Marc W. Kirschner Ph.D.

    The Kirschner lab studies spatial organization and temporal control in several different biological contexts, including the cell cycle, the cytoskeleton, and embryonic development. They also study a number of important signaling pathways, notably the Wnt pathway and various post-translational modification systems.

Department Faculty

Visiting Faculty

    Roy Kishony Ph.D.

    The Kishony lab is interested in understanding the system-level architecture of genetic networks and the interplay between their design and the evolutionary process. They combine theoretical and experimental approaches to study epistasis networks – networks that describe how perturbations (mutations or drugs) in a given biological system combine to aggravate or alleviate each other’s effect on a phenotype.

Lecturers & Instructors

    Gavin MacBeath Ph.D.

    The MacBeath lab is interested in  identifying, characterizing, and perturbing large collections of proteins or protein domains as a first step in understanding how the cell exploits molecular recognition to regulate complex processes such as protein trafficking, intercellular communication, growth factor signaling, and apoptosis.

    Laura Maliszewski Ph.D.

    Laura Maliszewski, PhD joined HMS in September 2012 to manage the development of the Laboratory of Systems Pharmacology and the Harvard Program in Therapeutic Science. She served previously as an Officer in the Science and Innovation Network of the UK Foreign and Commonwealth Office, developing a broad portfolio of research collaborations in regenerative medicine, health economics and stratified medicine.

    Debora Marks, Ph.D.

    One million human genomes, will it make a difference?


    The large and growing volume of genome information, from all forms of life, presents unprecedented opportunities for computational biologists. The challenge for our scientific generation is to turn an avalanche of sequence information into meaningful discovery of biological principles, predictive methods, or strategies for molecular manipulation for therapeutic and biofuel discovery. 

    The Marks lab is a new interdisciplinary lab dedicated to developing rigorous computational approaches to critical challenges in biomedical research, particularly on the interpretation of genetic variation and its impact on basic science and clinical medicine. To address this we develop algorithmic approaches to biological data aimed at teasing out causality from correlative observations, an approach that has been surprisingly successful to date on notoriously hard problems. In particular, we developed methods adapted from statistical physics and graphical modeling to disentangle true contacts from observed evolutionary correlations of residues in protein sequences.  Remarkably, these evolutionary couplings, identified from sequence alone, supplied enough information to fold a protein sequence into 3D.  The software and methods we developed is available to the biological community on a public server that is quick and easy for non-experts to use. In this evolutionary approach to accurately we have predicted the 3D structure of hundreds of proteins and large pharmaceutically relevant membrane proteins. Many of these were previously of unknown structure and had no homology to known sequences; two of the large membrane proteins have now been experimentally validated. We have now applied this approach genome wide to determine the 3D structure of all protein interactions that have sufficient sequences and can demonstrate the evolutionary signature of alternative conformations. 


    The vision for the Marks lab is to build computational methods that address three critical challenges (i) protein conformational plasticity in health and disease, (ii) genome-wide evaluation of mutations on disease likelihood, antibiotic resistance and personal drug response, and (iii) synthetic protein design.

    Mario Niepel Ph.D.

    A key challenge in treating cancer is the wide range of effectiveness of current targeted therapeutics and the rapid development of resistance. I am trying to understand the mechanisms of drug responses and development of resistance using established breast cancer cell lines as a model system, since they mirror much of the behavior and heterogeneity of primary disease. I mainly study therapeutic drugs and ligands to receptor tyrosine kinases that modulate signaling through the ErbB family and the PI3K/AKT signaling pathways, which are particularly important in the development and treatment of breast and ovarian cancer. Ultimately, a detailed understanding of drug action will move us towards the development of a more personalized medicine, where tumors from each patient are analyzed on a molecular level so they can be treated with specifically tailored drugs or combinations that have been predicted to maximize efficacy and minimize the risk of resistance and toxicity.

    I focus most of my work on proteins, rather than genomic measures, since they are both the key effectors of cellular function and the targets of the drugs. In my research I combine a variety of protein profiling methods, detailed measurements of phenotypic responses, and biochemical investigation into drug action. I analyze the data by statistical and computational methods to identify both predictors of drug response and the causal determinants drug sensitivities.

    Caroline Shamu Ph.D.

    Dr. Shamu is the Director of the ICCB-Longwood Screening Facility. The ICCB-Longwood Investigator Initiated Screening Program assists academic researchers in carrying out high-throughput screens of chemical and RNAi libraries to identify new tools for biological research. In addition to her expertise in implementing new high throughput assay technologies, Dr.Shamu is active in the development of data standards and repositories for large-scale datasets from high-throughput assays.

Affiliated Faculty

    James Collins Ph.D.

    James J. Collins is an Investigator of the Howard Hughes Medical Institute, and a William F. Warren Distinguished Professor, University Professor, Professor of Biomedical Engineering, Professor of Medicine and Co-Director of the Center for BioDynamics at Boston University. He is also a core founding faculty member of the Wyss Institute for Biologically Inspired Engineering at Harvard University.

    L. Mahadevan Ph.D.

    The Mahadevan group is interested in understanding the organization of matter in space and time, particularly at the scale observable by our unaided senses. We use a combination of techniques to pursue this, ranging from simple observations of phenomena to quantitative experiments and theory.

Department Fellows

    Mohammed AlQuraishi PhD.

    Dr. AlQuraishi's research interests lie at the intersection of systems and structural biology. He aims to obtain a systems-level understanding of biological processes through a molecular-level understanding of biological structures and their interactions. Towards that end he is developing computational methods for predicting the binding partners and quantitative binding affinities of biological molecules from their atomic structure.

    Martin Loose Ph.D.

    Dr. Loose's  research goal is to investigate the mechanisms of biochemical self-organization. He is particularly interested in how minimal protein systems are able to organize intracellular space and how these biochemical modules are conserved or change during evolution. For this he mainly uses biochemical approaches and microscopic techniques.


    Justin Meyer, Ph.D.

    Evolution is notoriously hard to predict primarily because a key population genetic term remains undefined: the fitness landscape. While often depicted in two dimensions, with hills and valleys, true fitness landscapes are many-dimensional with complicated interactions that cannot be described by the simple slopes of a hill. Until recently, the technology did not exist to permit direct experimental determination of landscapes. And even now, the technology is slow and much too laborious to capture the enormous size and dimensionality of landscapes.

    Jeanne Salje Ph.D.

    One of the fundamental problems in biology is how the individual components of a cell act together to form the dynamic and responsive structure that is a living cell. E. coli is an excellent model system for studying basic questions of cellular self organisation due to the topological simplicity that results from a lack of membrane-bound subcellular organelles, the extensive knowledge of components that comes from decades of dedicated research, as well as the fact that as a single celled organism it is not subjected to organism-level complexity.