ICTS

Cohesive group formation has been observed in diverse species, for example, a flock of birds, a school of fishes, a swarm of insects, and an aggregate of cells, to name a few. In nature, swarming behavior has generally been found in search of food, for breeding, to avoid predators, etc. It is quite intriguing how a large number of individuals self-organize to form collective groups and generate complex organized patterns. In this talk, I will discuss how simple computational models, namely self-propelled particle models, help get an insight into the underlying dynamics of a prey swarm under a predator attack. Our study shows that varying range of interaction strongly influences the trajectory of prey when chased by a predator and also affects the survival probability of the prey group. Further, the analysis of the prey group survival as a function of predator-to-prey mass ratio shows the existence of three distinct regimes: (i) frequent chase and capture leading to the non-survival of the prey swarm, (ii) an intermediate regime where competition between pursuit and capture occurs, resembling an arms race, and (iii) the survival regime without the capture of prey. Interestingly, our study demonstrates the existence of a favorable predator-prey mass ratio for efficient predation, which corroborates with the field studies.

We model a binary mixture of passive and active Brownian particles in two dimensions using the effective interaction between passive particles in the active bath. The activity of active particles and the size ratio of two types of particles are the two control parameters in the system. The effective interaction is calculated from the average force on two particles generated by the active particles. The effective interaction can be attractive or repulsive, depending on the system parameters. The passive particles form four distinct structural orders for different system parameters, viz., homogeneous structures, disordered cluster, ordered cluster, and crystalline structure. The change in structure is dictated by the change in nature of the effective interaction. We further confirm the four structures using a full microscopic simulation of active and passive mixture. Our study is useful to understand the different collective behavior in non-equilibrium systems.

Neurons are cells with long processes known as axons with a functional synapses that act as communication hubs often at the end of such processes. Cargo transport from the cell body is essential for development and maintenance of the synapse. Long distance axonal transport is dependent on both the microtubule cytoskeleton and microtubule dependent molecular motors. Synaptic vesicles are a major cargo essential for neurotransmission. The KIF1A motor is critical for transporting synaptic vesicles in axons in all animal models examined. Other proteins regulate the activation and processivity of the KIF1A motor. Additionally, cargo also show regulated movement. My work will touch upon both motor and cargo movement where the former is regulated by protein modification and the latter by crowding.

The living cell contains many sub-cellular organelles - for example endosomes, lysosomes, mitochondria,  endoplasmic reticulum, lipid droplets and so on. Each of these is essentially a factory enclosed inside a membrane. For the cell to function, these factories must communicate with each other and exchange their contents at so-called membrane contact sites (MCS).  

A fundamental physical constraint, however, is less discussed in the literature related to MCS. Most organelles are too large to diffuse around freely -- how,  then, can they find their cognate MCS at distant cellular locations? Notably, the same organelles are also actively transported by the kinesin and dynein motors. We therefore wondered if motors on an organelle could get switched from Transporter to Tether when the organelle reaches a specific MCS. Such a switch would allow organelles to sample the intracellular space with intermittent “pit-stops” at MCS where they can exchange proteins/lipids for onward communication. 

I will discuss some evidence to support this hypothesis. This includes a newly developed assay where we deposit an endoplasmic reticulum (ER)-mimicking proteinaceous membrane on a coverslip and engineer contacts between this ER-mimic and Lipid droplets held in an Optical tweezer. I will also bring out the potential implications of these results to control of systemic lipid homeostasis by the Liver.

Membranes are ubiquitous across Biological systems and especially in the context of cells. Even in cells there exists large heterogeneity in membrane composition and properties which are intricately related to their functionality. Given the complex spatiotemporal organization of biomembranes on short length and fast time scales it has proven extremely difficult to probe these properties using any single experimental technique. In this lecture I will try to shed some light on how use of correlated fluctuationspectroscopy and super-resolution microscopy captures some aspects of the spatio-temporal heterogeneity in model and cell membranes and reveals mechanism of their modulation due to external stress or interaction with other biomolecules which provides a glimpse of membrane mediated cellular response.

The living cell contains many sub-cellular organelles - for example endosomes, lysosomes, mitochondria,  endoplasmic reticulum, lipid droplets and so on. Each of these is essentially a factory enclosed inside a membrane. For the cell to function, these factories must communicate with each other and exchange their contents at so-called membrane contact sites (MCS).  

A fundamental physical constraint, however, is less discussed in the literature related to MCS. Most organelles are too large to diffuse around freely -- how,  then, can they find their cognate MCS at distant cellular locations? Notably, the same organelles are also actively transported by the kinesin and dynein motors. We therefore wondered if motors on an organelle could get switched from Transporter to Tether when the organelle reaches a specific MCS. Such a switch would allow organelles to sample the intracellular space with intermittent “pit-stops” at MCS where they can exchange proteins/lipids for onward communication. 

I will discuss some evidence to support this hypothesis. This includes a newly developed assay where we deposit an endoplasmic reticulum (ER)-mimicking proteinaceous membrane on a coverslip and engineer contacts between this ER-mimic and Lipid droplets held in an Optical tweezer. I will also bring out the potential implications of these results to control of systemic lipid homeostasis by the Liver.

The hyperbolic dependence of catalytic rate on substrate concentration is a classical result in enzyme kinetics, quantified by the celebrated Michaelis-Menten (MM) equation [1]. The ubiquity of this relation in diverse chemical and biological contexts has recently been rationalized by a graph-theoretic analysis of deterministic enzymatic networks [2]. Experiments, however, have revealed that “molecular noise” - intrinsic stochasticity at the molecular scale - leads to significant deviations from classical results and unexpected effects like “molecular memory”, i.e., the breakdown of statistical independence between turnover events [3]. Over several years we have developed a stochastic time-based approach which, in combination with the number-based approach, namely the chemical master equation, has unified the results of classical (deterministic) and single-molecule (stochastic) enzyme kinetics within a single theoretical framework [4-6]. In this lecture, a brief overview of classical and stochastic enzyme kinetics will be presented. A novel statistical analysis that uncovers the emergence of molecular memory and non-hyperbolicity in the (non- classical) transient regime, peculiar to stochastic reaction networks of multiple enzymes, will be described. New statistical measures to distinguish between the non-stationary and stationary states in single-enzyme kinetics will be proposed, and their application to experimental data from the landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites will be presented.

References
[1] L Michaelis and M L Menten, Biochem. Z. 49, 333 (1913); U. Deichmann, S. Schuster, and J-P.
Mazat, FEBS J. 281, 435 (2014); A. Cornish-Bowden, Perspect. Sci. 4, 3 (2015).
[2] F. Wong, A. Dutta, D. Chowdhury, and J. Gunawardena, Proc. Natl. Acad. Sci. 115, 9738 (2018).
[3] B. P. English, W. Min, A. M. Van Oijen, K. T. Lee, G. Luo, H. Sun, B. J. Cherayil, S. Kou, and X. S.
Xie, Nat. Chem. Biol., 2, 87 (2006).
[4] S. Saha, S. Ghose, R. Adhikari, and A. Dua, Phys. Rev. Lett. 107, 218301 (2011).
[5] A. Kumar, R. Adhikari, and A. Dua, Phys. Rev. Lett. 119, 099802 (2017).
[6] A. Kumar, R. Adhikari, and A. Dua, J. Chem. Phys. 154, 035101 (2021)

The self-interests of individuals in a society lead to over-exploitation of common resources therein. This tragic theme is not confined to only socio-economic systems---many biological systems can be envisaged to be plagued by the tragedy of the commons. We shall discuss how the insightful framework of evolutionary game theory is used to make stripped-down dynamical models that help to understand the eco-evolutionary dynamics of such systems.

Specifically, we shall consider a stochastic birth-death process in a population of replicators (any agent, at any level of biological organization, capable of replication) and an ecological resource whose state changes stochastically. Subsequently, we shall extract the deterministic mean-field model for the combined system in the limit of large population size. We shall contrast the situations of finite versus infinite populations, generation-wise overlapping versus non-overlapping populations, short-term versus long-term evolutions, punishment versus reward, and correct versus incorrect available information.

In the series of three lectures, I discuss some aspects of the stochastic dynamics of molecular machines as well as some of the economic constraints cells face in allocating molecular machines to different tasks. The focus will be on the machines that process the genetic code, RNA polymerases and ribosomes. I will introduce the dynamics of information processing, emphasizing the (sometimes conflicting) requirements for accuracy and speed and the relation of accuracy to thermodynamics. A second topic will be how the internal dynamics of molecular machines can generally be decribed by stochastic networks and how these networks can be coarse-grained. Finally, some economic constraints will be discussed, which relate both to the cost of the machines themselves and to their dynamics in the cell.
 

E.coli cell uses run-tumble motion to search for nutrient or other chemo-attractants in its environment. The biochemical noise present in its signaling network strongly affects the cell behavior. We show how this noise can induce an interplay between the sensing and adaptation modules of the signaling network and enhance chemotactic efficiency. The same interplay also gives rise to a highly non-trivial methylation dynamics as the cell navigates through spatially varying attractant environment. We find sensitive dependence of this dynamics on the strength of attractant gradient. Even for a non-swimming tethered cell, the cell behavior shows interesting dependence on the biochemical noise. After application of a step stimulus, the response of the cell is monitored and although most earlier studies focus on the long time adaptation dynamics of the cell, we study its short time extremal response, which is poorly understood. We perform exact calculations to derive the condition for extremal response and find good agreement with simulations. We also make experimentally verifiable prediction that there is an optimum size of the step stimulus at which the extremal response is reached in the shortest possible time.

In the series of three lectures, I discuss some aspects of the stochastic dynamics of molecular machines as well as some of the economic constraints cells face in allocating molecular machines to different tasks. The focus will be on the machines that process the genetic code, RNA polymerases and ribosomes. I will introduce the dynamics of information processing, emphasizing the (sometimes conflicting) requirements for accuracy and speed and the relation of accuracy to thermodynamics. A second topic will be how the internal dynamics of molecular machines can generally be decribed by stochastic networks and how these networks can be coarse-grained. Finally, some economic constraints will be discussed, which relate both to the cost of the machines themselves and to their dynamics in the cell.
 

Actin-based cellular protrusions are a ubiquitous feature of cell morphology, e.g., filopodia and microvilli, serving a huge variety of functions. Despite this, there is still no comprehensive model for the mechanisms that determine the geometry of these protrusions. We present here a detailed bbio-physical model that addresses a combination of multiple biochemical and physical processes involved in the dynamic regulation of the shape of these protrusions. This biological system allows us to introduce the concepts of actin polarized polymerization, molecular motors, and the elasticity of the membrane, as well as processes of diffusion and advection. We specifically explore the role of actin polymerization in determining both the height and width of the protrusions.

We consider deformations of biopolymers and membranes under the active drive of motor proteins. We use numerical calculations complemented by analytical theory to show the emergence of several morphological phases and phase transitions. A spherical cell membrane coupled to curvature-inducing activator proteins and active forces from polymerizing actin and myosin show instabilities towards pattern formation, localized, and running pulsations. On the other hand, we show that semiflexible filaments in a gliding assay will undergo re-entrant morphological transitions from open to spiral conformations. We obtain a significant simplification by mapping the drive from the gliding assay as an effective active bath.

In the series of three lectures, I discuss some aspects of the stochastic dynamics of molecular machines as well as some of the economic constraints cells face in allocating molecular machines to different tasks. The focus will be on the machines that process the genetic code, RNA polymerases and ribosomes. I will introduce the dynamics of information processing, emphasizing the (sometimes conflicting) requirements for accuracy and speed and the relation of accuracy to thermodynamics. A second topic will be how the internal dynamics of molecular machines can generally be decribed by stochastic networks and how these networks can be coarse-grained. Finally, some economic constraints will be discussed, which relate both to the cost of the machines themselves and to their dynamics in the cell.

Eukaryote cells have flexible membranes that allow them to have a variety of dynamical shapes. The shapes of the cells serve important biological functions,both for cells within an intact tissue, and during embryogenesis and cellular motility. How cells control their shapes and the structures that they form on their surface has been a subject of intensive biological research, exposing the building blocks that cells use to deform their membranes. These processes have also drawn the interest of theoretical physicists, aiming to develop models based on physics, chemistry and nonlinear dynamics. Such models explore quantitatively different possible mechanisms that the cells can employ to initiate the spontaneous formation of shapes and patterns on their membranes. We review here theoretical work where one such class of mechanisms was investigated: the coupling between curved membrane proteins, and the cytoskeletal forces that they recruit. Theory indicates that this coupling gives rise to a rich variety of membrane shapes and dynamics, while experiments indicate that this mechanism appears to drive many cellular shape changes.

In order to transmit and preserve genetic information, cells must duplicate DNA with very high fidelity. Large metazoan genome duplication is accomplished by assigning thousands of potential origins of replication, a fraction of which are utilized. The remaining sites remain flexibly dormant unless required as in case of replication stress. Besides, the start of replication from the utilized origin is also spatiotemporally regulated in S-phase. Arguably one of the key events during replication initiation from a selected origin is a consequence of regulated conformational changes in the replicative helicase MCM complex. MCM complex is loaded onto the DNA in the G1 phase of the cell cycle in a catalytically inactive form which at selected origins transitions into active translocating helicase in S- phase. However, (a) how this transition is achieved and (b) transition is regulated in a spatiotemporal fashion is not known. Here, we propose to address the dynamics of helicase activation by generating individual cellular model systems with mitigated regulation of replication by strategic mutations or oncogene-induction. We will perform biochemistry, and comparative proteomics combined with genomics while evaluating the spatiotemporal progression of replication in mammalian cells in order to decipher molecular regulators for genome duplication time and pattern.

Utilizing three-dimensional Monte-Carlo simulations, of the dynamics of a triangulated surface, we demonstrate that the coupling of curved membrane proteins that recruit the active forces of the cytoskeleton can drive the spontaneous formation of complex memrane dynamics, as observed in cells: the spontaneous formation of flat lamellipodia in spreading and motile cells, on flat and curved surfaces, and the self-organization of the actin cytoskeleton during phagocytosis.