I am a physicist at heart, systems biologist by day, and fiction writer by night.
My main research interest is in the field of stochastic gene expression, especially in the context of large gene regulatory networks. I focus on two aspects of this field: 1) developing new methods for efficient simulation of stochastic phenomena in gene regulatory networks; and 2) using these methods to discover correlations between stochasticity (for example the levels of intrinsic noise) and the topology of gene regulatory networks. For example, I would like to answer the question: are scale-free networks more or less noisy on average than random networks? Since biological networks are believed to be scale-free, this question would shed light on whether and/or why this is the case.
Recently, I have developed a stochastic simulation algorithm, which I named HSSA (hybrid stochastic simulation algorithm), that is up to 450 times faster than conventional algorithms, e. g. the Gillespie algorithm (GA). I am currently using the HSSA to generate a large set of artificial data on mRNA distributions (up to 50 genes). Simulations on this scale are extremely time consuming with conventional Monte Carlo methods. However, with the HSSA it is possible to simulate many large gene regulatory networks in the time it takes the GA to simulate only one. The goal of these data is to discover new methods of relating mRNA distributions to network topologies. Such methods can then be applied to distributions of real single-cell mRNA data in order to predict gene-gene interactions. I am also developing a graphical user interface to make the HSSA more accessible to other researchers.
Whenever time permits, I make YouTube videos on the subject of General Relativity intended for physics undergraduates.
Contact Jaroslav at firstname.lastname@example.org
Lecture series on General Relativity: https://www.youtube.com/user/dXoverdteqprogress