Hi! I am a postdoctoral researcher in the research group of Michael Stumpf at the University of Melbourne. My research is on stochastic modelling of single-cell biology, such as gene expression and cell proliferation. I am also interested in Bayesian methods for single-cell data. I completed my PhD at the University of Edinburgh under the supervision of Guido Sanguinetti and Ramon Grima. Prior to that I was a research assistant in Jakob Macke’s research group, working on likelihood-free inference for biophysical models of neurons.

Research

Randomness in biology: Random events occur at all levels in biology, from individuals (what eye colour would my children have? could they inherit genetic diseases? ) to populations and ecosystems (how do species evolve or become extinct? ). Some of these can have significant impact on our lives, such as what are my chances of getting cancer? and when is the next pandemic going to happen?. I try to understand the origins and consequences of biological noise on the cellular level.

Stochastic modelling: My PhD revolved around the Chemical Master Equation, a mathematical framework to model noisy cellular processes. The Chemical Master Equation is notoriously tricky to handle, and I look for ways to approximate and simplify it - this helps researchers make better predictions with less effort.

Cell populations: How does the behaviour of single cells affect the fate of a population? Cells must constantly make decisions in an unpredictable environment: they must locate food, communicate with each other, and avoid predators or harmful toxins. I combine statistical physics and branching processes to understand how these decisions shape population fitness.

Inference: Observing cells in real time is difficult: measurements are often limited and unreliable. Single-cell biology relies heavily on statistical tools to extract information from what we have. I develop and adapt inference methods for single-cell data, particularly using Likelihood-Free Inference (e.g. Approximate Bayesian Computation), which can be applied to many real-life problems in biology.

Further Reading: For an accessible introduction to the Chemical Master Equation, its approximations and some relevant inference methods I highly recommend the following resources:

• D. Schnoerr, G. Sanguinetti, R. Grima: Approximation and Inference for Stochastic Biochemical Kinetics - a Tutorial Review
• Darren Wilkinson: Stochastic Modelling for Systems Biology

If you want to know more about my research, please get in touch!

Publications

2025

• K. Öcal, M.P.H. Stumpf. The two-clock problem in population dynamics, preprint
  [paper]
• K. Öcal, M.P.H. Stumpf. Cell size distributions in lineages, Phys. Rev. Res. 7(1)
  [paper]

2024

• L. Ham, M.A. Coomer, K. Öcal, R. Grima, M.P.H. Stumpf. A stochastic vs deterministic perspective on the timing of cellular events, Nat. Commun. 15(1)
  [paper] [code]

2023

• K. Öcal. Incorporating extrinsic noise into mechanistic modelling of single-cell transcriptomics, preprint
  [paper] [code]
• K. Öcal, G. Sanguinetti, R. Grima. Model reduction for the Chemical Master Equation: an information-theoretic approach, J. Chem. Phys. 158(11)
  [paper] [code]

2022

• A. Sukys, K. Öcal, R. Grima. Approximating solutions of the Chemical Master Equation using neural networks, iScience 25(9)
  [paper] [code]
• K. Öcal, M.U. Gutmann, G. Sanguinetti, R. Grima. Inference and uncertainty quantification of stochastic gene expression via synthetic models, J. R. Soc. Interface 19(192)
  [paper] [code]

2020

• P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg, J.H. Macke. Training deep neural density estimators to identify mechanistic models of neural dynamics, eLife 9
  [paper] [code]

2019

• K. Öcal, R. Grima, G. Sanguinetti. Parameter estimation for biochemical reaction networks using Wasserstein distances, J. Phys. A 53(3)
  [paper] [code]

2017

• J.-M. Lueckmann, P.J. Goncalves, G. Bassetto, K. Öcal, M. Nonnenmacher, J.H. Macke. Flexible statistical inference for mechanistic models of neural dynamics, Adv. Neural Inf. Process. Syst. 30
  [paper] [code]

Software

A lot of my research involves scientific programming, for which I use Julia, Python, Stan and C/C++. All my code is available on GitHub.

I always welcome questions, issues and pull requests. All my code is intended to be freely used by others and is currently maintained. Apart from code the code linked in the above papers, I have written two Julia packages:

FiniteStateProjection.jl

Implements Finite State Projection algorithms to solve the Chemical Master Equation numerically. Part of Julia’s SciML ecosystem. Don’t forget to check out its sister package MomentClosure.jl, the original inspiration for this work!

Chemostats.jl

Various algorithms to efficiently simulate cell populations and estimate growth rates. This package, together with some of these algorithms in it, is currently in development.

Contact

You can contact me at firstname.lastname@unimelb.edu.au (lastname starts with an ‘o’). I am also active on Github. Feel free to get in touch if you have any questions concerning my research or the software I’ve been working on, or if you wish to contact me for outreach activities.