Dr Sergei Krivov

Research interests

Quantitative analysis of complex biological dynamics.

Life manifests itself through complex dynamics across many scales, from wiggling and jiggling of atoms (protein folding) to transcription dynamics in gene regulatory networks, disease dynamics and population dynamics. Advances in experimental techniques resulted in availability of multidimensional, time-resolved data about dynamical processes. On the other hand, advances in computer hardware and simulation algorithms made it possible to obtain detailed dynamical picture of real-life biological processes on a computer. Computational experiments became a vital component of researches arsenal, as was highlighted by the 2013 Nobel prize awarded to three computational chemists. It is becoming rather routine to simulate on a computer the “jiggling and wiggling of atoms” that are performed by biomolecules to fulfil their function. While the results of the simulations, in principle, contain all the information about the processes, the extraction of this information, its analysis and presentation in a convenient form are highly non-trivial tasks. Given the growing size and complexity of simulations, analyzing and interpreting such data is widely recognized as a fundamental bottleneck in atomistic simulations. My group focuses in developing original approaches to analyze complex dynamics and represent it in a simple, intuitive way. Mainly, we are interested in the analysis of state-of-the-art simulations of protein folding obtained from various groups (e.g., D. Shaw). However, analysis of other types of complex dynamics, e.g., dynamics of a disease from longitudinal cohort studies, or how proteins aggregate is also of interest.

Protein folding

A powerful approach to analyze dynamics contained in such atomistic simulations is to describe/approximate them by diffusion on a free energy landscape - free energy as a function of reaction coordinates (RC). For the description to be quantitatively accurate, RCs should be chosen in an optimal way. Recent theoretical results show that such an optimal RC exists; however, determining it for practical systems is a very difficult unsolved problem. Recently we described a solution to this problem. We described an adaptive nonparametric approach to accurately determine the optimal RC (the committor) for an equilibrium trajectory of a realistic system. The power of the approach was illustrated on a long equilibrium atomistic folding simulation of HP35 protein. We have determined the optimal folding RC - the committor, which was confirmed by passing a stringent committor validation test. It allowed us to determine a first quantitatively accurate protein folding free energy landscape. We have confirmed the theoretical results that diffusion on such a free energy profile can be used to compute exactly the equilibrium flux, the mean first passage times, and the mean transition path times between any two points on the profile. We have shown that the mean squared displacement along the optimal RC grows linear with time as for simple diffusion. The free energy profile allowed us to obtain a direct rigorous estimate of the preexponential factor for the folding dynamics.

Disease dynamics

A Jupyter Notebook illustrating the analysis of the patient recovery dynamics after kindney transplant and allowing hands on experience with the developed methodology can be found at https://mybinder.org/v2/gh/krivovsv/biomarker/master?filepath=biomarkerP.ipynb

• AKI-Predict: The use of an artificial intelligence-based prediction device in clinical practice for the early prediction of acute kidney injury

Qualifications

• PhD Physics , Novosibirsk, Russia
• MS Physics of Non-Equilbrium Processes, Novosibirsk, Russia

Student education

I currently teach bioinformatics and computational biology to second year biology students; bioinformatics to Biochemistry and Biological Sciences master students, programming to master students in Precision Medicine, statistics to first and second year Biochemistry and Biological Sciences students, carrier and professional development to first and second year biology students, an Advanced Topic Units to final year students. My laboratory hosts students for their final year research projects (Biochemistry and Biological Sciences) for both BSc and MBiol schemes as well as for NatSci program. I have developed and managing Analytical Skills module for masters in Precision Medicine. I am involoved in developing programming and data analysis/machine learning modules for NatSci program.

Undergraduate project topics:

Practical projects

Practical projects in my group always involve computers, but do not require previous programming experience. You will either install Linux on your laptop or will have your own mini supercomputer (with many cores) and access to large computational facilities depending on your project’s requirements; usage of Amazon cloud computing is possible. Projects involve a training period where you will learn about Linux, scripting languages, the basics of molecular simulation (how to set-up a simulation of your favourite protein, for example) and the usage of the analysis tools. Depending on your interests and skills, your project may involve the development and testing of new code to perform novel research, for example developing a new way to analyze clinical data to determine a biomarker that predicts the likelihood that kidney transplant will be rejected. Or else you may use standard computational tools to address specific biological questions, for example determine the folding free energy landscape of lambda repressor protein. Those, who specifically are interested in programming, can use their skills, for example, to develop a parallel version of the existing code or to develop a (biochemical) app for the Android platform.

Literature projects

I can offer a broad choice of literature projects, depending on your interests. Projects will have some computational aspects to them. I am interested for example in literature reviews on computational techniques in fields such as multi-scale modelling of large biological systems, synthetic biology, protein design, structure determination by electron microscopy. What are the drawbacks of current computational methods? How can these be improved?

See also:

• Faculty Graduate School
• FindaPhD Project details:
  • Project 32059
  • Project 41280

Research groups and institutes

• Structural Biology