Jasmin Fisher is a Senior Researcher at Microsoft Research Cambridge in the Programming Principles & Tools group. She is also an Associate Professor of Systems Biology in the Department of Biochemistry at the University of Cambridge. She is a member of the Cancer Research UK Cambridge Centre, Cambridge Systems Biology Centre and the Cambridge Stem Cell Institute, and in 2016 she was elected Fellow of Trinity Hall, Cambridge.

Jasmin received her Ph.D. in Neuroimmunology from the Weizmann Institute of Science in 2003. She then started her work on the application of formal methods to biology as a postdoctoral fellow in the Department of Computer Science at the Weizmann Institute, and then continued to work on the development of novel formalisms and tools that are specifically-tailored for modelling biological processes in the School of Computer Science at the EPFL in Switzerland. In 2007, Jasmin joined the Microsoft Research Lab in Cambridge. In 2009, she was also appointed a Research Group Leader in the University of Cambridge.

Jasmin has devoted her career to develop methods for Executable Biology; her work has inspired the design of many new biological studies. She is a pioneer in using formal verification methods to analyse mechanistic models of cellular processes and disease. Her research group focuses on cutting-edge technologies for modelling molecular mechanisms of cancer and the development of novel drug therapies.

Executable Biology
Our research focuses on the design and analysis of executable computer algorithms describing biological phenomena, in particular cancer biology. We call this approach Executable Biology. These kinds of models hold great promise for new discoveries in a wide variety of biological systems. Once an executable model has been built of a particular system, it can be used to get a global dynamic picture of how the system responds to various perturbations. In addition, preliminary studies can be quickly performed using executable models, saving valuable laboratory time and resources for only the most promising avenues.

Research Overview
Our work is focused on two main directions:

  1. The use of different formalisms to create executable models of biological phenomena, aiming to gain new insights into the molecular mechanisms underlying the fundamental question of cell fate determination (or in other words – how cells make the decision to develop into a particular cell type) during the course of normal development (e.g., our work on blood stem cells development), and cancer (e.g., stem cell differentiation in mammalian epidermis, EGFR/Notch/Wnt crosstalk in cancer cells, regulation of leukemic blood stem cells).
  2. The development of tools and design of algorithms that are specifically tailored for modelling and analyzing biological networks (e.g., bounded-asynchrony, synthesis algorithms for gene regulatory networks). We put a lot of emphasis on constructing user-friendly tools (i.e., visual, flexible), in order to facilitate the integration of such computational tools as mainstream techniques in biological and medical research (e.g., Bio Model Analyzer).


Single Cell Network Synthesis

Established: January 4, 2016

The Single Cell Network Synthesis tool (SCNS) is a tool for the reconstruction and analysis of executable models from single-cell gene expression data, which supports easy deployment of computation to the cloud for performance and control via a web-based graphical interface. SCNS can be used for understanding differentiation, developmental, or reprogramming journeys. SCNS takes single-cell qRNA or RNA-sequencing data, and treats expression profiles as binary states, where a value of 1 indicates a gene is expressed and 0 indicates…

Predictive Dynamic Model of Glioblastoma Early Development

Established: September 2, 2013

Glioblastoma multiforme (GBM) is the most common and most malignant form of brain cancer, being characterised by relentless growth and aggressive invasion into the healthy brain tissue, resulting in extremely poor outcome. Given the complexity and cell heterogeneity observed in this type of tumour, it is natural to study its development from an integrative and systems perspective. This project aims to develop an executable hybrid model of tumour growth. The model integrates a multi-level description…

Whole-Organism Model of C. Elegans Development

Established: January 6, 2013

The nematode C. elegans, with its invariant lineage, serves as a model organism for the study of development. We aim to create an open-source, extensible whole-organism model of C. elegans development to which the worm community can add new information. In the first stage of this project we use this simulation program to study developmental variance in C. elegans, and in particular how this may arise through perturbations in cell-cycle timing. This early version of…

Decoding transcriptional programs of blood cell development

Established: December 3, 2012

Understanding the mechanisms that govern stem cell self-renewal and cell fate decisions are fundamental to regenerative medicine and to understanding how these mechanisms are perturbed in disease states. Blood cell development (haematopoiesis) has long stood as a paradigm for studying stem cell biology. Genes encoding transcriptional regulators and components of cell signalling pathways are recognised as powerful regulators of developmental processes including the development of blood cells. The interplay of sensing the external environment (through…

Mechanisms of stem cell homeostasis during C. elegans germline development

Established: October 1, 2012

The establishment of homeostasis between cell growth, differentiation and apoptosis is of key importance for organogenesis. Stem cells respond to temporally and spatially regulated signals by switching from mitotic proliferation to asymmetric cell division and differentiation. Executable computer models of signalling pathways can accurately reproduce a wide range of biological phenomena by reducing detailed chemical kinetics to a discrete, finite form. Moreover, coordinated cell movements and physical cell-cell interactions are required for the formation of…

The Executable Path to Myc

Established: August 6, 2012

Myc is a key oncogene in various cancers occurring across a diverse range of tissues. In order to better understand and treat these cancers, it is vital that we understand how the opposing functions of Myc, proliferation and apoptosis, are balanced and regulated in healthy tissue, and how this goes wrong in cancer. In collaboration with the Evan lab (Department of Biochemistry, University of Cambridge) we aim to build comprehensive executable models of the Myc…

Executable network models to identify new treatment combinations for leukaemia

Established: June 4, 2012

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. We previously built a CML network-model using BMA,…



Bringing LTL Model Checking to Biologists
Zara Ahmed, David Benque, Sergey Berezin, Jasmin Fisher, Anna Caroline E. Dahl, Benjamin A. Hall, Samin Ishtiaq, Jay Nanavati, Nir Piterman, Maik Riechert, Nikita Skoblov, in Verification, Model Checking and Abstract Interpretation (VMCAI), Springer, January 15, 2017, View abstract, Download PDF




Single cell analyses of regulatory network perturbations using enhancer targeting TAL Effectors suggest novel roles for PU.1 during haematopoietic specification
Adam C. Wilkinson, Viviane K. S. Kawata, Judith Schütte, Duefei Gao, Stella Antoniou, Claudia Baumann, Steven Woodhouse, Rebecca Hannah, Yosuke Tanaka, Gemma Swiers, Victoria Moignard, Jasmin Fisher, Shimauchi Hidetoshi, Marloes R. Tijssen, Marella F. T. R. de Bruijn, Pentao Liu, Berthold Göttgens, in Development 141, The Company of Biologists, August 1, 2014, View abstract, Download PDF, View external link















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February 2, 2016


Sita Narayan-Dinanauth, Jasmin Fisher


Microsoft Research

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BioModelAnalyzer Trailer


February 2, 2016


Sita Narayan-Dinanauth, Jasmin Fisher


Microsoft Research

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BMA: Bio Model Analyzer


February 1, 2015


Jasmin Fisher, Alex Taylor


Microsoft Research Cambridge


Group Members

  • Steven Woodhouse (Postdoc, MSRC)
  • Matthew Clarke (PhD student, University of Cambridge)
  • Joseph McAbee (MD/PhD student, University of Cambridge, joint with Colin Watts)
  • Chee Yee Lim (PhD student, University of Cambridge, joint with Bertie Gottgens)
  • Simon Lam (Part III student, University of Cambridge; joint with Gerard Evan)


  • Marc Schaub (M.Sc. student, EPFL, 2005-2006)
  • Dennis Wang (M.Sc. student, Cambridge University, 2008)
  • Antje Beyer (M.Sc. student, Cambridge University, 2008)
  • Maria Mateescu (Ph.D. student, EPFL, joint with Tom Henzinger, 2006-2009)
  • Luke Church (Ph.D. student, Cambridge University, 2007-2009)
  • Avital Sadot (Post-doc, joint with David Harel, 2007-2010)
  • Garth Ilsley (Ph.D. student, EBI, Cambridge, 2007-2010)
  • Peter Ackermann (M.Sc. student, Cambridge University 2011, joint with Steve Oliver)
  • Lucinda Moore (M.Sc. student, Cambridge University 2012, joint with Bertie Gottgens)
  • Ryan Chuang (M.Sc. student, Cambridge University 2012, joint with Bertie Gottgens)
  • Antje Beyer (Ph.D. student, Cambridge University 2009-2012)
  • Ariel Feiglin (Ph.D. student, Bar-Ilan University, joint with Yanay Ofran)
  • Anthony Gitter (post-doc, MSR-NE, 2012-2014)
  • Ben Hall (post-doc, MSRC, 2012-2014)
  • Victoria Wang (M.Sc. student, University of Cambridge 2015, joint with Gerard Evan)
  • Ali Sinan Koksal (Ph.D. student, UC Berkeley, joint with Ras Bodik)
  • Moritz Reiterer (M.Sc. student, University of Cambridge 2016, joint with Gerard Evan)
  • Steven Woodhouse (Ph.D. student, University of Cambridge 2013-2016, joint with Bertie Gottgens)
  • Emilie Feral (M.Phil. student, University of Cambridge 2016)