About

Ciriyam Jayaprakash is an Emeritus Professor in the Department of Physics at The Ohio State University. His areas of expertise include modeling of the viral antagonists and immune system response, stochastic effects in subcellular processes, and the application of nonlinear dynamics to ecological systems and fully-developed turbulence. His educational background includes a Ph.D. in Physics from the University of Illinois at Urbana-Champaign obtained in 1978, an M.S. in Physics from the California Institute of Technology in 1975, an M.Sc. in Physics from the Indian Institute of Technology, Kanpur in 1973, and a B.Sc. in Physics from Loyola College, Madras University in 1971. Dr. Jayaprakash has contributed to the field through his research and teaching at Ohio State University, focusing on complex systems and biological modeling.

Research topics

• Physics
• Statistical physics
• Condensed matter physics
• Computer science
• Biology

Selected publications

• Abstract 5807: Differential T cell response to anti-PD1 in breast cancer Sub-types is driven by activity of intra-tumoral immune cells

  Cancer Research · 2025-04-21

  article

  Abstract Immune checkpoint inhibition (ICI) holds great promise for triple-negative breast cancer (TNBC), while it shows limited response hormone receptor positive (ER/PR+) breast cancer (CaBr). We employed the Farcast CaBr TruTumor histoculture platform, that preserves the native tumor microenvironment (TME), to study the role of tumor resident immune cell types in determining T cell activation levels on ICI treatment, in the two CaBr sub-types. Freshly resected tumor tissue samples along with matched blood were collected from consented patients. Tumor explants were generated and distributed into arms and cultured for 72 h. Media was replenished every 24 h and the supernatant was stored. The response to stimulation with anti-CD3 (100 ng/mL) + Interleukin-2 (IL-2, 100 IU/mL) and treatment with Nivolumab (132 µg/mL) was evaluated using cytokine release and flow cytometry based immune profiling. CaBr (n=115) samples had lower immune component than head and neck squamous cell carcinoma (n=113) (p<0.0001) but similar to ovarian (n=21) and renal cell (n=52) cancer prior to treatment with ICI. Amongst the two sub-types, immune component in TNBC (n=43) was higher than ER/PR+ subtypes (n=46) (p<0.05). TNBC (n=10) showed a greater proportion of lymphocyte (58.46%) compared to myeloid (39.21%) compartment. This bias was not observed in ER/PR+ samples (n=12). Anti-CD3+IL2 stimulation showed similar response between the two CaBr sub-types. Upon treatment with Nivolumab, 2 out of 4 TNBC samples exhibited a response phenotype, with >1.6-fold increase in CD8+GzmB+ cells and interferon-gamma (IFN-γ) release. In contrast, only 1 out of 4 ER/PR+ CaBr samples showed a modest increase of 1.3 fold for CD8+GzmB+ cells with no detectable IFN-γ release. To understand the basis for the differential response in the two sub-types, we studied in detail, one ER/PR+ (H1) and two TNBC (T1 and T2) samples, displaying varying levels of response. T2 and H1 did not show a Nivolumab response phenotype, whereas T1 demonstrated a strong T-cell reinvigoration and tumor cytotoxicity. Interestingly, all three samples showed anti-CD3+IL2 stimulation driven T cell response. tSNE analysis of CTLs in the control arm showed two distinct sub-populations of exhausted CTLs (CD8+PD1+). Population1 (Pop1) was Granzyme B-positive, while population2 (Pop2) was not. Upon anti-CD3 stimulation, Pop1 showed further increase of activation whereas Pop2 did not, indicative of Pop2 being an irreversibly exhausted T cell population. T2 notably had lower Pop1 and Pop2 CTLs, along with highest proportion of monocytes across all three samples pointing towards an immunosuppressive TME. H1 mainly contained over-exhausted Pop2 and negligible Pop1 CTLs. Interplay between different TME immune sub-types, thus influence response to Nivolumab in CaBr. This is effectively captured by the TruTumor platform. Citation Format: Mouniss MM, Biswajit Das, Kowshik Jaganathan, Syamkumar V, Moumita Nath, Chandan Bhowal, Dharanidharan M, Saikrishna S, Abdul Haseeb, Pallavi R, Kubera Chandran, Rajashekar M, Oliyarasi M, Méhul Kapur, Jayaprakash C, Venkatesh T, Ganesh MS, Amritha Prabha, Prakash BV, Ravi Krishnappa, Upendra K, Ritu Malhotra, Govindaraj K, Pavithira ., Mohit Malhotra, Nandini Pal Basak, Satish Sankaran. Differential T cell response to anti-PD1 in breast cancer Sub-types is driven by activity of intra-tumoral immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5807.

  PublisherDOI

• Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers

  Computational and Structural Biotechnology Journal · 2025-01-01

  articleOpen access

  Solid tumors are characterized by a spatially heterogeneous mixture of cancer cells, immune cells and other non-tumor cells. Recent characterization of the heterogeneity at the single cell level has revealed spatial patterns of different cell types often lacking a simple geometric structure associated with cancer progression. Here we investigated the occurrence of physical "fencing" of tumor cells by specific immune and non-immune phenotypes in the tumor microenvironment (TME) and the association of these clusters to cancer progression in a wide range of solid tumors formed in different organs. We analyzed published datasets providing patient tumor progression data coupled with imaging mass cytometry (IMC) data obtained from tumor tissues to characterize the presence of fencing clusters of various cell types and their association with differing patient outcomes. The six datasets spanned patients with triple-negative breast cancer (279 patients), lung cancer (416 patients), melanoma (30 patients), colorectal cancer (9 patients), glioma (185 patients), and head and neck cancer (139 patients). Devising and employing simple mechanistic and stochastic spatially-resolved computational models we examine two potential mechanistic hypotheses regarding the pro- and anti-tumor roles of a fencing cluster via physical blocking and chemokine gradient in the local environment, respectively. The fencing structures formed by non-tumor cells in the TME can be important for connecting microscopic cellular patterns to tumor progression and treatment responsiveness to immunotherapy in solid cancers.

  PublisherDOI

• Corporate Social Responsibility Communication in Sustainable Agriculture Through the Lens of its Stakeholders

  Journal of Communication and Management · 2025-08-25

  articleOpen access

  Corporate Social Responsibility (CSR) has become a vital strategy for businesses seeking to enhance their brand image while contributing to society. India stands out as the first country to mandate CSR activities under Section 135 of the Companies Act 2013, requiring high-profit corporations to allocate at least 2% of their earnings to CSR initiatives. Companies often collaborate with government agencies or NGOs, directing funds toward social and environmental projects. This study explores stakeholder perspectives in the agricultural sector, particularly organic farmers, corporate sponsors, and media entities, to understand CSR’s role in sustainable agriculture. Using a qualitative approach grounded in Stakeholder and Agenda-Setting theories, the research highlights how CSR communication through media can bridge the gap between corporations and stakeholders. Findings reveal that many farmers remain unaware of CSR support in sustainable agriculture, emphasizing the need for enhanced corporate media strategies. The study concludes that effective CSR communication fosters continuous engagement, ultimately helping corporations create a sustainable agricultural ecosystem. It provides valuable insights for businesses to better address farmers’ needs through strategic CSR initiatives.

  PublisherOA PDFDOI

• Spatial statistics of submicron size clusters of activating and inhibitory Natural Killer cell receptors in the resting state regulate early time signal discrimination

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-28

  preprintOpen access

  Abstract Natural Killer (NK) cells are lymphocytes of the innate immunity and sense healthy or diseased target cells with activating and inhibitory NK cell receptor (NKR) molecules expressed on the cell surface. The protection provided by NK cells against viral infections and tumors critically depends on their ability to distinguish healthy cells from diseased target cells that express 100- fold more activating ligands. NK cell signaling and activation depend on integrating opposing signals initiated by activating and inhibitory NKRs interacting with the cognate ligands expressed on target cells. A wide range of imaging experiments have demonstrated aggregation of both activating and inhibitory NKRs in the plasma membrane on submicron scales in resting NK cells. How do these submicron size NKR clusters formed in the resting state affect signal discrimination? Using in silico mechanistic signaling modeling with information theory and published superresolution imaging data for two well-studied human NKRs, activating NKG2D and inhibitory KIR2DL1, we show that early time signal discrimination by NK cells depends on the spatial statistics of these clusters. When NKG2D and KIR2DL1 clusters are disjoint in the resting state, these clusters help NK cells to discriminate between target cells expressing low and high doses of the activating cognate ligand, whereas, when the NKR clusters fully overlap the NK cells are unable to distinguish between healthy and diseased target cells. Therefore, the spatial statistics of submicron scale clusters of activating and inhibitory NKRs at the resting state provides an additional layer of control for signal discrimination in NK cells. Significance Signal integration of opposing signals initiated by activating and inhibitory NK cell receptors (NKRs) in a noisy environment determine an NK cell’s response to healthy and diseased target cells. Superresolution microscopy imaging revealed aggregation of NKRs in submicron scales in resting NK cells. Using computational modeling, information theory, and published imaging data, we show when these clusters of the opposing NKRs are disjoint, the NK cells can separate healthy from diseased target cells but fail to do so when the clusters overlap. Thus, spatial statistics of submicron-sized NKR clusters in the resting state provide a lever for distinguishing self from non-self. The results suggest spatial organization of receptors in the resting state in may modulate signal discrimination in immune cells.

  PublisherOA PDFDOI

• Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-26

  preprintOpen access

  Abstract Brain cancer is one of the most aggressive forms of cancer in the central nervous system occurring as primary or metastatic tumors. Sequencing of resected tissues from glioblastoma (GBM) and brain metastases (BrMET) reveals high heterogeneity in neoantigens and T cell receptor (TCR) repertoires. Our analysis of published sequencing data in different spatial regions of tumors GBM and BrMET patients show the presence of T cell clones of sizes with a heavy right-tailed distribution spanning several orders of magnitude (e.g., 1 – 1000 cells) with a few (<10) large clone sizes and many small clones. We investigated how neoantigens in the tumor microenvironment (TME) drive T cell expansion in GBM and BrMET by developing a mechanistic mathematical model based on the interaction of T cells and the neoantigens that incorporates their stochastic proliferation in the immunosuppressive environment and trained it to predict the emergence of T cell clones in different spatial regions. The model accurately predicts the distribution of observed T cell clone sizes and reveals that the strength of interaction between TCR and neoantigen-MHC complex and stochastic T cell proliferation crucially regulates T cell expansion in the TME. It also suggests higher rate of T cell proliferation BrMET compared to GBM. An extended version of the model predicts the ability of individual neoantigens to generate T cell clones in the periphery in patients receiving personalized neoantigen vaccines. Our model may facilitate the discovery of improved peptide combinations in neoantigen vaccine studies. Significance Statement Neoantigen-driven T cell responses are key to immune defense against solid tumors. Multi-region sequencing of brain tumors reveals spatial heterogeneity in neoantigens and T cell repertoires. To understand whether neoantigen-driven T cell expansion underlies the TCR repertoire heterogeneities, we developed a stochastic, mechanistic model of T cell proliferation using published TCR and neoantigen data from primary and metastatic brain tumors. The model accurately predicts clone size distributions, showing faster T cell proliferation in metastases and stronger responses to clonal (shared) neoantigens than to private (region-specific) ones. The model is extended to describe T cell clonal expansion in the periphery in response to neoantigen vaccine in glioblastoma patients. This framework may help design optimal peptide combinations in neoantigen vaccine development.

  PublisherOA PDFDOI

• An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data

  Entropy · 2025-03-06

  articleOpen accessCorresponding

  Recent single-cell experiments that measure copy numbers of over 40 proteins in thousands of individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the statistical time-evolution of protein abundances in single cells, information that could yield insights into the mechanisms influencing the biochemical signaling kinetics of a cell. However, when multiple candidate models (i.e., mechanistic models applied to initial protein abundances) can potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. For example, popular approaches like Kullback-Leibler divergence and Akaike's Information Criterion are often difficult to implement largely because mathematical expressions for the likelihoods of candidate models are typically not available. To perform model selection, we introduce an entropy-based approach that uses split-sample techniques to exploit the availability of large data sets and uses (1) existing generalized method of moments (GMM) software to estimate model parameters, and (2) standard kernel density estimators and a Gaussian copula to estimate candidate models. Using simulated data, we show that our approach can select the "ground truth" from a set of competing mechanistic models. Then, to assess the relative support for a candidate model, we compute model selection probabilities using a bootstrap procedure.

  PublisherOA PDFDOI

• An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-04

  preprintOpen access

  Recent single-cell experiments that measure copy numbers of over 40 proteins in individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the time-evolution of protein abundances that could yield mechanisms that underlie signaling kinetics. We recently developed a generalized method of moments (GMM) based approach that estimates parameters of mechanistic models using TSS data. However, when multiple mechanistic models potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. Popular approaches like Kullback-Leibler divergence and Akaike's Information Criterion are difficult to implement because the distribution that gave rise to the "noisy" data is only known numerically and approximately. To perform model selection in this situation, we introduce an entropy-based approach that incorporates our GMM based parameter estimation and commonly used estimators in kernel density estimation. Using simulated TSS data, we show that our approach can select the "ground truth" from a set of competing mechanistic models. Furthermore, we use a bootstrap procedure to compute model selection probabilities, which can be useful when measuring the relative support of a candidate model.

  PublisherOA PDFDOI

• Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-13

  preprintOpen access

  Abstract Solid tumors are characterized by a spatially heterogeneous mixture of cancer cells, immune cells and other non-tumor cells. Recent characterization of the heterogeneity at the single cell level has revealed spatial patterns of different cell types often lacking a simple geometric structure associated with cancer progression. Here we investigated the occurrence of physical fencing of tumor cells by specific immune and non-immune phenotypes in the tumor microenvironment (TME) and the association of these clusters to cancer progression in a wide range solid tumors formed in different organs. We analyzed published patient response and imaging mass cytometry (IMC) datasets from tumor microarrays obtained from tumor tissues in triple-negative breast cancer (279 patients), lung cancer (416 patients), melanoma (30 patients), colorectal cancer (9 patients), glioma (185 patients), and head and neck cancer (139 patients) to characterize the presence of fencing clusters of various cell types and their association with differing patient outcomes. Devising and employing simple mechanistic and stochastic spatially-resolved computational models we quantify the dependence of the pro- and anti-tumor roles of a fencing cluster on the size and the lifetime of the cluster, as well as the chemokine gradient in the local environment. We unveiled that spatial patterns of immune cells, especially through fencing tumor boundary, affects tumor progression and treatment responsiveness to immunotherapy.

  PublisherOA PDFDOI

• Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immunotherapy in melanoma patients

  PNAS Nexus · 2024-11-26 · 3 citations

  articleOpen access

  Abstract High-dimensional, spatial single-cell technologies, such as CyTOF imaging mass cytometry (IMC), provide detailed information regarding locations of a large variety of cancer and immune cells in microscopic scales in tumor microarray slides obtained from patients prior to immune checkpoint inhibitor (ICI) therapy. An important question is how the initial spatial organization of these cells in the tumor microenvironment (TME) changes with time and regulates tumor growth and eventually outcomes as patients undergo ICI therapy. Utilizing IMC data of melanomas of patients who later underwent ICI therapy, we develop a spatially resolved interacting cell system model that is calibrated against patient response data to address the above question. We find that the tumor fate in these patients is determined by the spatial organization of activated CD8+ T cells, macrophages, and melanoma cells and the interplay between these cells that regulate exhaustion of CD8+ T cells. We find that fencing of tumor cell boundaries by exhausted CD8+ T cells is dynamically generated from the initial conditions that can play a protumor role. Furthermore, we find that specific spatial features such as co-clustering of activated CD8+ T cells and macrophages in the pretreatment samples determine the fate of the tumor progression, despite stochastic fluctuations and changes over the treatment course. Our framework enables the determination of mechanisms of interplay between a key subset of tumor and immune cells in the TME that regulate clinical response to ICIs.

  PublisherDOI

• Tumor histoculture captures the dynamic interactions between tumor and immune components in response to anti-PD1 in head and neck cancer

  Nature Communications · 2024-02-21 · 8 citations

  articleOpen access

  Dynamic interactions within the tumor micro-environment drive patient response to immune checkpoint inhibitors. Existing preclinical models lack true representation of this complexity. Using a Head and Neck cancer patient derived TruTumor histoculture platform, the response spectrum of 70 patients to anti-PD1 treatment is investigated in this study. With a subset of 55 patient samples, multiple assays to characterize T-cell reinvigoration and tumor cytotoxicity are performed. Based on levels of these two response parameters, patients are stratified into five sub-cohorts, with the best responder and non-responder sub-cohorts falling at extreme ends of the spectrum. The responder sub-cohort exhibits high T-cell reinvigoration, high tumor cytotoxicity with T-cells homing into the tumor upon treatment whereas immune suppression and tumor progression pathways are pre-dominant in the non-responders. Some moderate responders benefit from combination of anti-CTLA4 with anti-PD1, which is evident from better cytotoxic T-cell: T-regulatory cell ratio and enhancement of tumor cytotoxicity. Baseline and on-treatment gene expression signatures from this study stratify responders and non-responders in unrelated clinical datasets.

  PublisherOA PDFDOI

Frequent coauthors

• Kan Chen

  48 shared

• Jayajit Das

  The Ohio State University

  35 shared

• F. Hayot

  Icahn School of Medicine at Mount Sinai

  35 shared

• H. R. Krishnamurthy

  Indian Institute of Science Bangalore

  30 shared

• Wolfgang Wenzel

  Karlsruhe Institute of Technology

  25 shared

• Ravi Bhagavatula

  Wichita State University

  23 shared

• G. Grinstein

  21 shared

• Sanjoy K. Sarker

  Islamic University

  18 shared

Education

• Ph. D, Physics

  University of Ilinois at Urbana-Champaign

  1978