
p19ARF is a critical mediator of both cellular senescence and an innate immune response associated with MYC inactivation in mouse model of acute leukemia
2015; 6 (6): 3563-3577
MYC-induced T-ALL exhibit oncogene addiction. Addiction to MYC is a consequence of both cell-autonomous mechanisms, such as proliferative arrest, cellular senescence, and apoptosis, as well as non-cell autonomous mechanisms, such as shutdown of angiogenesis, and recruitment of immune effectors. Here, we show, using transgenic mouse models of MYC-induced T-ALL, that the loss of either p19ARF or p53 abrogates the ability of MYC inactivation to induce sustained tumor regression. Loss of p53 or p19ARF, influenced the ability of MYC inactivation to elicit the shutdown of angiogenesis; however the loss of p19ARF, but not p53, impeded cellular senescence, as measured by SA-beta-galactosidase staining, increased expression of p16INK4A, and specific histone modifications. Moreover, comparative gene expression analysis suggested that a multitude of genes involved in the innate immune response were expressed in p19ARF wild-type, but not null, tumors upon MYC inactivation. Indeed, the loss of p19ARF, but not p53, impeded the in situ recruitment of macrophages to the tumor microenvironment. Finally, p19ARF null-associated gene signature prognosticated relapse-free survival in human patients with ALL. Therefore, p19ARF appears to be important to regulating cellular senescence and innate immune response that may contribute to the therapeutic response of ALL.
View details for Web of Science ID 000352696200012
View details for PubMedID 25784651
CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells
PLOS COMPUTATIONAL BIOLOGY
2014; 10 (7)
A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.
View details for DOI 10.1371/journal.pcbi.1003664
View details for Web of Science ID 000339890900004
View details for PubMedID 25078380
Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction
2014; 30 (3): 414-419
For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network.In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development.The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics firstname.lastname@example.org or email@example.comSupplementary Materials are available at Bioinfomatics online.
View details for DOI 10.1093/bioinformatics/btt696
View details for Web of Science ID 000331271100016
View details for PubMedID 24292937
Wnt secretion is required to maintain high levels of Wnt activity in colon cancer cells
Aberrant regulation of the Wnt/β-catenin pathway has an important role during the onset and progression of colorectal cancer, with over 90% of cases of sporadic colon cancer featuring mutations in APC or β-catenin. However, it has remained a point of controversy whether these mutations are sufficient to activate the pathway or require additional upstream signals. Here we show that colorectal tumours express elevated levels of Wnt3 and Evi/Wls/GPR177. We found that in colon cancer cells, even in the presence of mutations in APC or β-catenin, downstream signalling remains responsive to Wnt ligands and receptor proximal signalling. Furthermore, we demonstrate that truncated APC proteins bind β-catenin and key components of the destruction complex. These results indicate that cells with mutations in APC or β-catenin depend on Wnt ligands and their secretion for a sufficient level of β-catenin signalling, which potentially opens new avenues for therapeutic interventions by targeting Wnt secretion via Evi/Wls.
View details for DOI 10.1038/ncomms3610
View details for Web of Science ID 000326471800002
View details for PubMedID 24162018
Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2009; 106 (16): 6447-6452
Cellular decision making in differentiation, proliferation, or cell death is mediated by molecular signaling processes, which control the regulation and expression of genes. Vice versa, the expression of genes can trigger the activity of signaling pathways. We introduce and describe a statistical method called Dynamic Nested Effects Model (D-NEM) for analyzing the temporal interplay of cell signaling and gene expression. D-NEMs are Bayesian models of signal propagation in a network. They decompose observed time delays of multiple step signaling processes into single steps. Time delays are assumed to be exponentially distributed. Rate constants of signal propagation are model parameters, whose joint posterior distribution is assessed via Gibbs sampling. They hold information on the interplay of different forms of biological signal propagation. Molecular signaling in the cytoplasm acts at high rates, direct signal propagation via transcription and translation act at intermediate rates, while secondary effects operate at low rates. D-NEMs allow the dissection of biological processes into signaling and expression events, and analysis of cellular signal flow. An application of D-NEMs to embryonic stem cell development in mice reveals a feed-forward loop dominated network, which stabilizes the differentiated state of cells and points to Nanog as the key sensitizer of stem cells for differentiation stimuli.
View details for DOI 10.1073/pnas.0809822106
View details for Web of Science ID 000265506800007
View details for PubMedID 19329492