arXiv subjects

Jayajit Das

Publications and source records attributed to Jayajit Das.

At least 19 recordsLinked to original sources

arXiv2022

Determining clinically relevant features in cytometry data using persistent homology

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as `elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

q-bio.QM↗

arXiv2017

Connecting the dots across time: Reconstruction of single cell signaling trajectories using time-stamped data

Single cell responses are shaped by the geometry of signaling kinetic trajectories carved in a multidimensional space spanned by signaling protein abundances. It is however challenging to assay large number (>3) of signaling species in live-cell imaging which makes it difficult to probe single cell signaling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 - >40) of signaling species but are unable to track single cells. Thus cytometry experiments provide detailed time stamped snapshots of single cell signaling kinetics. Is it possible to use the time stamped cytometry data to reconstruct single cell signaling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signaling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signaling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations and live-cell imaging measurements. The use of invariants and slow variables to reconstruct trajectories provides a radically different way to track object using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

q-bio.QM↗

arXiv2016

Limiting energy dissipation induces glassy kinetics in single cell high precision responses

Single cells often generate precise responses by involving dissipative out-of-thermodynamic equilibrium processes in signaling networks. The available free energy to fuel these processes could become limited depending on the metabolic state of an individual cell. How does limiting dissipation affect the kinetics of high precision responses in single cells? I address this question in the context of a kinetic proofreading scheme used in a simple model of early time T cell signaling. I show using exact analytical calculations and numerical simulations that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-to-cell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and, ergodicity breaking. Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, can create a fundamental difficulty in interpreting single cell kinetics from cell population level results.

q-bio.CB↗

arXiv2015

Maximum Entropy estimation of probability distribution of variables in higher dimensions from lower dimensional data

A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n m. In general, in the absence of additional information, there is no unique solution to Q in those cases. Nevertheless, one may still want to draw some inferences about Q. To this end, we propose a novel maximum entropy (MaxEnt) approach that estimates Q(x) based only on the available data, namely, P(y). The method has the additional advantage that one does not need to explicitly calculate the Lagrange multipliers. In this paper we develop the approach, for both discrete and continuous probability distributions, and demonstrate its validity. We give an intuitive justification as well, and we illustrate with examples.

q-bio.QM↗

arXiv2014

Host-to-host variation of ecological interactions in polymicrobial infections

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used in analyzing host-microorganism relationships, as well as most of the ecological theories proposed to explain co-evolution of host and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of inter-host differences. Here we analyze mechanisms underlying host-to-host variations, using the well-characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a Maximum Entropy (MaxEnt) based inference scheme. We find that the nature of the interactions among bacterial species critically regulates host-to-host variations of these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

q-bio.PE↗

arXiv2013

Cell responses only partially shape cell-to-cell variations in protein abundances in Escherichia coli chemotaxis

Cell-to-cell variations in protein abundance in clonal cell populations are ubiquitous in living systems. Since protein composition determines responses in individual cells, it stands to reason that the variations themselves are subject to selective pressures. But the functional role of these cell-to-cell differences is not well understood. One way to tackle questions regarding relationships between form and function is to perturb the form (e.g., change the protein abundances) and observe the resulting changes in some function. Here we take on the form-function relationship from the inverse perspective, asking instead what specific constraints on cell-to-cell variations in protein abundance are imposed by a given functional phenotype. We develop a maximum entropy (MaxEnt) based approach to posing questions of this type, and illustrate the method by application to the well characterized chemotactic response in Escherichia coli (E. coli). We find that full determination of observed cell-to-cell variations in protein abundances is not inherent in chemotaxis itself, but in fact appears to be jointly imposed by the chemotaxis program in conjunction with other factors, e.g., the protein synthesis machinery and/or additional non-chemotactic cell functions such as cell metabolism. These results illustrate the power of MaxEnt as a tool for the investigation of relationships between biological form and function.

q-bio.CB↗

arXiv2013

Data-driven quantification of robustness and sensitivity of cell signaling networks

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data driven Maximum Entropy (MaxEnt) based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.

q-bio.QM↗

arXiv2013

In silico Modeling of Itk Activation Kinetics in Thymocytes Suggests Competing Positive and Negative IP4 Mediated Feedbacks Increase Robustness

The inositol-phosphate messenger inositol(1,3,4,5)tetrakisphosphate (IP4) is essential for thymocyte positive selection by regulating plasma-membrane association of the protein tyrosine kinase Itk downstream of the T cell receptor (TCR). IP4 can act as a soluble analog of the phosphoinositide 3-kinase (PI3K) membrane lipid product phosphatidylinositol(3,4,5)trisphosphate (PIP3). PIP3 recruits signaling proteins such as Itk to cellular membranes by binding to PH and other domains. In thymocytes, low-dose IP4 binding to the Itk PH domain surprisingly promoted and high-dose IP4 inhibited PIP3 binding of Itk PH domains. However, the mechanisms that underlie the regulation of membrane recruitment of Itk by IP4 and PIP3 remain unclear. The distinct Itk PH domain ability to oligomerize is consistent with a cooperative-allosteric mode of IP4 action. However, other possibilities cannot be ruled out due to difficulties in quantitatively measuring the interactions between Itk, IP4 and PIP3, and in generating non-oligomerizing Itk PH domain mutants. This has hindered a full mechanistic understanding of how IP4 controls Itk function. By combining experimentally measured kinetics of PLCγ1 phosphorylation by Itk with in silico modeling of multiple Itk signaling circuits and a maximum entropy (MaxEnt) based computational approach, we show that those in silico models which are most robust against variations of protein and lipid expression levels and kinetic rates at the single cell level share a cooperative-allosteric mode of Itk regulation by IP4 involving oligomeric Itk PH domains at the plasma membrane. This identifies MaxEnt as an excellent tool for quantifying robustness for complex TCR signaling circuits and provides testable predictions to further elucidate a controversial mechanism of PIP3 signaling.

q-bio.CB↗

arXiv2012

Positive feedback produces broad distributions in maximum activation attained within a narrow time window in stochastic biochemical reactions

How do single cell fate decisions induced by activation of key signaling proteins above threshold concentrations within a time interval are affected by stochastic fluctuations in biochemical reactions? We address this question using minimal models of stochastic chemical reactions commonly found in cell signaling and gene regulatory systems. Employing exact solutions and semi-analytical methods we calculate distributions of the maximum value ($N$) of activated species concentrations ($P_{max}(N)$) and the time ($t$) taken to reach the maximum value ($P_{max}(t)$) within a time window in the minimal models. We find, the presence of positive feedback interactions make $P_{max}(N)$ more spread out with a higher "peakedness" in $P_{max}(t)$. Thus positive feedback interactions may help single cells to respond sensitively to a stimulus when cell decision processes require upregulation of activated forms of key proteins to a threshold number within a time window.

q-bio.MN↗

arXiv2012

Dramatic reduction of dimensionality in large biochemical networks due to strong pair correlations

Large multidimensionality of high-throughput datasets pertaining to cell signaling and gene regulation renders it difficult to extract mechanisms underlying the complex kinetics involving various biochemical compounds (e.g., proteins, lipids). Data-driven models often circumvent this difficulty by using pair correlations of the protein expression levels to produce a small numbers (<10) of principal components, each a linear combination of the concentrations, to successfully model how cells respond to different stimuli. However, it is not understood if this reduction is specific to a particular biological system or to nature of the stimuli used in these experiments. We study temporal changes in pair correlations described by the covariance matrix between different molecular species that evolve following deterministic mass action kinetics in large biologically relevant reaction networks and show that this dramatic reduction of dimensions (from hundreds to <5) arises from the strong correlations between different species at any time and is in sensitive of the form of the nonlinear interactions, network architecture and values of rate constants and concentrations over a wide range. We relate temporal changes in the eigenvalue spectrum of the covariance matrix to low-dimensional, local changes in directions of the trajectory embedded in much larger dimensions using elementary differential geometry. We illustrate how to extract biologically relevant insights such as identifying significant time scales and groups of correlated chemical species from our analysis. Our work provides for the first time a theoretical underpinning for the successful experimental analysis and points to way to extract mechanisms from large- scale high throughput data sets.

q-bio.QM↗

arXiv2009

Positive Feedback Regulation Results in Spatial Clustering and Fast Spreading of Active Signaling Molecules on a Cell Membrane

Positive feedback regulation is ubiquitous in cell signaling networks, often leading to binary outcomes in response to graded stimuli. However, the role of such feedbacks in clustering, and in spatial spreading of activated molecules, has come to be appreciated only recently. We focus on the latter, using a simple model developed in the context of Ras activation with competing negative and positive feedback mechanisms. We find that positive feedback, in the presence of slow diffusion, results in clustering of activated molecules on the plasma membrane, and rapid spatial spreading as the front of the cluster propagates with a constant velocity (dependent on the feedback strength). The advancing fronts of the clusters of the activated species are rough, with scaling consistent with the Kardar-Parisi-Zhang (KPZ) equation in one dimension. Our minimal model is general enough to describe signal transduction in a wide variety of biological networks where activity in the membrane-proximal region is subject to feedback regulation.

cond-mat.soft↗

arXiv2004

Nonequilibrium steady states of the isotropic classical magnet

We drive a d-dimensional Heisenberg magnet using a spatially anisotropic current of mobile particles or heat. The continuum Langevin equation is analyzed using a dynamical renormalization group, stability analysis and numerical simulations. We discover a rich steady-state phase diagram, including a critical point in a new nonequilibrium universality class, and a spatiotemporally chaotic phase. The latter may be `controlled' in a robust manner to target spatially periodic steady states with helical order. We discuss several physical realizations of this model and make definite predictions which could be tested in experimental or model lattice systems.

cond-mat.soft↗

arXiv2003

Dynamics of magnetic flux lines in the presence of correlated disorder

We investigate the dynamics of interacting magnetic flux lines driven by an external current in the presence of linear pinning centers, arranged either in a periodic square lattice or placed randomly in space, by means of three-dimensional Monte Carlo simulations. Compared to the non-interacting case, the repulsive forces between the vortices reduce the critical current J_c, as determined from the depinning threshold in the current-voltage (I-V) characteristics. Near the depinning current J_c, the voltage power spectrum S(w) reveals broad-band noise, characterized by a $1/w^a$ power law decay with a <= 2. At larger currents the flux lines move with an average velocity v_{cm}. For a periodic arrangement of columnar pins with a lattice constant $d$ and just above J_c, distinct peaks appear in the voltage noise spectrum S(w) at w ~ v_{cm}/d which we interpret as the signature of stick-slip flux line motion.

cond-mat.supr-con↗

arXiv2002

Driven Heisenberg Magnets: Nonequilibrium Criticality, Spatiotemporal Chaos and Control

We drive a $d$-dimensional Heisenberg magnet using an anisotropic current. The continuum Langevin equation is analysed using a dynamical renormalization group and numerical simulations. We discover a rich steady-state phase diagram, including a critical point in a new nonequilibrium universality class, and a spatiotemporally chaotic phase. The latter may be `controlled' in a robust manner to target spatially periodic steady states with helical order.

cond-mat.stat-mech↗

arXiv2002

Vortex transport and voltage noise in disordered superconductors

We study, by means of three-dimensional Monte Carlo simulations, the current-voltage (IV) characteristics and the voltage noise spectrum at low temperatures of driven magnetic flux lines interacting with randomly placed point or columnar defects, as well as with periodically arranged linear pinning centers. Near the depinning current J_c, the voltage noise spectrum S(w) universally follows a 1/w^a power law. For currents J > J_c, distinct peaks appear in S(w) which are considerably more pronounced for extended as compared to point defects, and reflect the spatial distribution of the correlated pinning centers.

cond-mat.stat-mech↗

arXiv2000

Ordering Dynamics of Heisenberg Spins with Torque: Crossover, Spinwaves and Defects

We study the effect of a torque induced by the local molecular field on the phase ordering dynamics of the Heisenberg model when the total magnetization is conserved. The torque drives the zero-temperature ordering dynamics to a new fixed point, characterized by exponents $z=2$ and $λ\approx 5$. This `torque-driven' fixed point is approached at times such that $g^2 t\gg 1$, where $g$ is the strength of the torque. All physical quantities, like the domain size $L(t)$ and the equal and unequal time correlation functions obey a crossover scaling form over the entire range of $g$. An attempt to understand this crossover behavior from the approximate Gaussian Closure Scheme fails completely, implying that the dynamics at late times cannot be understood from the dynamics of defects alone. We provide convincing arguments that the spin configurations can be decomposed in terms of defects and spin-waves which interact with each other even at late times. In the absence of the torque term, the spin waves decay faster, but even so we find that the Gaussian closure scheme is inconsistent. In the latter case the inconsistency may be remedied by including corrections to a simple gaussian distribution. For completeness we include a discussion of the ordering dynamics at $T_c$, where the torque is shown to be relevant, with exponents $z=4-ε/2$ and $λ= d$ (where $ε= 6-d$). We show to all orders in perturbation theory that $λ=d$ as a consequence of the conservation law.

cond-mat.stat-mech↗

arXiv1999

Dynamics of Ordering of Isotropic Magnets

We study the dynamics of ordering of the nonconserved and conserved Heisenberg magnet. The dynamics consists of two parts - an irreversible dissipation into a heat bath and a reversible precession induced by a torque due to the local molecular field. For quenches both to T=0 and T=T_c, we show that the torque is irrelevant when the dynamics is nonconserved but relevant when the dynamics is conserved and is governed by a new nontrivial fixed point.

cond-mat.stat-mech↗

arXiv1999

Conserved Ordering Dynamics of Heisenberg Spins with Torque

We show that a torque induced by the local molecular field drives the zero-temperature ordering dynamics of a conserved Heisenberg magnet to a new fixed point, characterised by exponents z=2 and $λ\approx 5.15$. Numerical solutions of the Langevin equation indicate that theories using a Gaussian closure are inconsistent even when the torque is absent. The torque is relevant even for quenches to T_c, with exponents $z=4-ε/2$ and $λ= d$ (where $ε= 6-d$). Indeed $λ$ is always equal to d for quenches to T_c whenever the order parameter is conserved.

cond-mat.stat-mech↗