{"id":135,"date":"2024-12-10T10:28:37","date_gmt":"2024-12-10T15:28:37","guid":{"rendered":"https:\/\/www.golive.clarku.edu\/faculty\/profiles\/ranjan-mukhopadhyay\/"},"modified":"2026-04-22T17:38:25","modified_gmt":"2026-04-22T21:38:25","slug":"ranjan-mukhopadhyay","status":"publish","type":"cu_faculty","link":"https:\/\/www.clarku.edu\/faculty\/profiles\/ranjan-mukhopadhyay\/","title":{"rendered":"Ranjan Mukhopadhyay"},"content":{"rendered":"<p><span>Professor\u00a0Mukhopadhyay received his M.Sc. at the Indian Institute of Technology, Kanpur, his Ph.D. from Caltech in 1998, and did postdoctoral work at Simon Fraser University and the University of Pennsylvania. He spent two years at NEC Laboratories in Princeton before joining Clark in 2003.<\/span><\/p>\n<p>Professor Mukhopadhyay works on a range of theoretical and computational problems at the interface of physics and biology. He is fascinated by the mysteries of life at the meso-scale; a central goal of his biophysical research is to uncover the physical and organizational principles that arise at scales between atoms and organisms. His current research activities include cytoskeletal dynamics and self-organization; the physics of biomembranes and the interplay of geometry, elasticity, and spatial organization; information processing in living cells; and the evolutionary dynamics of biochemical networks. In addition to his primary research area, Professor Mukhopadhyay\u00a0also maintains an\u00a0active\u00a0interest\u00a0in foundational problems in quantum mechanics, and in understanding the nature of the mind-brain relationship.<\/p>\n<p>Some ongoing projects include:<br \/>1.\u00a0In-silico evolution of biochemical networks<br \/>Evolution is the fundamental physical process that gives rise to the diversity of biological phenomena. The goal of this research project is to develop a theoretical framework to understand the relation between biological evolution and design\/architectural principles of biomolecular networks. The challenge in formulating any quantitative understanding of this relationship is the complexity of the mapping between genotype and phenotype (which is often not understood), since change is driven by random processes operating at the genotypic level while selection occurs at the phenotypic level. Mukhopadhyay and his collaborators have recently developed a simplified but physically based model in the context of protein-protein interaction networks that makes it possible to generate such a mapping and are currently applying this model to understand architectural principles in relatively simple biochemical circuits as well as to address broad and fundamental questions such as the relation between evolvability and robustness. The project will integrate theoretical ideas and mathematical techniques from nonlinear systems dynamics, stochastic processes, statistical mechanics, and information theory.\u00a0<br \/>\u00a0<br \/>2.\u00a0Physical principles of intracellular protein organization in living cells<br \/>Both eukaryotic as well as prokaryotic cells exhibit a high degree of protein organization and localization, and the proper localization of many of these proteins is often essential for their functioning; mislocalization of these proteins leads to defects in phenotype. The group has applied methods and modeling techniques from statistical mechanics and soft condensed matter to elucidate the physical principles underlying protein organization in living cells. One class of projects involves modeling spatial and temporal organization of the biomembrane, it\u2019s coupling to membrane geometry, and its role in protein localization and organization. A second class of projects focuses on protein clustering and the role of cellular dynamics in modulating protein assemblies and their spatio-temporal localization.<\/p>\n<p>3.\u00a0Cytoskeletal self-organization<br \/>The intracellular actin cytoskeleton is a dynamical system where actin filaments treadmill, growing and disassembling continuously, and the actin polymer network undergoes constant and rapid reshaping. Actin dynamics play a vital role in processes such as cell motility, active cell shape control, generation of cleavage furrow during cell division, and phagocytosis. The goal of this project is to understand the spatiotemporal dynamics and self-organization of the cytoskeleton as well as to model the coupling between cytoskeletal dynamics and membrane mechanics. Professor Mukhopadhyay has developed a minimal dynamical model to explain experimental observations of actin waves in living cells (in particular, in cells where the actin cytoskeleton was chemically depolymerized and then allowed to repolymerize); the next step would be to understand how this dynamics couples to the membrane, which is relevant, for example, for explaining phagocytosis in Dictyostelium cells. Another direction is modeling the dynamics of cell-spreading on a substrate and understanding both the role of the cytoskeleton and the biomembrane in governing this dynamics.<\/p>\n<p>Professor Mukhopadhyay\u2019s teaching experience includes core undergraduate and graduate courses, such as Methods of Physics, Quantum Mechanics, and graduate Electrodynamics; science perspective courses such as Discovering Physics; as well as specialized courses developed by him which include the Physics of Biomolecular Networks, Information theory and Inference, and Advanced Condensed Matter Physics.\u00a0<\/p>\n","protected":false},"author":0,"featured_media":0,"parent":0,"template":"","meta":{"cu_faculty_f180_userid":"C17541358","cu_faculty_first_name":"Ranjan","cu_faculty_last_name":"Mukhopadhyay","cu_faculty_employment_status":"Full Time","cu_faculty_rank":"Associate Professor","cu_faculty_position":"Associate Professor","cu_faculty_phone":"","cu_faculty_email":"rmukhopadhyay@clarku.edu","cu_faculty_location":"","cu_faculty_about":"<p><span>Professor\u00a0Mukhopadhyay received his M.Sc. at the Indian Institute of Technology, Kanpur, his Ph.D. from Caltech in 1998, and did postdoctoral work at Simon Fraser University and the University of Pennsylvania. He spent two years at NEC Laboratories in Princeton before joining Clark in 2003.<\/span><\/p>\n<p>Professor Mukhopadhyay works on a range of theoretical and computational problems at the interface of physics and biology. He is fascinated by the mysteries of life at the meso-scale; a central goal of his biophysical research is to uncover the physical and organizational principles that arise at scales between atoms and organisms. His current research activities include cytoskeletal dynamics and self-organization; the physics of biomembranes and the interplay of geometry, elasticity, and spatial organization; information processing in living cells; and the evolutionary dynamics of biochemical networks. In addition to his primary research area, Professor Mukhopadhyay\u00a0also maintains an\u00a0active\u00a0interest\u00a0in foundational problems in quantum mechanics, and in understanding the nature of the mind-brain relationship.<\/p>\n<p>Some ongoing projects include:<br>1.\u00a0In-silico evolution of biochemical networks<br>Evolution is the fundamental physical process that gives rise to the diversity of biological phenomena. The goal of this research project is to develop a theoretical framework to understand the relation between biological evolution and design\/architectural principles of biomolecular networks. The challenge in formulating any quantitative understanding of this relationship is the complexity of the mapping between genotype and phenotype (which is often not understood), since change is driven by random processes operating at the genotypic level while selection occurs at the phenotypic level. Mukhopadhyay and his collaborators have recently developed a simplified but physically based model in the context of protein-protein interaction networks that makes it possible to generate such a mapping and are currently applying this model to understand architectural principles in relatively simple biochemical circuits as well as to address broad and fundamental questions such as the relation between evolvability and robustness. The project will integrate theoretical ideas and mathematical techniques from nonlinear systems dynamics, stochastic processes, statistical mechanics, and information theory.\u00a0<br>\u00a0<br>2.\u00a0Physical principles of intracellular protein organization in living cells<br>Both eukaryotic as well as prokaryotic cells exhibit a high degree of protein organization and localization, and the proper localization of many of these proteins is often essential for their functioning; mislocalization of these proteins leads to defects in phenotype. The group has applied methods and modeling techniques from statistical mechanics and soft condensed matter to elucidate the physical principles underlying protein organization in living cells. One class of projects involves modeling spatial and temporal organization of the biomembrane, it\u2019s coupling to membrane geometry, and its role in protein localization and organization. A second class of projects focuses on protein clustering and the role of cellular dynamics in modulating protein assemblies and their spatio-temporal localization.<\/p>\n<p>3.\u00a0Cytoskeletal self-organization<br>The intracellular actin cytoskeleton is a dynamical system where actin filaments treadmill, growing and disassembling continuously, and the actin polymer network undergoes constant and rapid reshaping. Actin dynamics play a vital role in processes such as cell motility, active cell shape control, generation of cleavage furrow during cell division, and phagocytosis. The goal of this project is to understand the spatiotemporal dynamics and self-organization of the cytoskeleton as well as to model the coupling between cytoskeletal dynamics and membrane mechanics. Professor Mukhopadhyay has developed a minimal dynamical model to explain experimental observations of actin waves in living cells (in particular, in cells where the actin cytoskeleton was chemically depolymerized and then allowed to repolymerize); the next step would be to understand how this dynamics couples to the membrane, which is relevant, for example, for explaining phagocytosis in Dictyostelium cells. Another direction is modeling the dynamics of cell-spreading on a substrate and understanding both the role of the cytoskeleton and the biomembrane in governing this dynamics.<\/p>\n<p>Professor Mukhopadhyay\u2019s teaching experience includes core undergraduate and graduate courses, such as Methods of Physics, Quantum Mechanics, and graduate Electrodynamics; science perspective courses such as Discovering Physics; as well as specialized courses developed by him which include the Physics of Biomolecular Networks, Information theory and Inference, and Advanced Condensed Matter Physics.\u00a0<\/p>","cu_faculty_degrees":"<span>Ph.D. in Physics,<\/span> California Institute of Technology, 1998\n<span>M.S. in Physics,<\/span> Indian Institute of Technology, 1991","cu_faculty_cv":"https:\/\/faculty180.interfolio.com\/public\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU12YzVEeDFvUTFJNWxkNVpoSll6WnM5bWhmOVcxbjc3UUp2RjBnSzZ4aGVTMkwvckFDcy9hUVdnaHJaZWxNSXI4Zz09","cu_faculty_links":"[]","cu_faculty_scholarly_interests":"","cu_faculty_scholarly_works":"[{\"activityid\":4210,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"&lt;p&gt;Information Transmission in a Biochemical Multi-step Cascade&lt;\\\/p&gt;\",\"Journal Title\":\"\",\"Series Title\":\"Physical Review\",\"Month \\\/ Season\":\"\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"&lt;p&gt;Signal transduction networks can form highly complex interconnected systems within cells with multiple feedback loops. To better understand the evolutionary design principles underlying such networks, we study the effect of feedback along signaling pathways on their capacity for information transmission. To study information transmission along these biochemical channels, we employ chemical Langevin equations, which approximately model the stochastic dynamical behavior of a well-stirred mixture of interacting molecular species, and draw upon Shannon's information theory. Our results show interesting effects of negative versus positive feedback on information transmission.&lt;\\\/p&gt;\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C17541358\",\"status\":[{\"id\":4210,\"status\":\"In Progress\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":1,\"completionorder\":1},{\"id\":4210,\"status\":\"In Progress\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":1,\"completionorder\":1}],\"userid\":\"C17541358\",\"attachments\":[],\"coauthors_list\":[\"Ranjan Mukhopadhyay\",\"Ammar Tareen\",\"Bilal Benjimoun\",\"Ned S. Wingreen\"],\"sort_date\":\"2025-01-01\"},{\"activityid\":7680,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"&lt;p style=&quot;font-size:12px;&quot;&gt;&lt;\\\/p&gt;\\n&lt;p&gt;Evolving a Circadian oscillator from a non-oscillatory network&lt;\\\/p&gt;\",\"Journal Title\":\"\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2025,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C17541358\",\"status\":[{\"id\":7680,\"status\":\"In Progress\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":1,\"completionorder\":1},{\"id\":7680,\"status\":\"In Progress\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":1,\"completionorder\":1},{\"id\":7680,\"status\":\"In Progress\",\"term\":\"Summer\",\"year\":2022,\"termid\":\"2021\\\/05\",\"listingorder\":1,\"completionorder\":1}],\"userid\":\"C17541358\",\"attachments\":[],\"coauthors_list\":[\"Ranjan Mukhopadhyay\",\"M. Zulfikar Ali\",\"Ned S Wingreen\"],\"sort_date\":\"2025-01-01\"},{\"activityid\":1769,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Asymmetry between Activators and Deactivators in Functional Protein Network\",\"Journal Title\":\"Scientific Reports \",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2020,\"Publisher\":\"Nature Publishing Group\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"10\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"10131\",\"ISSN\":\"\",\"DOI\":\"https:\\\/\\\/doi.org\\\/10.1038\\\/s41598-020-66699-y\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/www.nature.com\\\/articles\\\/s41598-020-66699-y\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C17541358\",\"status\":[{\"id\":1769,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2020,\"termid\":\"2019\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C17541358\",\"attachments\":[],\"coauthors_list\":[\"Ranjan Mukhopadhyay\",\"Ammar Tareen\",\"Ned S. Wingreen\"],\"sort_date\":\"2020-1-01\"}]","cu_faculty_awards_and_grants":"[{\"activityid\":1018,\"fields\":{\"Title\":\"Modeling emergence of intentionality in evolving living systems \",\"Sponsor\":\"Templeton Foundation\",\"Grant ID \\\/ Contract ID\":\"\",\"Award Date\":null,\"Start Date\":\"2021-06-01\",\"End Date\":null,\"Period Length\":1,\"Period Unit\":\"Year\",\"Indirect Funding\":0,\"Indirect Cost Rate\":null,\"Total Funding\":\"0\",\"Total Direct Funding\":null,\"Currency Type\":\"USD\",\"Description\":\"\",\"Abstract\":\"&lt;p&gt;Even the simplest life forms appear capable of goal-directed behavior. For example, single-celled bacteria can move\\\/swim towards food sources or away from harmful chemicals, not because of direct physical attraction or repulsion, but rather governed by an apparent drive to survive and reproduce. Such goal-directed behavior is not usually observed in naturally occurring non-living systems. However, given that living systems can also be treated as physical systems subject to the laws of physics, this begs the question: what is special about living systems that enables such goal-directed behavior? Can it be understood in terms of the material organization of the system, which in turn is fine-tuned by eons of evolution? Moreover, given that evolutionary outcomes often appear as historical contingency, is the apparent intentionality displayed by organisms then merely a historical contingency, or does it emerge naturally from general principles of physics, such as those of non-equilibrium stochastic thermodynamics?&lt;\\\/p&gt;\",\"Number of Periods\":3,\"URL\":\"\"},\"facultyid\":\"C17541358\",\"funding\":{\"1873\":{\"id\":1873,\"grantid\":1018,\"fundedamount\":\"0\",\"yearfunded\":1,\"fundedtype\":\"Total\",\"currencytype\":\"USD\",\"startdate\":\"2021-06-01\",\"enddate\":\"2022-06-01\"},\"1874\":{\"id\":1874,\"grantid\":1018,\"fundedamount\":\"0\",\"yearfunded\":2,\"fundedtype\":\"Total\",\"currencytype\":\"USD\",\"startdate\":\"2022-06-01\",\"enddate\":\"2023-06-01\"},\"1875\":{\"id\":1875,\"grantid\":1018,\"fundedamount\":\"0\",\"yearfunded\":3,\"fundedtype\":\"Total\",\"currencytype\":\"USD\",\"startdate\":\"2023-06-01\",\"enddate\":\"2024-06-01\"}},\"coauthors\":{\"1579\":{\"authorid\":1579,\"grantid\":1018,\"firstname\":\"Ranjan\",\"middleinitial\":\"\",\"lastname\":\"Mukhopadhyay\",\"authortype\":\"PI\",\"percenteffort\":\"100\",\"sameschoolflag\":1,\"facultyid\":\"C17541358\",\"primaryunitid\":21}},\"status\":[{\"grantid\":1018,\"status\":\"Submitted - Not Funded\",\"statuslabel\":\"Submitted - Not Funded\",\"term\":\"Fall\",\"year\":2020,\"termid\":\"2020\\\/01\",\"listingorder\":5,\"completionorder\":3}],\"userid\":\"C17541358\",\"attachments\":[],\"sort_date\":\"2021-06-01\"}]","cu_faculty_title":"Associate Professor, Physics","cu_faculty_department":"Physics","cu_faculty_affiliated_departments":"Physics","footnotes":""},"cu_faculty_group":[],"cu_faculty_department":[14],"cu_faculty_position":[],"class_list":["post-135","cu_faculty","type-cu_faculty","status-publish","hentry","cu_faculty_department-physics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Ranjan Mukhopadhyay | Faculty<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clarku.edu\/faculty\/profiles\/ranjan-mukhopadhyay\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ranjan Mukhopadhyay\" \/>\n<meta property=\"og:description\" content=\"Professor\u00a0Mukhopadhyay received his M.Sc. at the Indian Institute of Technology, Kanpur, his Ph.D. from Caltech in 1998, and did postdoctoral work at Simon Fraser University and the University of Pennsylvania. 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