MIG Workshop: Mechanistic inference from single-cell data

Parameter inference is essential for interpreting empirical data and validating mechanistic models. In this workshop, we will cover the basics of parameter estimation and uncertainty quantification by fitting stochastic models of gene expression to single-cell RNA sequencing data. We will implement maximum likelihood and Bayesian inference methods in Julia and explore the issue of parameter identifiability along with its practical implications for model selection. We will also discuss how to distinguish the different sources of biological and technical noise present in high-throughput single-cell data.

This workshop builds on top of the fundamentals of biochemical modelling covered in October workshop of the series.

Pre-requisites: some familiarity with statistics and programming would be beneficial.

Lead Instructors: Augustinas Sukys & Kaan Ocal

Please note that the $25 booking fee will be fully refunded upon workshop attendance.

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To secure your spot in a workshop, we require a nominal fee, which will be fully refunded upon your attendance at the workshop. This policy helps ensure commitment and allows us to manage resources effectively.

Cancellations Made in Advance: If you need to cancel your registration, you must notify us at least 72 hours before the workshop start time. In such cases, the nominal fee will be fully refunded. Refunds will happen within one week of the event.

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Illness or Exceptional Circumstances: Cancellations due to illness or exceptional circumstances will be considered on a case-by-case basis, and refunds will be provided at MIG's discretion.

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