Mayank Shreshtha - Scientific Illustrations

A molecular motor walking along a filament fluctuates due to stochastic environment. Snapshots of successive runs depict uncertainty (or lack of precision) in position. The lower portion depicts how mechanical motion of motor is achieved via release of chemical energy. The TUR puts a minimal thermodynamic cost on the uncertainty. This image conveys the meaning of uncertainty (spread) in a probability distribution (figure below) and how it emerges from multiple simulations of a random process.

Cell division: during interphase, the cell increases in size. The DNA of the chromosomes is replicated, and the centrosome is duplicated.

Antigen presentation machinary: majority histocompatibility complex (MHC)-I peptide presentation. Made for Rahul Kumar (final-year PhD, Simon Centre for Living Systems, NCBS Bangalore) PhD thesis

Different cell types within tumour microenvironment (TME). Made for Rahul Kumar (final year PhD, Simon Centre for Living Systems, NCBS Bangalore) PhD thesis (in preparation)

Precision always comes at a price. Thermodynamic uncertainty relations (TURs) are the new universal relations in nonequilibrium statistical physics. It reveals that narrower distributions of fluctuating quantities (precision), can only be achieved if the dissipation rate increases. It is applicable to biochemical networks to nanomachines like molecular motors and heat engines The idea, here, was to convey the visual meaning of uncertainty (spread) in a probability distribution and how it emerges from multiple simulations of a random process.

Recreation of bacterial locomotion schematic from Wadhwa, N., Berg, H.C. Bacterial motility: machinery and mechanisms. Nat Rev Microbiol 20, 161–173 (2022).

Immunoproteasome Formation: made for Rahul Kumar (final year PhD, Simon Centre for Living Systems, NCBS Bangalore) PhD thesis.

Immunoproteasome Formation (white background).

An alternative version of the previously drawn self-interacting diffusion process. In this version, a gradient represent the increase in time. At longer times (light pink), the trail of the particle tends to get attracted towards the greyish bulk where the particle has spent most time in past. This intuitive version makes it easier to understand the process. It can also be used to represent cases when multiple particles are present in the system.

The multi-particle version of the self-interacting diffusion process. At longer times, all particles (identical but colour coded here to distinguish the trajectories) tend to get affected (here, attracted) by the combined spatial memory (time spent by all particles in a particular region). The gradient representation makes it easier to visualise the long-time tails (light pink) of the trajectories. One can clearly see all light-pink tails are attracted by the combined memory of particles, i.e., how much time they have spent in past in a given region.

Self-interacting diffusion process (I): Most ant species, chemicals along the trails to the food source to guide other ants. It is an example of self-interaction where organisms are either attracted or repelled by their own secretions. The schematic encapsulates a specific version of self-interacting diffusion process (made for Dr. Francesco Coghi, NORDITA, Stockholm). It can be used to model processes where a particle trajectory is affected by how much time it spends in a particular region in space. It means the trail is affected by the bulk of its spatial memory. [...]

Self-interacting diffusion process (II): [...] Initially, the self-interaction arrows point to all previously visited positions in space. However, as time increases (t = 7 onwards), the arrows are less spread and more directed towards the bulk where particle has spent most of the time. These memory-driven processes are used to design self-phoretic (migration induced by gradient in chemical concentration) particles which are useful in targeted drug delivery.

Tumour evolution in cancer: branched evolution; Different cell types within tumour microenvironment (TME). Made for thesis of Rahul Kumar (final year PhD, NCBS Bangalore).

Light- induced topological phase transitions, reproduction of figure 3 given in Bao, C., Tang, P., Sun, D. et al. Light-induced emergent phenomena in 2D materials and topological materials. Nature Review Physics 4, 33–48 (2022).

ASEP is widely used to model the walking of a molecular motor walking along a filament (microtubules). The molecular motor can be modeled using a biased random walk and this simple model provides a lot of crucial insights into the phenomena of biological transport happening at nanoscale.

A schematic of topological insulators (related to topic of the Nobel Prize in Physics 2016). The electrons are trapped in the middle (bulk) in a strong magnetic field but they form skipping orbits on the edges and constitute current. The magnetic field lines (violet) were intentionally made blurry to narrow down the focus on the plane. The bulk electrons (closed orbits) are depicted with slight dull colours to contrast them with the electron hopping on the edges. The stroboscopic effect on the edges is used to emphasise the semi-classical representation of electron (cloud plus particle nature). This choice also avoided the curved arrows on the left and right skipping orbits.

Schematic of a typical bacterial locomotion.

Run-and-tumble abstract model for PhD thesis: Shreshtha, M. (2021). Fluctuations and uncertainty in stochastic models with persistent dynamics (Doctoral dissertation, Queen Mary University of London).

Over-simplistic representation of gene-editing process.

Deep Ultraviolet (UV) and Extreme UV wavelength regions in the electromagnetic spectrum. DUV and EUV are used in lithography machines which imprint patterns on silicon wafers to produce microchips.