Schedule

Temperate bacteriophages are amongst the simplest organisms that can be said to make a developmental decision. Upon infecting a bacterium, they either produce many offspring and kill the bacterium (lysis), or lie dormant and replicate along with the bacterium (lysogeny). This lysis-lysogeny decision depends on the state of the bacterium, environmental conditions, and, interestingly, the number of phages that have simultaneously infected the bacterium. Phage lambda can even distinguish between one or two viral genomes being present in an infected cell. I will discuss work in which we examined computer models of millions of small genetic networks to see what features are required to produce such an ability to count genomes and bias the developmental decision accordingly. We found that the networks that did this in the most robust way tended to separate the functions of decision-making and decision-maintenance, which may provide an interesting way of looking at other developmental decisions. 

Cascading one signaling enzyme following another, e.g., the MAP3K-MAP2K-MAPK cascade present in all eukaryotes, is a ubiquitous design motif in intra-cellular signaling networks. It is known that increasing the number of layers in a cascade makes it ultra-sensitive, i.e., enhances the sensitivity of the cascade. However, cascades comprising only around 3 layers are usually observed in nature, raising the question as to what may be responsible for limiting the height of a cascade. We show using the MAPK cascade model of Huang and Ferrell that in the presence of noise, the number of levels is an outcome of a trade-off between increasing (i) the resolution of the estimated stimulus and (ii) the response contrast. For a 2-state model under free-ligand approximation, we can explicitly show that this is achieved by shifting the stimulus-response curve with increasing cascade height. This in turn decreases the transition zone width and increases the dynamic range of response. Our work shows how evolution has designed an optimal design for performing reliable computation with noisy information.

Drug resistance and consequent tumor relapse are major clinical challenges in cancer treatment. While pre-existing subpopulations of genetically distinct drug-resistant cells have been shown to contribute to this phenomenon, recent studies reveal the role of drug-induced cell-state switching in aggravating disease outcomes by unlocking phenotypic plasticity and heterogeneity. To overcome this challenge, it is essential to understand the emergent dynamics of underlying regulatory networks enabling such switching. I will present several examples from our work integrating computational modeling of regulatory networks, single-cell high-throughput data and experimental validation to better understand what trajectories cells take to evade targeted therapy as well as immunotherapy in different cancers. We present an in silico platform to rationally identify combinatorial therapies to minimize cancer aggressiveness.

In natural biological systems, cells organise into tissues through a combination of processes, including cellular signalling, collective migration, contractile activity of cytoskeletal elements, and interactions with their surroundings. In recent decades, advancements in microscopy, genetic engineering, biochemistry, and computational modelling have enabled a more quantitative understanding of these processes. In this work, we present an integrated computational software that accounts for various physical mechanisms that influence cellular assembly and collective behaviour and predict the final structure starting from an initial configuration. Specifically, we use this framework to study how cell-cell adhesion and cellular motility collectively interact to determine the phase behaviour of cellular collective starting from a random as well as a specified bio-printed structure. Moreover, we observe how phase behaviour depends on the mechanical properties of the medium. Altogether, this work leads to a computational framework that allows us to design the phase behaviour of a collective of cells, tuning their interaction as well as motility.

Cells undergo fate transitions orchestrated by complex transcription factor networks, which are crucial for development, differentiation, and diseases like carcinoma metastasis. Understanding these transitions is vital for elucidating developmental processes and tumor heterogeneity driven by phenotypic plasticity. Using gene regulatory networks (GRNs) involved in epithelial-mesenchymal transition—a program enabling reversible transitions among epithelial, mesenchymal, and hybrid states—we investigated the interplay between network topology and cellular phenotypes. Our findings reveal that GRN topologies, including feedback loops, interaction consistency, and cohesion, provide insights into emergent dynamics and phenotypes. Mathematical models of two-component networks show that cooperativity and logic underlie robust phenotypic switches, enabling hysteretic or smooth state transitions. These transitions mimic processes like differentiation, trans-differentiation, and reprogramming, reflecting a dynamic epigenetic landscape. We also examined the role of intrinsic and extrinsic signals in modulating transitions, uncovering how network logic and signal asymmetry influence cellular reprogramming and differentiation. Different logics can drive cells toward distinct fates. Overall, our results highlight how GRN features and logic contribute to understanding fate transitions in development and carcinomas. These insights could inform targeted cancer therapies to disrupt metastatic adaptability and strategies for engineering cell fates in synthetic biology and regenerative medicine.

Gene regulatory networks with more than a few genes can support different phenotypes in different conditions. These conditions may be external inputs like intercellular signaling, resource abundance or internal variability like abundance of ribosomes, RNAP or even copy number of different enzymes. The different conditions can be modeled as changes in parameters of a gene network model.  The mathematical challenge is to develop methods to describe, search and analyze behavior of models across large sets of parameters. I will illustrate the use of the techniques that we developed, which are based on combining discrete Boolean approaches with differential equations models, on three problems:  The first problem studies a problem where naive CD4$^+$ cells differentiate into Th1, Th2, Th17 and Treg subsets which mutually inhibit each other. In our model we compare prevalence, across all parameters, of fully differentiated cell type to a cell type which combines characteristic of two of the four types. We find that such two type hybrid occurs more frequently.   This suggests that differentiation to four types likely happens in a two-step process rather than in a single step.  The model is general, and conclusions may apply to other differentiation processes.  The second problem concerns the ability of the same cell cycle network in yeast to support two phenotypes: (a) regular cell cycle, and (b) endo cycling, where the cell duplicates the genome but does not go through mitosis.  Endocycling can be induced experimentally by knocking down mitotic cyclin, and we use the data to show that, indeed, a single network in different parameter regimes can support these different phenotypes.  The third problem concerns the gap gene network in Drosophila. Maternal gradients provide cell-specific input into the gap gene network along the head-to-tail axis. We investigate if this varying input, interpreted as varying specification of parameters of the gap gene network, is sufficient to explain different states that the network reaches in different segments, which lays down the segmentation plan for the animal.  If interest and time remains, I can explain a bit of the mathematics that allows us to do this type of analysis.

Chemotaxis, the movement of cells guided by chemical gradients, plays an essential role in many biological processes, including tumour dissemination, wound healing, and embryogenesis. Bacteria travel towards or away from a chemical during chemotaxis in search of food and to ensure their survival while the chemical concentration in the immediate surroundings continually changes (temporal). We are focused on understanding the chemotactic movement of eukaryotes and prokaryotes in a controlled chemical environment using microfluidics. We used Dictyostelium discoideum (eukaryotic cells) and Bacteria like Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa as model organisms. Cells of the social amoeba Dictyostelium discoideum migrate to a source of periodic travelling waves of chemoattractant as part of a self-organised aggregation process. Using a novel microfluidic device, we try to understand the aggregation process of Dicty.  Also, we are working on rapidly detecting the antibiotic susceptibility of bacteria using a Lab-on-a-chip design. Antibiotic resistance is a growing concern across the world. Because of the increasing usage of antibiotics, these bacteria are becoming resistant to them. Rapid Antibiotic Susceptibility Tests can tell us whether the bacteria are resistant to the particular antibiotic, and we can wisely administer the drug. We try to understand the interplay mechanism between the chemotaxis and antibiotic resistance pathway using novel microfluidics and develop a rapid lab-on-a-chip device capable of detecting antibiotic susceptibility of bacteria based on the understanding. 

Spatiotemporal pattern formation plays a key role in various biological phenomena including Epithelial Mesenchymal Transition (during cellular differentiation as well as cancer initiation). Though the reaction-diffusion systems enabling pattern formation have been studied phenomenologically, the bio-mechanical underpinnings of these processes has not been modeled in detail. I shall talk about the emergence of multistable spatiotemporal patterns in response to intracellular transcriptional/cooperative gene regulation, protein dimerization, and host-circuit interaction. Patterns form due to the coupling of inherent multistable behaviour of transcriptional toggle switches (bistability) and toggle triads (tristability), coupled with their molecular diffusion, with varying diffusion coefficients, across a two-dimensional tissue. I shall talk about another setup of a diffusible cellular environment where we investigate emergent spatiotemporal bistability by a motif with non-cooperative positive feedback that imposes a metabolic burden on its host. Spatiotemporal diffusion coupled with competitive protein dimerization and autoregulatory feedback induces higher-order spatiotemporal multistabilty — quadra-, hexa-, and septastabilty. These analyses offer valuable insights into the design principles of synthetic bio-circuits and suggest some plausible underlying mechanisms of biological pattern formation.

The dissolution of organ architecture during cancer is driven through the acquisition of several traits by cancer cells. These traits alter the interaction of such transformed cells with their surrounding extracellular matrix (ECM), helping the cells migrate from their origin into their stromal surroundings and subsequently into blood vessels for transport to distant organs. I will discuss a new unpublished story from my group, where we ask how the stromal fibroblasts affect the migration kinetics of breast cancer cells through synthesis of an ECM with novel properties. 

Extracellular matrix (ECM) cues regulate a variety of cellular functions, but their complexity obscures our understanding of their specific contributions to cellular behavior. Our work aims to identify age-related ECM remodeling in tissues and develop biomaterials to mimic matrix properties in vitro at micro-to-nano length scales. This talk highlights some of our systems that have revealed age- and matrix-dependent regulation of cellular dysfunction that can ultimately inform future matrix-based treatment strategies. 

The maturation of functional eggs in ovaries is essential for successful reproduction in mammals. Yet, despite its biological and clinical importance, the underlying mechanisms regulating folliculogenesis remain enigmatic. Here, we report a novel role of theca cells (TCs) in regulating follicle growth through mechanical signalling. Direct mechanical measurements reveal that these TCs are highly contractile and exert compressive stress to the follicle, potentially through active assembly of fibronectin around the follicles. Abolishing TC contractility disrupts fibronectin assembly, increases follicle size, and decreases intrafollicular pressure and viscosity. We further show that the granulosa cells (GCs) within the follicles exhibit spatial patterns of YAP signalling and proliferation. Manipulation of tissue pressure through bulk compression, laser ablation or pharmacological perturbation of TC contractility leads to changes in GC YAP signalling, proliferation, oocyte-GC communications, and impaired follicle growth at long-term. Altogether, our study unveils the unique role of TC-mediated tissue pressure in mammalian folliculogenesis.

Colorectal cancer (CRC) is the third most common solid cancer and the second leading cause of cancer deaths worldwide. It harbours 70% mutations in tumor suppressor p53 leading to higher metastatic potential and drug resistance. Of the several mutations implicated in CRC patients, mutations in tumour suppressor p53 are predominant and serve to reduce WT p53 activity and provide neomorphic functions contributing to tumorigenesis. Though the genetic events regulating CRC with p53 loss/mutation have been documented, the epigenetic events accompanying the loss of p53 have not been well understood. This talk would describe our attempts to understand the epigenetic alterations accompanied by the loss or mutation of tumour suppressor Tp53 in colorectal cancer cells and in CRC patient tissues. High resolution and high content imaging experiments in the lab on colorectal cancer cells have demonstrated an increase in the repressive epigenetic marks H3K9me3, H3K27me3 and in the active mark H3K4me3 with the loss of p53. The alterations we observed in nuclear morphometry with increase in nuclear area and heerochromatin to euchromatin volume led us to explore the differential occupancy of the epigenetic marks across the nucleus. This talk will further outline our findings on the rewiring of the distal enhancer landscape and the bivalency induced by the loss of p53 in colorectal cancer cells and tumours.. 

Hydra are able to maintain their immortality and regenerative capacities through cells that are in a continuous state of proliferation. Starting in the 1900s, Ethel Browne and others hypothesized the existence of three morphogens in Hydra, a head activator, a head inactivator and a foot activator, that control patterning and cell fate. More recently, mechanical strain at topological defect sites in regenerating Hydra has been shown to be regulate hydra morphogenesis. We use a series of amputations of varying tissue sizes in the ever-regenerating hydra to dissect the relationship of hydra morphogens. Chemical perturbations to induce a head activator yield dramatically different results depending on tissue size – giving rise to hydra with multiple heads, or complete tissue disintegration.  A positive feedback loop wherein the head activator regulates itself, but also activates its own inhibitor is required to explain the results.

Understanding how the brain's functional activity emerges from the complex interplay of neurons remains a fundamental question in neuroscience. This talk will demonstrate a mathematical modeling approach to simulate the activity of a brain region, comprising of different excitatory and inhibitory neuronal populations. It will describe how collective firing patterns within these populations give rise to emergent activity at the regional level.  Furthermore, it will demonstrate the potential of such models in understanding the underlying biophysics of brain activity in both healthy and diseased states, offering insights that are difficult to infer from raw neuroimaging data alone. 

Epithelial tissues consist of a monolayer of polygonal cells interconnected by cell-cell junctions. Within these tissues, a dynamic interplay between cellular biochemistry, signaling pathways, and the cytoskeletal machinery generates active internal forces. Additionally, ATP-driven actomyosin activity within cells and along junctions facilitates cell-neighbor exchanges. This internal activity fundamentally governs the mechanical behavior of epithelial tissues, playing a crucial role in morphogenesis. In this talk, I will present findings from a computational model that elucidates how internal activity influences the mechanical response of epithelial tissues. 

Complex systems, from the brain to social, ecological or cellular networks are often described by interaction graphs, whose state is captured by the activities of all nodes, e.g., the excitation of neurons in brain networks or the expression levels of genes in subcellular interactions. The dynamics around these states are then observed by the system's response to activity perturbations, e.g., a local spike in neuronal activity, an outbreak of an epidemic or a sudden hike in the expression of one or several genes. How, then, will a complex system respond to such perturbations? Will it remain stable and witness the perturbation decay, or will it lose stability and transition to an entirely new state? Will the system's response be rapid or slow? Will it be dispersed throughout the network or condense on specific nodes? Encoded within the system’s stability matrix, the Jacobian, the answer to all these questions is obscured by the scale and diversity of the relevant graph structures, their broad parameter space, and their nonlinear interaction dynamics. To penetrate this complexity, we develop the dynamic Jacobian ensemble, which allows us to systematically investigate the fixed-point dynamics of a broad range of graph-based nonlinear interaction models. We find that real-world Jacobians exhibit universal scaling patterns in which structure and dynamics are deeply intertwined. Once constructed, these unique - and most crucially, unexplored, Jacobians map each combination of topology and dynamics into an effective weighted graph, whose link weights adapt to capture the effect of the system's nonlinearity. Hence, identical graphs will acquire distinct link weights, depending on the nature of their interaction dynamics - social, biological or technological. The result: effective network maps, whose weighted topology is designed to predict precisely whether the system is stable or unstable, rapid or slow, dispersed or condensed. 

I will describe work in which we use computational descriptions of  large-scale nuclear architecture to model the biophysics of chromatin organization and nucleolus assembly in eukaryotic cells at a mesoscopic scale, using methods  that incorporate cell-type-specific active processes. The model provides predictions for the statistics of positional distributions, shapes, and overlaps of each chromosome as well as the formation of the nucleolus. Simulations of the model reproduce common organizing principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organization of chromosomes (including both gene-density-based and size-based chromosome radial positioning schemes), the nonrandom locations of chromosome territories, and the shape statistics of individual chromosomes. These models combine relatively new concepts in the description of cell-scale biological structuring -  activity and phase separation - illustrating how the overlap of physics, biology and computation can provide quantitative approaches to old problems.

All living organisms need to respond to changing environmental signals for their survival. Therefore, organisms have developed elaborate intracellular and intercellular signaling mechanisms to respond appropriately to changing environmental conditions [1, 2]. In this talk, I will focus on our study of signal-response mechanisms associated with sensing temperature changes using the roundworm, C. elegans, as a model system. In the first part of the talk, I will discuss a theoretical model that helps in understanding how temperature fluctuations are relayed across the thermosensory AFD neuron through the cGMP pathway leading to changes in Calcium response dynamics [3]. In the second part of my talk, I will discuss our experimental study where we look into how the heat shock response dynamics is affected by the AFD neuron specific receptor guanylate cyclases (rGCs). Through behavioral assays and molecular biology studies at the mRNA and protein levels, we find that the rGCs in the neuron play a very important role in upregulating the small heat shock proteins present in the other cells during high temperature (>30 deg celcius) conditions [4].   [1] Sushmita Pal and Rati Sharma, Comp. Biol. and Chem. 93, 107534 (2021) 

[2] Ayush Ranawade, Rati Sharma and Erel Levine. Biomolecules 12, 1645 (2022) 

[3] Abhilasha Batra and Rati Sharma, Manuscript in submission. 

[4] Abhilasha Batra and Rati Sharma, Manuscript in preparation.

Post embryonic development in plants is driven by structures called meristems. Above-ground organs form at the shoot tip within the shoot apical meristem (SAM). The SAM hosts a group of self-renewing stem cells that undergo differentiation and help in organogenesis. The regulation of stem cell populations is orchestrated by two genes, CLAVATA3(CLV3) and WUSCHEL (WUS). Stem cells express CLV3at the SAM tip which downregulates WUS, expressed centrally, a few cell layers below the SAM apex which help perpetuate the stem cell activity. We used atomic force microscopy and confocal microscopy, to show that SAM morphometric parameters are influenced by genetic and mechanical properties, in Arabidopsis thaliana. The morphologies and stiffness of clv3 SAM cells were significantly different as compared to the wild type (wt). Cells were less stiff and their composition was significantly altered from the wild type in clv3 mutant SAM. In addition, the cellular stiffness in clv3 SAMs was heterogeneous as compared to being uniformly stiff for the stem cells in wt SAM. We used these observations to develop an analytical model to delineate changes in the differing architectures of clv3 mutant SAM as a function of the underlying mechanical properties. We model the SAM as an axially growing planar elastic rod attached to an elastic foundation. Our model shows that the changes in mutant SAM morphologies are due to growth-induced mechanical buckling. Using stability analysis,we show that buckling depends on growth and material parameters including foundation stiffness and rod geometry. The incorporation of heterogeneities in growth and material parameters resulted in a significant alteration to the mode shapes following buckling; these corroborated with the experimental findings. Growth and remodelling studies in plant biomechanics help in understanding how mechanical properties of stem cells contribute to SAM morphometrics.

Interfacing optoelectronic materials with neuronal cells provides a platform for understanding the formation and function of neuronal circuits in the brain. Here I will present two examples from my research where I have utilised optoelectronic materials to engineer the growth of neuronal circuits and stimulate their activity. I will first highlight the use of organic semiconductors as artificial photoreceptors for interfacing with the visual system. In these studies, we are utilising the optoelectronic signals from the organic semiconductor/electrolyte interface to stimulate neuronal cells and thereby elicit neuronal activity in blind retinal tissue. These results have implications for the development of all-organic retinal prosthetic devices. Next, I will present the design of micro-nanoscale surface topography on biocompatible scaffolds to mimic the biophysical features in the brain’s extracellular matrix. We use these scaffolds to guide the growth of neurons, understand the formation of neuronal circuits and evaluate the neuronal network activity in response to the biophysical properties of their surrounding environment. These results have implications for developing biocompatible scaffolds to better understand the functioning of the brain. 

Fluidization is a crucial emergent feature of apparently solid-like epithelial cell layers, e.g., our skin, that allows these tissues to perform fundamental biological functions, like morphogenesis, healing a wound, responding to inflammations, etc. A common feature of these phenomena is the development of migratory behaviors of cells starting from a quiescent arrangement of cells in homeostasis. I will describe a general theoretical framework to understand different pathways of fluidization observed in a solid-like epithelial cell monolayer via the conventional epithelial-mesenchymal transition and an unconventional unjamming transition, observed first in an asthmatic human lung epithelium. This model predicts the physical mechanisms and distinct features of the two processes based on the abilities of single cells to change shapes, cooperate with neighbors and generate forces for movement. I will also show how the scope of this model has been broadened to address collective cell behaviors observed in various contexts of development and disease. 

Physiological interstitial spaces exhibit different levels of confinement, constriction, and compression that affect migrating cells in both normal and diseased states. The nucleus in particular experiences external forces from this physical environment during confined migration. Although numerous systems have been developed to induce nuclear deformation and study resultant functional changes, the mechanisms with which nuclear volume is regulated remain unclear due to limitations in time resolution and challenges in imaging with PDMS-based microfluidic chips. Traditional volumetric measurement using confocal microscopy is hindered by high degrees of phototoxicity, slow speed, limited throughput, and artifacts in fast-moving cells. To overcome these challenges, we developed a double fluorescence exclusion microscopy technique designed to operate at the interface of microchannel-adjacent PDMS sidewalls, enabling the tracking of cellular and nuclear volume dynamics during confined migration. By confirming the vertical symmetry of nuclei in confinement, we derived computational estimates of nuclear surface area. We then monitored nuclear volume and surface area under physiological confinement with a time resolution exceeding 30 frames per minute. We observed that during self-initiated entry into confinement, the cell rapidly expands its surface area until a threshold is reached, followed by a sharp decrease in nuclear volume. Additionally, we used osmotic shock to manipulate nuclear volume in confinement and discovered that the nuclear response to hypo-osmotic shock in this setting does not adhere to classical scaling laws. This suggests that the limited expansion capacity of the nuclear envelope may be a limiting factor in nuclear volume regulation in confined in vivo environments. As a clear understanding of nuclear volume dynamics during confined migration is crucial to parsing the functional responses of cells migrating through tight spaces in the body, this system offers a new avenue into understanding nuclear morphology regulation in a physiological environment and its resulting effects of gene expression.