ICTS

I will discuss in vivo dynein-based transport, and how it is regulated in vivo. In addition to discussing  how force production is regulated, I will touch on cellular places where such transport might matter.

The motor protein cytoplasmic dynein is regulated for its activity in living cells by remaining in an inactive conformation until the formation of a tripartite complex comprising dynein, its regulator, dynactin and a cargo adaptor. Thereupon, dynein processively transports cargo towards the minus ends of microtubules. How this process of dynein activation occurs in living cells is unclear, since this entails the congregation of three kinds of protein complexes inside the crowded environs of cell. Here, we employed high time resolution, single-molecule imaging and tracking techniques to visualize and follow fluorescently-tagged dynein heavy chain in HeLa cells. We observed dynein that bound afresh from the cytoplasm to the microtubule engaged in processive minus end-directed movement only about 30% of the time. Contrary to the existing model of cargo capture by dynein clusters at microtubule plus ends, we discovered that dynein encountered cargo that were anchored all along the length of the microtubule. Taken together, we propose a search strategy that is facilitated by dynein’s frequent microtubule binding-unbinding kinetics: (1) in the event of a futile event when dynein does not encounter cargo anchored to the microtubule, dynein unbinds and diffuses back into the cytoplasm, (2) when dynein encounters cargo upon microtubule binding, it is activated and transports the cargo in a short run towards the minus end. In conclusion, dynein activation and cargo capture are coupled in a step that employs reduction in dimensionality to enable minus-end directed transport in vivo.

 

Much attention has been focused on stochasticity in gene expression, which should arise from the small copy number or single-molecular behaviors of biomolecules in cells. For bacterial cells, the statistical physical modeling on stochasticity has been successfully applied to engineering gene regulation. However, much remains elusive in higher organisms. In physical models of gene expression, difference in time scales of various processes in transcription determines the stochasticity in gene regulatory dynamics. This physical picture should be particularly important for analyzing mouse embryonic stem cells (mESCs); in a conventional cell-culture condition, the observed large fluctuation of Nanog expression in mESCs can be due to the slowness of chromatin dynamics for forming super-enhancers at the Nanog locus. We show the combined description of probability landscape and circular probability flux should help to analyze the effect of this time-scale difference. We further discuss the relations between chromatin dynamics and gene transcription by comparing the live-cell microscopy data and polymer models of chromatin movement, which revealed the unexpected relation that the transcription activity suppresses chromatin movement.

References
[1] K. Zhang, M. Sasai, J. Wang, Eddy current and coupled landscapes for nonadiabatic and nonequilibrium complex system dynamics, Proc. Natl. Acad. Sci. USA, 110,14930 (2013).
[2] M. Sasai, et al., Time scales in epigenetic dynamics and phenotypic heterogeneity of embryonic stem cells, PLoS Comput. Biol. 9, e100338 (2013). 
[3] R. Nagashima, K. Hibino, S. S. Ashwin, et al., Single nucleosome imaging reveals loose genome chromatin networks via active RNA polymerase II. J. Cell Biol. 218, 1511 (2019).
[4] S. S. Ashwin, T. Nozaki, K. Maeshima, M. Sasai, Organization of fast and slow chromatin revealed by single-nucleosome dynamics. Proc. Natl. Acad. Sci. USA 116, 19939 (2019).　
[5] S. S. Ashwin, K. Maeshima, M. Sasai, Heterogeneous fluid-like movements of chromatin and their implications to transcription. Biophys. Rev. 12, 461 (2020) .
[6] B. Bhattacharyya, J. Wang, M. Sasai, Stochastic epigenetic dynamics of gene switching. Phys. Rev. E 102, 042408 (2020).

DNA metabolic processes, such as transcription, repression, replication, and DNA damage repair elicit movement of proteins from one subnuclear location to another. Transport of these DNA binding proteins (DBPs) is amazingly fast and their specific binding to short DNA target sequences in an enormous genomic background containing many alternative sites of similar sequences to that of the specific target site is extremely accurate. In my presentation, I shall discuss the physical basis of such fast and accurate target search mechanism of DNA binding proteins on both single- and double-stranded DNA track.

A macroscopic theory for cellular states with steady-growth is presented, based on consistency between cellular growth and molecular replication, as well as robustness of phenotypes against perturbations. Adaptive changes in high-dimensional phenotypes are shown to be restricted within a low-dimensional slow manifold, from which a macroscopic law for cellular states is derived, as is confirmed by adaptation experiments of bacteria under stress. Next, the theory is extended to phenotypic evolution, leading to proportionality between phenotypic responses against genetic evolution and by environmental adaptation. Evolutionary relevance of slow modes in controlling high-dimensional phenotypes is discussed. Last, if I have time, I will discuss transition from exponential-growth to stationary phases by providing a general law between the starvation time and lag-time, and also touch upon the origin of central dogma in molecular biology as symmetry breaking between function and information.

References:
1. Kaneko K., Life: An Introduction to Complex Systems Biology, Springer (2006)
2. K. Kaneko, C.Furusawa, T. Yomo, "Macroscopic phenomenology for cells in steady-growth state", Phys.Rev.X(2015) 011014
3. C. Furusawa, K. Kaneko "Global Relationships in Fluctuation and Response in Adaptive Evolution", J of Royal Society Interface 12(2015), 20150482.
4. C. Furusawa, K. Kaneko " Formation of Dominant Mode by Evolution in Biological Systems” Phys. Rev. E 97(2018)042410
5. K. Kaneko, C. Furusawa “Macroscopic Theory for Evolving Biological Systems Akin to Thermodynamics”, Annual Rev. Biophys. (2018) 47, 273-290
6. Y. Himeoka, K. Kaneko (2017) “Theory for transitions between exponential and stationary phases: universal laws for lag time” Physical Review X,(2017) 7, 021049
7. N. Takeuchi & K. Kaneko (2019) “ The origin of the central dogma through conflicting multilevel selection” Proceedings of the Royal Society B, 286(1912), 20191359.

Cancer metastasis and therapy resistance remain two unsolved clinical challenges for cancer, and cause over 90% of all cancer-related deaths. Despite extensive efforts to identify genomic determinants of metastasis, no unique mutational signature has yet been found. Instead, the ability of cancer cells to adapt and reversibly switch to another state - phenotypic switching - has been identified as a hallmark of metastasis. Phenotypic switching has also been implicated in cancer cells evading attacks by multiple therapies. However, the topological traits and emergent dynamics of regulatory networks enabling phenotypic switching remain elusive. We have investigated the dynamics of multiple regulatory networks enabling phenotypic switching. Discrete and continuous dynamical simulations of these networks reveal their multistable behavior that can explain co-existence of experimentally observed phenotypes and ability to spontaneously switch among them. Analysis of the underlying network topology uncovers that multistability emerges from positive feedback loops embedded in a network, which can often lead to two teams of players that mutually inhibit each other but members of a team activate one another, forming a ‘toggle switch’ between the two teams. Deciphering these topological signatures in these regulatory networks can unravel their ‘latent’ design principles and offer a rational approach to characterize phenotypic switching in a tumor and offer rational and unorthodox ways to inhibit metastasis and the evolution of drug resistance - by reducing the number of positive feedback loops in a network.

Protein-protein interactions whether it be between domains in a single protein or between multiple proteins, are often transient, dynamic and weak. However, these interactions exhibit exquisite level of selectivity in some cases or promiscuity in other cases, in coupling to one or multiple partner proteins upon a cell signaling event. Using G-protein coupled receptor (GPCRs) interactions with G proteins as an example, I will demonstrate the critical role of multi-scale molecular dynamics simulation methods in determining the spatial and temporal components that explain the selective coupling of GPCRs towards G proteins. I will also demonstrate the reshaping of protein-protein coupling interface that explains the promiscuous coupling of some GPCRs to multiple G proteins. Allosteric communication in signaling proteins play an important role in transducing signal across cell membranes. Delineating the amino acid residues involved in this communication would allow us to design ligands that are specific or “biased” to a given signaling pathway. Such “biased ligands” are therapeutically desirable and would enable design of drugs with minimal side effects.

The behavior of a cell is controlled by the biochemical reaction network inside it. This reaction pathway is often noisy since various protein levels inside the cell are prone to fluctuations. With the advent of sophisticated techniques to measure single cell response in experiments, an important question has emerged: how pathway noise affects the cell response. In this talk, I will address this question within the framework of E.coli chemotaxis. The chemotaxis describes the migration tendency of  E.coli bacteria towards higher nutrient concentration. The underlying biochemical network has two main modules, sensing and adaptation, which are coupled to each other through the activity of the transmembrane chemoreceptors. The (de)methylation reaction of these receptors is generally believed to be the most important source of noise, since this is the slowest step in the whole pathway. However, recent experiments have shown that even in absence of methylation, large fluctuations are present inside the cell which originate from cooperativity or clustering tendency of the receptors. Using extensive numerical simulations in a detailed theoretical model, we investigate how this newly found noise source is related to the chemotactic performance. We find that there is an optimum size of the receptor cluster at which the chemotactic performance is at its best. We further show that this performance peak results from a competition between sensing and adaptation. 

A defining feature of eukaryotic cells is the appearance of well defined functional compartmentalisation in the form of membrane-bound organelles. Cellular compartments, such as the Endosomal or Golgi systems, are subject to stochastic trafficking that involves active fission and fusion of cargo vesicles. These are stable structures driven far from equilibrium. I will discuss our current ideas regarding the non equilibrium control of size, shape, spatial position and number of compartments, with special emphasis to the Golgi system.

Hydrolysis (chemical switching of subunits) is a usual process in cytoskeletal filaments like microtubules. In the talk, I will discuss how hydrolysis can make the dynamics of a biofilament non-equilibrium in nature, leading to some collective effects for multiple filaments. Previously, we predicted that when multiple filaments grow against a rigid barrier, forces generated by individual filaments become non-additive in the presence of hydrolysis. Recently, we explored how hydrolysis affects the sizes of microtubules that grow by stochastic assembly and disassembly of subunits in a limiting subunit pool. The length distribution of a single filament is unimodal. In contrast, for multiple filaments, individual filaments toggle stochastically between bigger and smaller lengths, leading to bimodal distributions of individual lengths. Consequently, a limiting subunit pool cannot control filament lengths when the hydrolysis is absent. Thus, we shall discuss how tweaking of an intrinsic parameter (hydrolysis rate) leads to size diversity in an ensemble of microtubules.

Protein synthesis by translation is a fundamental process in all cellular organisms. The initial steps of translation are also tightly regulated to synthesize the right proteins in appropriate amounts at the correct time, thereby playing a broader role in regulating all cellular processes. During translational initiation, the start codon in mRNA is recognized and decoded at the P site of the small ribosomal subunit by a specialized methionyl-tRNA with the help of initiation factors. In eukaryotes, translation initiation is complex and involves many eukaryotic initiation factors (eIFs), some of which are large multi-subunit complexes themselves. In this seminar, I’ll talk about our efforts in understanding the initial steps of eukaryotic protein synthesis and its regulation. 

Natural environments are seldom static and therefore it is important to ask how a population adapts in a changing environment. I will consider a stochastically evolving population in which the mutant is beneficial during a part of the seasonal cycle and deleterious in another. The chance that the mutant spreads in the population depends on the instant it arose in the population and described by a time-inhomogeneous backward Fokker-Planck equation. I will discuss our analytical results for the first-passage probability and first-passage time, and their relevance to the evolution of genetic dominance.

Recent developments in microscopy techniques have allowed probing of cellular dynamics at an unprecedented resolution and throughput. For example, these advances are now allowing us to study the phenomenon of cellular proliferation at the single cell level, rather than the population dynamics of millions of cells. However, interpreting the inevitably noisy datasets associated with such single cell measurements is a fundamental challenge and provides an exciting opportunity for developing physical models in combination with statistical inference.   

Here I will present work where we combined time-lapse microscopy and Bayesian inference to uncover surprising correlations in the division and death times of colon cancer cells closely related by lineage, both before and during chemotherapy treatment. These correlations could not be explained using simple protein production-degradation models that are currently believed to underlie cell fate control. We then developed a stochastic model explaining how the observed correlations can arise from oscillatory mechanisms underlying cell cycle control. Our model was able to recapitulate the data only with specific oscillation periods that fit measured circadian rhythms, suggesting that cell to cell heterogeneity in cell cycle progression rates may arise from circadian control over the cell cycle. Finally, I will discuss some new experiments and theory we are developing to further investigate the role of the circadian clock in cellular proliferation, both in cancer as well as in stem cells.

The systematic variation of diameters in branched networks has tantalized biologists since the discovery of da Vinci’s rule for trees. Da Vinci’s rule can be formulated as a power law with exponent two: the square of a mother branch’s diameter is equal to the sum of the squares of those of the daughters. Power laws, with different exponents, have been proposed for branching in many biological systems ranging from the respiratory and cardiovascular systems in animals to the vascular system of plants. The laws have been derived theoretically, based on optimality arguments, but, for the most part, have not been tested rigorously. In the case of neuronal dendrites, diameter changes across branch points have functional implications for the spread of electrical signals: for example, Rall’s law with an exponent of 3/2 maximizes propagation speeds of action potentials across branch points. Using a super-resolution method to measure the diameters of all dendrites in highly branched Drosophila Class IV sensory neurons, we have tested Rall’s law and shown it to be false. In its place, we have discovered a new diameter-scaling law: the cross-sectional area is proportional to the number of dendrite tips supported by the branch plus a constant, corresponding to a minimum dendrite diameter. The law accords with microtubules providing force and transport for dendrite tip growth. Our new scaling law generalizes to other branched processes such as the vasculature of plants and the circulatory system of animals.

The biochemical regulation of mechanical stresses and the resulting hydrodynamic flows are key ingredients for pattern formation in active fluids. In the absence of chemical reactions, stationary patterns in the concentration of a single regulator of active stress can emerge from a subtle balance between advective and diffusive fluxes. We show that the addition of a linear turnover reaction can offset this balance and lead to the emergence of nonlinear oscillatory patterns in thin-film active fluids.  Our analytical and numerical calculations show that these oscillatory patterns are genuinely nonlinear effects, are independent of boundary conditions, and can be controlled by external signals to transition between different states.

Search processes precedes assembly of many functional structures in cell. For instance, during the organization of cellular organelles, division of cells, or finding specific targets on the cell surface, a crucial search is guided by dynamic microtubule. In this talk, I will discuss models that capture the dynamics of a broad class of systems, giving examples of complex search by microtubules while assembling mitotic spindles and interacting with pathogens on the surface of a T cell.

The dynein-dynactin nanomotor transports cargoes (e.g. mitochondria, virus) along microtubules in cells. Why dynactin interacts separately with dynein and also with microtubules is hotly debated. Here we disrupted these interactions in a targeted manner on cargoes extracted from cells, followed by optical trapping to measure the force of dynein. Perturbing the dynactin-dynein interaction reduced dynein’s ON-rate to microtubules. In contrast, perturbing the dynactin-microtubule interaction increased dynein’s OFF-rate, i.e. it caused premature detachment when dynein had to generate force against the optical trap. The latter observation is important because many known mutations in dynein-dynactin that are implicated in neurodegeneration cause premature detachment of dynein. The dynactin-microtubule link is therefore required for “high-load” functions of dynein, for example to transport cargo in constricted axonal spaces. A less studied property of dynein, namely its detachment rate against load, therefore appears key to dynein dysfunctions.

Planar Cell Polarity (PCP), characterized by asymmetric localization of proteins at the cell membrane within the epithelial plane, plays essential roles in embryonic development and physiological functions. The significance of PCP can be appreciated by the outcomes of PCP failure in the form of defects in neural tube formation, tracheal malfunctions, organ shape misregulation, hair follicle misalignment etc. Extensive experimental works on PCP in fruit fly Drosophila melanogaster have classified the proteins involved in PCP into two modules: `core’ module, acting locally by inter-cellular protein interactions, and, `global’ module, responsible for the alignment of cell polarities with that of the tissue axis. Despite the involvement of different molecular players the asymmetric localization of the proteins of the two modules on cell membrane primarily involve inter-cellular dimer formations.

We have developed a continnum model of the localization of PCP proteins on the cell membrane and its regulation via intra- and inter-cellular protein-protein interactions. We have identified the conditions for the asymmetric protein localization, or PCP establishment, for uniform and graded protein expression levels in the tissue. We have found that in the absence of any tissue level expression gradient the polarized state of the tissue is not stable against finite length perturbations which is also a property of the active polar matter. However, in the presence of tissue level expression gradients of proteins the polarized state remains stable. We have also looked at the influence of the loss of PCP proteins from a select regions of the tissue on the polarization of the cells outside of that region. This continuum theory of the planar cell polarity can be coupled with the active matter hydrodynamics to study the cell flows and their regulation by genetic machinery.

How bacteria are able to maintain their sizes and shapes so precisely remains an open fundamental question. It is believed that cells have narrow distributions of sizes and shapes as a consequence of a homeostasis, a set of physiological conditions that allow bacteria to function at the most optimal conditions. Several phenomenological approaches to explain these observations have been presented, but the microscopic origins of the cell-size regulation are still not understood. Current explanations rely heavily on the assumption of thresholds, but experimental observations did not find any mechanisms that would support thresholds. Here, we propose a new idea to explain these striking observations. It is based on the stochastic approach to investigate the molecular mechanisms of maintaining the cell sizes in bacteria. We argue that the cell-size regulation is a result of coupling of two stochastic processes, cell growth and division, which eliminates the need for introducing the thresholds. Dynamic properties of the system are explicitly evaluated, and it is shown that the model is consistent with the experimentally supported adder principle of the cell-size regulation. In addition, theoretical predictions agree with experimental observations on E. coli bacteria. Theoretical analysis clarifies some important features of bacterial cell growth.

Protein thresholds often act as ancient timekeeping devices regulating cellular processes. An interesting example is the lysis of the bacterium E. coli infected by bacteriophage lambda. The stochastic timing of a lysis event in the latter example is regulated by the accumulation and attainment of a threshold by a protein called holin. For the general problem of bursty production and degradation of protein, the mean and variance of the first passage times of the protein number crossing a threshold was known earlier [1]. We would discuss our recent work on the full first passage time distribution of this process for long-lived proteins like holin [2]. The result can incorporate the cases of positive or negative self-regulation of the protein.

[ Refs: [1] K. R. Ghusinga, J. J. Dennehy, and A. Singh, PNAS 114, 693  (2017)

[2]  K. Rijal,  A. Prasad, and D. Das, Phys Rev E 102, 052413 (2020) ]

DNA is a long polymer that contains the genetic code. Even though different cell types in our body (skin, muscle etc) have exactly the same DNA sequence, these cells function very differently. This functional diversity is thought to be achieved through realizations of different "chromatin states". Chromatin is a three- dimensional assembly of DNA bound by many proteins. Chromatin can be assembled/organized in multiple ways, forming multiple structures, and they eventually result in different function.

In this talk, I will discuss physical models we developed that are useful to understand static and dynamic aspects of chromatin assembly, and copying epigenetic information embedded in folded chromatin. First, we will discuss results from our simulations on 3-dimensional organization of chromatin. We solved an “inverse problem” to reconstruct the 3D organization of chromatin from experimental data that give us contact probabilities between far away segments of chromatin. This helps us to predict various chromatin properties that are consistent with experimentally known constraints. I will also discuss our work trying to understand inheritance of epigenetic information (information beyond the genetic code, embedded in chromatin) during cell division.

The cell is a complex autonomous machine taking in information, performing computations, and responding to the environment. Many of the internal structures and architecture are transient and created through active processes. Recent advances in active matter physics with biological elements are opening new insights into the physics behind how cellular organizations are generated, maintained, and destroyed. I will present two recent stories on two different topics at the interface between biological and soft matter physics. The first will discuss self-organization of microtubules in the presence of “weakly interacting” crosslinkers. The second will discuss possible mechanisms for the cell to mix itself using self-propelled single molecule enzymes. These works illustrate the importance of the fundamental physics to build structures and propel matter inside living cells while informing on new physics we can learn from biological elements and materials.

Throughout all kingdoms of life, DNA is found to form compact structures. DNA is neatly wound inside a viral capsid, DNA forms hierarchical structures in cell nuclei, DNA could even be woven into complex 3D structures known as DNA origami. Such a ubiquitous compaction is surprising, as it contradicts, at the first look, the very basic physical properties of DNA: the high electrostatic charge and resistance to bending at the scale of 50 nm or less. This lecture will describe new insights into the physics of dense DNA systems originating from all-atom, coarse-grained and multi-resolution simulations. The topics covered will include microscopic mechanics of DNA minicircles, viral genome packaging and transport properties of self-assembled DNA nanosystems.