Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming
Project Details
Program
BioScience
Field of Study
computer science, mathematical modeling, machine learning, systems biology, bioscience
Division
Biological and Environmental Sciences and Engineering
Faculty Lab Link
Project Description
Recent work on stem cells and different mature specialized cells in different systems/organs (neurons, blood cells,) has revealed a stunning plasticity and capacity of reprogramming cells. For example, mature cells can be reprogrammed into pluripotent stem cells, and exciting work on engineered design of tissues and organs (organoids) are underway. On the one hand the community has since the sequencing of the human genome produced very efficient tools to read off the corresponding molecular events accompanying reprogramming and engineering of cells. Recently, the discovery of the CRISPR techniques has equipped us with unprecedented opportunities for precise writing or editing of the genomes. These developments in fundamental biology and biotechnology are currently opening new tools and perspectives of vital significance for drug development, regenerative medicine, synthetic biology, and personalized medicine. Yet, in essence all these efforts require and would be greatly facilitated if we could advance from correlative data-analysis to a predictive discovery of which interventions (edits, engineering) are producing which effects. Thus, we are facing the fundamental problem on how to discover causal relations from data, or in other words, can we derive quantitative predictive laws fromdata?We offer internships forseveral highly motivatedbachelor (B.Sc.) ormaster (M.Sc.) students who will explore this fundamental question primarily from a computational standpoint. This includes using high-performance simulations of dynamical models, and design of algorithms in a controlled in-silico environment. For example, to identify (a) efficient algorithms for generation of ensembles of dynamical models, (b) use supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets, (c) to perform deep data-driven analysis of computational models in biology, (d) pursue investigations of transfer entropy and related techniques for system identification. These tools will be tested utilizing rich and recent molecular data on cellular reprogramming.
About the Researcher
Jesper Tegner
Professor, Bioscience
Affiliations
Education Profile
- Ph.D. Medicine/Medicine Doctor, Karolinska Institutet, 1997
- Advanced Ph.D. courses in pure and computational mathematics (corresponding 2 years full time), Royal Institute for Technology & Stockholm University, 1992-1996
- B.Sc. Medicine (Med Kand, Physician Program), Karolinska Institutet, 1990
- B.Sc. Philosophy, Stockholm University, 1990
- B.Sc. Mathematics, Stockholm University, 1988
Research Interests
Following his PhD (09/1997), he was appointed assistant professor in Computer Science (Dept. of Computer Science and Numerical Analysis, Engineering School, 06/1998). He took a leave of absence, for two postdocs (08/1998-07/2001), Sloan Center for Computational Neuroscience, & Center for Biodynamics, Dept. of Biomedical Engineering, (Boston, US). He was awarded a Swedish Wennergren Foundation Fellowship, 5-year visiting scientist position & faculty position upon return, (first of its kind) & 3-year Alfred P. Sloan Fellowship in Computational Science (US). Upon his return to Sweden (08/2001), awarded a new assistant professor position in Computer Science with special reference to Bioinformatics (Stockholm Center for Bioinformatics), but was awarded a new chaired full professorship in Computational Biology (Dept. of Physics, Engineering School, 02/2002), first of its kind in Sweden. In 10/2009, he was specially recruited to become a strategic chaired full professor in Computational Medicine, appointed a Director for the Computational Medicine Division, at the Dept. of Medicine, Karolinska Institutet & Division of Clinical Epidemiology, Karolinska Hospital. In 06/2014, he was named Faculty at the Science for Life Laboratory (SciLifeLab -A National Center for Molecular Biosciences, Stockholm). Since 08/2016 he is a Professor in Bioscience (BESE) and Professor in Computer Science (CEMSE) at KAUST. He is an ERC co-investigator (2013-) on causal discovery, ranked as outstanding (highest distinction among faculty, ERA 2012) at Karolinska Institutet, winner of the international DREAM competition (2008) on network inference, founder of two BioIT companies, and in 2005 he became the winner of the national award for founding the most promising start-up company of the year. He serves on several editorial boards including being an Associate Editor a- Frontiers in Big Data a- Medicine and Public Health (joint section with Machine Intelligence and Artificial Intelligence), Acting Section Editor on Clinical and Translational Systems Biology in Current Opinion on Systems Biology, Editorial Board of Complex Systems (first in the field, founded 1987 by Stephen Wolfram), Editorial Board of BMC Systems Biology, Senior Editor in Progress in Preventive Medicine, and Editorial Board of Neurology: Neuroinflammation & Neurodegeneration His research targets the circuit architecture and algorithms enabling learning and adaptation in living systems and synthetic machines. Since cells are fundamental building blocks (c.f. atoms in the periodic table) of all living matter, we interrogate their intrinsic circuitry, i.e. networks, by exploiting experimental single cell genomics techniques for temporal multi-molecular profiling, deep imaging, live-cell imaging, and molecular interventions using genomic editing techniques. Such high-dimensional and multi-dimensional data are deciphered by means of advanced bioinformatics, mathematical modeling, and machine learning techniques to uncover the fundamental dynamical equations governing cellular decisions, differentiation, reprogramming, and learning. Theory and algorithms for designing causal discovery machines, are developed by cross-pollinating algorithmic information theory, dynamical systems, inverse modeling, data-driven machine learning techniques, including deep learning architectures. Our applications from this program are threefold; engineered cellular control (reprogramming of stem cells, immune cells, and neurons), software development (data-management, bioinformatics software, causal discovery and machine learning algorithms), and clinical translation (currently Melanoma, Breast Cancer, Multiple Sclerosis, Alzheimer, Frontal Dementia, and Retinal diseases). At the core of our program we posit that such fundamental (causal) dynamical equations drive ""breath of life"" from matter. Since living systems can learn, represent, predict, and in extension understand their local environments, across several orders-of-magnitude of spatial-temporal scales, we believe that the formal deconstruction and reconstruction of such generative mechanisms, evolved over billions of years, will guide the design of algorithmic autonomous learning machines.Selected Publications
- Zenil, Kiani and Tegner. Algorithmic Information Dynamics, Monograph, Cambridge University Press, 2019
- Zenil, Kiani and Tegner. Algorithmic Cognition, Monograph, Springer, 2019
- Zenil, Kiani and Tegner. Graph and Tensor Complexity, Monograph, Springer, 2019
- Zenil et al. A review of graph and network complexity from an algorithmic information perspective, 20, (551), Entropy, 2018
- Kular et al. DNA methylation at HLA as a mediator of the DRB1*15:01 risk haplotype and a novel protective variant in Multiple Sclerosis Nature Communications, 2018
- Zenil et al. A decomposition method for global evaluation of Shannon entropy and local estimation of algorithmic complexity 20, (605) Entropy, 2018
- Kotelnikova et al. Dynamics and heterogeneity of brain damage in multiple sclerosis, PLoS computational biology 13 (10), e1005757, 2017
- H Zenil, NA Kiani, J TegnA©r, Low-algorithmic-complexity entropy-deceiving graphs, Physical Review E 96 (1), 012308, 2017
- Gomez-Cabrero, et al. On the emergence of short advanced courses in Systems Medicine and Biology: recent experiences and recommended guidelines. Cell Systems September, 2017
- David Gomez-Cabrero, and Jesper TegnA©r. Iterative Systems Biology for Medicine a- time for advancing from networks signature to mechanistic equations Current Opinion in Systems Biology May, 2017
- Manuel Zeitelhofera, et al. Functional genomics analysis of vitamin D effects on CD4+ T-cells in vivo in experimental autoimmune encephalomyelitis PNAS February, 2017
- Hill, S. et al. Empirical assessment of causal network learning through a community-based effort Nature Methods, 2016.
- RodrAguez-Cortez, et al. Monozygotic twins discordant for common variable immunodeficiency reveal impaired DNA demethylation during naA¯ve-to-memory B-cell transition Nature Communications 6/17, 2015.
- Yang, et al. VEGF-B promotes cancer metastasis through a VEGF-Aa-independent mechanism and serves as a marker of poor prognosis for cancer patients PNAS 5/19, 2015.
- Lindholm, et al. An integrative analysis reveals coordinated reprogramming of the epigenome in human skeletal muscle after training Epigenetics.12/2, 1557-69. 2014
- Gomez-Cabrero et al. Data integration in the era of omics: current and future challenges. BMC Systems Biology, vol 8 (Supp 2):I1), 2014
- Marabita, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform Volume 8, Issue 3 Epigenetics March, 8(3), 333a-46., 2013
- Teschendorff et al. S A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinum 450k DNA methylation data Bioinformatics. Jan 15;29(2):189-96, 2013
- Ravasi et al. An atlas of combinatorial transcriptional regulation in mouse and man, Cell. Mar 5;140(5):744-52. 2010
- Suzuki et al. A complex transcriptional network controls growth arrest and differentiation in a human myeloid leukemia cell line, Nature Genetics May;41(5):553-62. Epub 2009 Apr 19, 2009
- Pena, J., Nilsson, R, BjA¶rkegren, J. and TegnA©r, J. An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. Journal of Machine Learning Research 2009.
- Fredrik Edin, et al. Mechanism for Top-down Control of Working Memory Capacity. PNAS Apr 21;106(16):6802-7. Epub Apr 1. 2009
- PeA±a, J. M., Nilsson, R., BjA¶rkegren, J. and TegnA©r, J. Towards scalable and Data Efficient Learning of Markov Boundaries, International Journal of Approximate Reasoning, Volume 45, Issue 2, Pages 211-232 2007
- TegnA©r, J. and BjA¶rkegren, J. Perturbations to uncover gene networks. Trends in Genetics, Jan;23(1):34-41, 2007
- Nilsson, R., PeA±a, J. M., BjA¶rkegren J., and J. TegnA©r, Consistent feature selection for pattern recognition in polynomial time, Journal of Machine Learning Research, 8(March):589-612, 2007
- Carninci, T. et al. The transcriptional landscape of the mammalian genome, Science. Sep 2;309 (5740):1559-63. 2005
- TegnA©r, J., et al. Reverse engineering gene networks -- integrating genetic perturbations with dynamical modeling. PNAS. 100,5944-5949, 2003.
Desired Project Deliverables
Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated by dynamical causal systems, be it computational or those we find in the natural world, i.e. living cells. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will get a good hands-on perspective on the frontiers in dynamical systems and bioscience using state-of-the-art simulation and machine learning tools.
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Be part of the journey with VSRP
3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year