“Modelling leads to more stable and better optimized systems”

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“Modelling leads to more stable and better optimized systems”

Optogenerapy partner GeneXplain has built a mathematical model to further improve and optimize the optogenetic pathway of Optogenerapy’s medical device. The model, which allows to predict the behaviour of the cells involved in the production of IFN- β drug for patients suffering from multiple sclerosis, can simulate different scenarios, helping to explore the system without spending resources on experiments.


In this interview, Kamilya Altynbekova, software developer and data analyst at GeneXplain, details what is modelling and how Optogenerapy model was developed, as well as how modelling can play a big role to further improve and optimize engineered pathways and lead to a more stable resulting system.


Could you please explain us what modelling is?


We should first start with defining what is a model in order to understand what is modelling. In general, a model is an abstract representation of an object or process. The representation is simplified in order to concentrate on the most relevant and distinctive characteristics, while omitting the non-essential details.


The process of determining the relevant features and the way they should be represented for a particular case is called modelling. The main goal is to effectively illustrate, explain and communicate complex concepts and to predict its behaviour in real life.


How modelling applies to Optogenerapy project?


We modelled the optogenetic pathway by concentrating on the key genes and proteins involved in signal transduction, keeping out many other existing elements and ongoing activities in the cell. The model created allows us to predict the way the medical device will function for further improvement and optimization.


Which technologies did you use for modelling the optogenetic pathway?


We used geneXplain platform, which is built upon two main components, including a set of analysis and modelling methods and manually curated databases. The platform, based on the open source Java environment BioUML, has been optimized to support SBML (Systems Biology Markup Language) and SBGN (Systems Biology Graphic Notation) standards, allowing the models built on the platform to be exported and used with other tools.


How is the model simulation performed?


To build the model we used the data obtained in actual experiments conducted by Optogenerapy partnersto deduce the mathematical equations that can generally describe the interaction between various participants of the pathway.


The dynamic simulation of the model is done with the help of the Ordinary Differential Equation (ODE) simulation engine, which uses equations and given initial settings, like light induction time, to calculate the values of the variables. All the interactions between the elements of the pathway are modelled with differential equations according to the reaction kinetics, meaning that the rates of reactions are related to the concentrations of the species represented as variables in the model.


Besides modelling tools, did you use other analysis methods for defining the optogenetic pathway activation?

Yes, definitely. Some other analysis methods from geneXplain platform are applied to the Optogenerapy project, such as the Composite Module Analyst (CMA). Since the beginning of Optogenerapy this algorithm was updated and a newer version called CMAcorrel is applied in the project. The idea is to analyze the gene expression data in the used cells and try to reconstruct the proteins  interactions. By identifying key nodes that may effect optogenetic pathway, we can propose how to improve the currently used promoter construct and make it more reliable.

Which challenges did you encounter when defining the optogenetic pathway?


When modelling, it is very important to identify the most relevant characteristics of the system to model. In the case pathways models, particularly, it is essential to define which elements are participating and may have an effect on the signal transduction.


In order to do that, we needed to work closely with the partners to define the key nodes of the system, search the databases to find out what molecules may influence the system performance and check some of them experimentally. Encounter the key elements of the system to be able to perform a good model may be the greater challenge we found in the whole process.


How modelling can improve Optogenerapy’s project implant?


The computational model developed for this project can be used to run simulations under different conditions to better understand and predict the behavior of the engineered pathway. The predictions of the model can be used to improve and optimize the Optogenetic pathway, which leads to a more stable resulting system and better performance.


Which type of simulations could we perform with the model?


The fitted model can be used to extrapolate more data for the pathway analysis. Let’s say that we measured the production of IFN-β in the cells after the induction with NIR-light for 2 and 4 hours. The generalized model can predict the level of production for any other time period, helping to explore the system better without spending the resources on the actual experiments.


Could the modelling and analysis algorithms developed within the project be used for other applications?


Yes, the modelling engine developed, as well as the mathematical equations and principles used, can be applied to a wide range of biological and engineered pathways.


On the other hand, the methods used for the transcription factors, promoter and pathway analysis that are used in Optogenerapy project can also be used as a basis for drug-target discoveries.


About Kamilya Altynbekova

Kamilya Altynbekova joined geneXplain company as a software developer and data analyst after graduation from Nazarabyev University (Astana, Kazakhstan) with bachelor’s degree in computer science. Being always passionate about the technologies and natural sciences, Kamilya found it a great opportunity to work in the company with the focus on bioinformatics.