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brunch, 31 July 2016 at Jeremy & Mary's, photo by Mohan

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Deepesh Agarwal

Deepesh Agarwal

Postdoctoral Fellow
deepesh.agrwal at gmail.com

I am a post-doctoral fellow working in Jeremy's lab on a collaborative project with Galit Lahav's lab at HMS and Neil Kelleher's lab at Northwestern University. We are invesitigating information processing by post-translational modification (PTM) with a particular focus on p53 PTMs. I am developing algorithms based on linear programming and uniform sampling to analyze top-down and bottom-up mass spectrometry data for p53 in different cellular states. The broad aim of this project is to establish whether there is indeed a PTM code which encodes the cellular condition and is then decoded or read to activate particular downstream pathways.

I pursued an engineering degree (dual degree program - Bachelors and Masters) in Biochemical engineering and Biotechnology at IIT Delhi, India where I also had hands-on experience in certain wetlab techniques. I was, however, interested in application of mathematics and computer science to get insights into the working of biological systems. I then pursued a one year masters in computational biology and biomedicine at Polytech Sophia Antipolis, France. It was followed by a PhD at INRIA, Sophia Antipolis with Frederic Cazals, in which I undertook algorithmic investigations of the structure of large protein assemblies using native top-down mass spectrometry (PMID 25850436).

last updated on 1 September 2016

Alexander Barabanschikov

Alexander Barabanschikov

Research associate
abarabanschikov at yahoo.com

My background is in theoretical physics, in particular, string theory. Having completed my Ph.D. at Northeastern University in 2004, I decided to move to biology and try to apply theoretical and computational methods to investigate some of the fascinating mechanisms of the functioning of the cell. At the molecular level I had experience in modeling and spectroscopic studies of active sites of (mostly heme) proteins. Numerous studies of theoretical models and chemical model compounds help to identify the reactive motions in the molecule that drive processes like ligand binding or dissociation. At the systems level one is immediately faced with the complexity of reaction networks and the extremely large number of difficult to measure parameters. Currently, I am investigating a certain class of reaction networks arising in post-translational modification with the purpose of finding biologically-important characteristics of these networks that are largely independent of the parameters (such as rate constants). I teach at the Moscow Institute of Physics and Technology and visit Boston regularly.

last updated on 10 October 2012

John Biddle

John Biddle

Postdoctoral Fellow
biddlephys at gmail.com

I did my PhD research in theoretical physics at the University of Maryland, focusing on thermodynamics and statistical mechanics, and especially the study of phase transitions and metastable states. My dissertation was directed by Mikhail Anisimov on the topic of thermodynamic anomalies in supercooled water.

Gene expression in eukaryotes is known to take place away from thermodynamic equilibrium. However, the models that are used to describe eukaryotic gene regulation have so far been derived from our understanding of prokaryotes, where gene expression takes place at equilibrium. My research in the Gunawardena lab aims to improve our understanding eukaryotic gene regulation by taking into account non-equilibrium effects.

last updated on 1 September 2016

David Croll

David Croll

Sabbatical visitor
david.croll at regiscollege.edu

My involvement as a visitor to the Gunawardena group stems from my interest in biochemical reaction networks, especially metabolic pathways. I am building metabolic models in little b, with the goal of understanding the dynamic complexity of metabolism. I am also interested in the development of little b to provide a feature-rich language that will allow biological modelers to express biophysical and regulatory features of enzymes in a natural and convenient fashion.

In graduate school at Purdue University I studied theoretical chemical physics and biophysical chemistry, doing my Ph.D. dissertation research in the group of John Markley, where I used multi-nuclear/multi-dimensional NMR techniques to study the physical chemistry of the ovomucoids, a family of protein-proteinase inhibitors. This was followed by a postdoc at the Biophysics Institute at the Boston University School of Medicine, where I used NMR spectroscopy and computation to study lipoprotein dynamics and biophysical properties.

As a faculty member at Regis College, I have supervised model-oriented undergraduate research projects in biochemistry. I have also been involved in metabolically oriented research projects at Tufts University, including the modeling and measurement of whole body cholesterol metabolism.

last updated on 21 December 2008

Tathagata Dasgupta

Tathagata Dasgupta

Postdoctoral Fellow
tathagata_dasgupta at hms.harvard.edu

I did my PhD in string theory at the University of Cambridge. In the Gunawardena lab, I have been involved in applying and developing mathematical, statistical and computational techniques to learn complexities of living systems. Example projects span from computational algebraic geometry approach in the context of regulation of mammalian glycolysis (PMID 24634222) to using machine learning heuristics in the emerging area of "computational pathology" (PMID 26553024). The latter direction involves working closely in collaboration with clinicians to build predictive models using microenvironmental immunology profile in various contexts of human reproduction and cancer.

last updated on 5 September 2016

Joseph Dexter

Joseph Dexter

UG research student
jdexter at princeton.edu

I am an undergraduate at Princeton in the Department of Chemistry and the Lewis-Sigler Institute for Integrative Genomics. My research with the Gunawardena group is focused on developing biochemically realistic mathematical models of important metabolic and signaling networks. I am particularly interested in understanding how robust behavior is implemented in biological systems through specific molecular features such as enzymatic multifunctionality and oligomerization. My models strive to capture essential biochemical details and make heavy use of algebraic geometric techniques developed by the Gunawardena group to enable analysis. At Princeton I also work on the application of microfluidics to a variety of problems in systems biology and biophysics. I am an active alumnus of the Research Science Institute and have taught at the program the past two summers. Outside of science, I am a student of the classics, where my research is mostly centered on ancient theatre and how classical literature has influenced modern literary and cultural concerns.

last updated on 8 August 2012

Javier Estrada

Javier Estrada

Postdoctoral Fellow
jestrada at hms.harvard.edu

I am a joint postdoctoral fellow between the DePace and Gunawardena labs. I try to understand transcription in eukaryotes using Drosophila development as inspiration (PMID 27368104). This is a fascinating yet extremely complex problem that I try to understand by finding answers to these questions: can we understand eukaryotic transcription using the same concepts we use for prokaryotes? Do regulatory cofactors play an important role in controlling transcription? Is energy being dissipated when genes are turned on and off? Which mechanisms can realistically explain the high mutation rate of regulatory DNA sequences observed between closely related species? These are fundamental questions that we need to answer if we want to get to a precise description of how the genome works. A list of ingredients doesn't make a good recipe unless we also have instructions for how to use them!

I trained as a physicist at the Universidad Autónoma de Madrid, Spain and previously worked with the Gunawardena lab on "cellular interrogation" (PMID 27367445).

last updated on 1 September 2016

Jeremy Gunawardena

Jeremy Gunawardena

Associate Professor of Systems Biology
(617) 432 4839
jeremyat hms.harvard.edu

I used to be a very pure mathematician, an algebraic topologist, but fell from grace some years ago (to borrow Marc Kac's gracious way of putting it) when I volunteered to teach computer science while a L.E. Dickson Instructor in the Mathematics Department at the University of Chicago. That started my fascination with complexity, which eventually led to a long stint in industrial research at HP (Hewlett-Packard) Labs, where I ran part of the company's "blue skies" research programme. Post-genome systems biology brings complexity to centre stage and brought me to Harvard.

Our focus in the group is on information processing in mammalian cells. In what sense can cellular processes be considered to "process" information? What kind of information is it and how do we measure it? How are information processing tasks implemented by the molecular mechanisms within cells? What systematic methodologies are needed for attacking such problems and how do we develop them? We approach these questions through a combination of experiment, theory and computation.

last updated on 8 August 2012

Dan Lu

Dan Lu

Postdoctoral Fellow
dan_lu at hms.harvard.edu

I completed my PhD in Biochemistry in 2016 at the University of Cambridge working in Gerard Evan's lab. Cancer therapies rely on targeting vulnerabilities in tumour cells that are not present in normal physiological cells. Systemic inhibition of the MYC transcription factor was shown to specific kill tumour cells, but not normal proliferating tissues, establishing a clear therapeutic window. My PhD project was therefore to identify the causal mechanisms of tumour cell death focusing on perturbations of the metabolic and apoptotic pathways.

Currently in the Gunawardena group in collaboration with Galit Lahav's lab, my research project revolves around the transcription factor, p53. The aim is to functionally attribute the roles of post-translational alterations on the p53 protein following stress induction to the subsequent cell fate. The benefits in qualitatively and quantitatively decoding dynamic cell signals are invaluable both in enhancing existing cancer therapies as well as understanding evolutionary and developmental processes.

last updated on 1 September 2016

Mohan Malleshaiah

Mohan Malleshaiah

Postdoctoral Fellow
mohan_malleshaiah at
hms.harvard.edu

I am interested in understanding the nature of molecular networks and how they process signal information to "compute" cell fate decisions. To this end I utilize integrative approaches by combining quantitative measurements (at single-cell & population level) with computational analysis and modelling. I have explained new mechanisms for cell state transition in the budding yeast as well as in pluripotent stem cell model systems.

I did my PhD with Professor Stephen Michnick at University of Montreal where I analyzed the protein complex dynamics within living cells. I analyzed the MAPK signaling proteins to explain a unique zero-order ultrasensitivity mechanism for switch-like budding to mating decision in yeast cells (PMID 20400943). I also explained how yeast cells simultaneously integrate multiple signals and prioritize their response by tuning sensitivity to signals through cross-pathway interactions (PMID 22186894).

Inspired to explain cell fates in the highly complex mammalian system, I chose to analyze stem cells for my postdoctoral research. As a CIHR (Canadian Institutes of Health Research) Fellow at Professor Jeremy Gunawardena's lab, I developed an integrative approach to analyze embryonic stem cells (ESC). In collaboration with Professor Alfonso Martinez-Arias' lab at Cambridge University, I applied this approach to understand the dynamics of transcription factors (TFs) network of pluripotency during ESCs differentiation into the alternative cell fates. We discovered that a subset of pluripotency TFs is reconfigured to generate new networks that promote differentiation (PMID 26832399 and Cell Reports, 2016).

To better understand individual stem cell behavior and their population heterogeneity, I have further implemented single-cell proteomics, and computational methods to analyze the complex data. In collaboration with Professor George Daley's lab. I am analyzing distinct cell populations during reprogramming of pluripotent stem cells to a) totipotent cells and b) hematopoietic stem cell progenitors.

My publications on NCBI.

last updated on 14 November 2016

Felix Wong

Felix Wong

UG research student
fwong at college.harvard.edu

I graduated from Harvard College with an A.B. degree in mathematics and an S.M. degree in computer science in 2014. I am currently a third-year Ph.D. student in Harvard's applied physics program, advised by Ariel Amir and supported by the NSF Graduate Research Fellowship. I have been working with Jeremy's lab since I was an undergraduate and have a particular interest in the "linear framework" and its applications to gene regulation and non-equilibrium mechanisms (PMIDs 25475875 and 27368104). My personal website is here.

last updated on 1 September 2016

 

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