Systems Biology at Harvard Medical School

Systems Biology at Harvard Medical School
The Department of Systems Biology was founded in 2003 in response to a growing
realization that complex diseases—such as rheumatoid arthritis, osteoporosis, cancer, and
schizophrenia—result from complex combinations of factors that are challenging to
address with the current tools of biomedical science. This complexity is forcing biologists
to turn to the intellectual tools of other disciplines, such as physics, mathematics, and
computational science. Harvard Medical School's bold step was to create a full Department
of Systems Biology—the first in the world—within the intense biomedical research
environment of the Longwood Medical Area. Thanks to initial key hires of outstanding
theoreticians with a genuine commitment to biomedical research, the department has
become a magnet for the very best young researchers in this area, and the related field of
synthetic biology. The department's alumni, students and post-doctoral fellows who
trained in the department, are now leading the development of systems biology and
synthetic biology across the globe.
SELECTED STATISTICS
Growth of the Department

The number of members of the Department of Systems Biology grew from 73 to 336
between 2004 and 2014.
Scientific Contributions

The department has published 1,508 papers over the last 10 years, using mathematical
and computational approaches to offer novel insights into cancer, infectious disease,
aging, human physiology, genetic diseases, and vertebrate development. Key
contributions include:
o Computer-enabled methods for measuring cell growth, aging, gene expression,
and cell movement in model organisms;
o A new perspective on how antibiotic resistance evolves, and how to slow or
reverse the spread of resistance;
o Profound revisions to the textbook models of spinal cord development and the
behavior of adult stem cells;
o New models of the behavior of signaling pathways in cancer that help predict
and explain how these pathways behave;
o Dramatic progress in engineering biological materials to make precise shapes,
engineering improved carbon fixation and making useful molecules such as
hydrogen, sugar, and other products;
o New theoretical and experimental approaches to the problem of variation, or
“noise”, in biology. Noise is important in clinical problems, such as “fractional
kill” in cancer therapy and the persistence of bacterial infections after antibiotic
treatment;
o A new level of understanding of the mitochondrion, the powerhouse of the cell,
and its role in disease;
o Novel ways to detect diseases, such as anemia, colon cancer, kidney injury,
diabetes, and tuberculosis, as well as engineered cells that record antibiotic
treatments;
o Methods for predicting protein structure from DNA sequence information,
solving a decades-old problem of great importance;
o Ways to understand drug action at a new level, contributing to the founding of
the Harvard Program in Therapeutic Science (HiTS).
A Pioneering Interdisciplinary Effort

All laboratories in the department include an interdisciplinary mix of biologists,
mathematicians, physicists, computational scientists, and engineers. Because of the
interdisciplinary training we offer, many individual scientists are able to span multiple
disciplines in their own research.

Department faculty and trainees come from many different backgrounds, but are all
entirely committed to biomedical research. This commitment has led to an unusual
degree of focus on real medical problems, compared to other systems biology efforts.
Global leadership

The PhD Program in systems biology is acknowledged to be one of the very best
programs of its kind in the country. It is highly competitive (average ~7% acceptance
rate) and about two-thirds of admitted U.S. students win national fellowship awards.

Ninety-two students have entered the program so far, 24 of whom have graduated. An
average student graduating from the program has published 3.5 papers, two of which
s/he is first author

Post-docs and students from the department are now leading their own labs in many
institutions across the world, as evidenced by the maps on the following page.
More than 100 department alumni are now faculty across the globe…
…with more than 70 in the United States
Systems Biology Accomplishments
Table of Contents
A brief summary of the accomplishments of the Department of Systems Biology in the
last decade (selected from 1508 publications)
Computer-enabled methods for probing cell growth, aging, gene expression,
cell division, and cancer.............................................................................................page 2
A new perspective on how antibiotic resistance evolves, and how to slow or
reverse the spread of resistance................................................................................page 3
Profound revisions to the textbook models of spinal chord development and
the behavior of adult stem cells.................................................................................page 4
New models of signaling pathways in cancer that help predict and explain how
these pathways behave.............................................................................................page 6
Dramatic progress in engineering biological materials and cells to provide
useful products..........................................................................................................page 7
New theoretical and experimental approaches to the problem of variation,
or "noise" in biology...................................................................................................page 8
A new level of understanding of the mitochondrion, the powerhouse of the
cell. ..........................................................................................................................page 10
New ways to detect diseases such as anemia, colon cancer, kidney injury,
diabetes and tuberculosis, as well as engineered cells that record antibiotic
treatments...............................................................................................................page 11
Methods for predicting protein structure from DNA sequence information,
solving a decades-old problem of great importance...............................................page 12
A world-leading effort in applying new theoretical principles to biological
systems....................................................................................................................page 13
Ways to understand drug action at a new level, contributing to the founding
of Harvard Medical School's Therapeutics Initiative................................................page 14
1
Computer-enabled methods for probing cell growth, aging, gene expression, cell
division, and cancer
The power of mathematical and computational analysis has transformed much of daily
life, and is now transforming biological experimentation. Our faculty have been
pioneers in several areas. Publications include:
Kafri R, Levy J, Ginzberg MB, Oh S, Lahav G, Kirschner MW. Dynamics extracted from fixed cells
reveal feedback linking cell growth to cell cycle. Nature. 2013 Feb 28;494(7438):480-3.
Stroustrup N, Ulmschneider BE, Nash ZM, López-Moyado IF, Apfeld J, Fontana W. The
Caenorhabditis elegans Lifespan Machine. Nat Methods. 2013 Jul;10(7):665-70.
Wunderlich Z, Bragdon MD, Eckenrode KB, Lydiard-Martin T, Pearl-Waserman S, DePace AH.
Dissecting sources of quantitative gene expression pattern divergence between Drosophila
species. Mol Syst Biol. 2012;8:604.
Zidovska A, Weitz DA, Mitchison TJ. Micron-scale coherence in interphase chromatin dynamics.
Proc Natl Acad Sci U S A. 2013 Sep 24;110(39):15555-60.
Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL, Kafri R, Kirschner MW, Clish
CB, Mootha VK. Metabolite profiling identifies a key role for glycine in rapid cancer cell
proliferation. Science. 2012 May 25;336(6084):1040-4.
Hao N, Budnik BA, Gunawardena J, O'Shea EK. Tunable signal processing through modular
control of transcription factor translocation. Science. 2013 Jan 25;339(6118):460-4.
Choi PJ, Mitchison TJ. Imaging burst kinetics and spatial coordination during serial killing by
single natural killer cells. Proc Natl Acad Sci U S A. 2013 Apr 16;110(16):6488-93.
2
A new perspective on how antibiotic resistance evolves
Antibiotic resistance is a growing, and extremely worrying, problem. A highly influential
set of publications from the Kishony lab shows that carefully chosen combinations of
drugs can slow, and even reverse, the evolution of resistance. Publications include:
Toprak E, Veres A, Michel JB, Chait R, Hartl DL, Kishony R. Evolutionary paths to antibiotic
resistance under dynamically sustained drug selection. Nat Genet. 2011 Dec 18;44(1):101-5.
Palmer AC, Angelino E, Kishony R. Chemical decay of an antibiotic inverts selection for
resistance. Nat Chem Biol. 2010 Mar;6(3):244.
Bollenbach T, Quan S, Chait R, Kishony R. Nonoptimal microbial response to antibiotics underlies
suppressive drug interactions. Cell. 2009 Nov 13;139(4):707-18.
Chait R, Craney A, Kishony R. Antibiotic interactions that select against resistance. Nature. 2007
Apr 5;446(7136):668-71.
Bollenbach T, Quan S, Chait R, Kishony R. Nonoptimal microbial response to antibiotics underlies
suppressive drug interactions. Cell. 2009 Nov 13;139(4):707-18.
Michel JB, Yeh PJ, Chait R, Moellering RC Jr, Kishony R. Drug interactions modulate the potential
for evolution of resistance. Proc Natl Acad Sci U S A. 2008 Sep 30;105(39):14918-23
Hegreness M, Shoresh N, Damian D, Hartl D, Kishony R. Accelerated evolution of resistance in
multidrug environments. Proc Natl Acad Sci U S A. 2008 Sep 16;105(37):13977-81.
Chait R, Craney A, Kishony R. Antibiotic interactions that select against resistance. Nature. 2007
Apr 5;446(7136):668-71
3
Profound revisions to the textbook models of the behavior of adult stem cells, and the
development of the spinal cord
Adult stem cells reside in individual tissues and are responsible for tissue regeneration
and repair. Until recently, our understanding has been that stem cells always divide
asymmetrically, producing one cell that retains "stem-ness" and another that is used to
regenerate the tissue. New research from Allon Klein now shows that these cells
instead maintain themselves probabilistically: stem cell divisions can produce two stem
cells, one stem cell and a regenerating cell, or two regenerating cells.
New model
Old model
Publications include:
Lim X, Tan SH, Koh WL, Chau RM, Yan KS, Kuo CJ, van Amerongen R, Klein AM, Nusse R.
Interfollicular epidermal stem cells self-renew via autocrine Wnt signaling. Science. 2013 Dec
6;342(6163):1226-30.
Doupé DP, Alcolea MP, Roshan A, Zhang G, Klein AM, Simons BD, Jones PH. A single progenitor
population switches behavior to maintain and repair esophageal epithelium. Science. 2012 Aug
31;337(6098):1091-3.
Klein AM, Simons BD. Universal patterns of stem cell fate in cycling adult tissues. Development.
2011 Aug;138(15):3103-11.
Klein AM, Nikolaidou-Neokosmidou V, Doupé DP, Jones PH, Simons BD. Patterning as a
signature of human epidermal stem cell regulation. J R Soc Interface. 2011 Dec 7;8(65):1815-24.
4
As the spinal chord develops in vertebrate embryos, neural precursors are instructed to
develop into different types of neurons (e.g.motor neurons that control muscles and
sensory neurons that respond to pain, etc.) that must arrange themselves in different
locations. Until this year, our understanding has been that the neural precursors decide
what kind of neuron to become based on their position in the developing spinal cord.
New work from the Megason lab now shows that instead precursors decide what to
become first, then move to the appropriate position.
Old model
New model
Key publication:
Xiong F, Tentner AR, Huang P, Gelas A, Mosaliganti KR, Souhait L, Rannou N, Swinburne IA,
Obholzer ND, Cowgill PD, Schier AF, Megason SG. Specified neural progenitors sort to form
sharp domains after noisy Shh signaling. Cell. 2013 Apr 25;153(3):550-61.
5
New models of signaling pathways in cancer that help predict and explain how these
pathways behave
Cancer is one of the best-studied situations in which multiple mutations interact to
create a disease state. Systems biology approaches aim to understand and predict how
molecular changes cause changes in the behavior of the pathway. The Kirschner, Lahav
and Sorger laboratories are especially active in this area.
Hernández AR, Klein AM, Kirschner MW. Kinetic responses of β-catenin specify the sites of Wnt
control. Science. 2012 Dec 7;338(6112):1337-40
Gaglia G, Guan Y, Shah JV, Lahav G. Activation and control of p53 tetramerization in individual
living cells. Proc Natl Acad Sci U S A. 2013 Sep 17;110(38):15497-501.
Purvis JE, Karhohs KW, Mock C, Batchelor E, Loewer A, Lahav G. p53 dynamics control cell fate.
Science. 2012 Jun 15;336(6087):1440-4.
Gaudet S, Spencer SL, Chen WW, Sorger PK. Exploring the contextual sensitivity of factors that
determine cell-to-cell variability in receptor-mediated apoptosis. PLoS Comput Biol.
2012;8(4):e1002482.
Aldridge BB, Gaudet S, Lauffenburger DA, Sorger PK. Lyapunov exponents and phase diagrams
reveal multi-factorial control over TRAIL-induced apoptosis. Mol Syst Biol. 2011 Nov 22;7:553.
Dasgupta T, Croll DH, Owen JA, Vander Heiden MG, Locasale JW, Alon U, Cantley LC,
Gunawardena J. A fundamental trade off in covalent switching and its circumvention by enzyme
bifunctionality in glucose homeostasis. J Biol Chem. 2014 Mar 14 (in press).
6
Dramatic progress in engineering biological materials and cells to provide useful
products
Biology is an extraordinarily powerful technology. Biological systems have produced all
of the fossil fuels we use today, as well as producing building materials and food from
air, a handful of minerals, and water. One reason for the power of biological systems is
that they can control events on the molecular level through enzymes and molecular
machines. The Silver and Yin labs are leaders in efforts to harness these technologies
for new uses, by building precise structures out of DNA or engineering cells to produce
biofuels such as hydrogen, or other useful materials. Publications include:
Myhrvold C, Dai M, Silver PA, Yin P. Isothermal self-assembly of complex DNA structures under
diverse and biocompatible conditions. Nano Lett. 2013 Sep 11;13(9):4242-8.
Torella JP, Ford TJ, Kim SN, Chen AM, Way JC, Silver PA. Tailored fatty acid synthesis via dynamic
control of fatty acid elongation. Proc Natl Acad Sci U S A. 2013 Jul 9;110(28):11290-5.
Delebecque CJ, Silver PA, Lindner AB. Designing and using RNA scaffolds to assemble proteins in
vivo. Nat Protoc. 2012 Oct;7(10):1797-807
Iinuma R, Ke Y, Jungmann R, Schlichthaerle T, Woehrstein JB, Yin P. Polyhedra Self-Assembled
from DNA Tripods and Characterized with 3D DNA-PAINT. Science. 2014 Mar 13.
Ke Y, Ong LL, Shih WM, Yin P. Three-dimensional structures self-assembled from DNA bricks.
Science. 2012 Nov 30;338(6111):1177-83
Lin C, Jungmann R, Leifer AM, Li C, Levner D, Church GM, Shih WM, Yin P. Submicrometre
geometrically encoded fluorescent barcodes self-assembled from DNA. Nat Chem. 2012
Oct;4(10):832-9.
7
New theoretical and experimental approaches to the problem of variation, or "noise"
in biology
Variation is the heart of biology. It drives evolution — natural selection selects the
fittest among variants — and allows organisms to survive challenges and change. Unlike
a machine, a biological system does not stop working when a change is made. Instead,
it adapts: it makes use of its ability to vary to compensate for the change. Effects
traceable to variation and adaptability are present in all diseases, notably cancer,
infectious disease, and genetic predisposition
Variation has been relatively little studied in biology until the last 10 years. It is difficult
to study because the effects of variation can be deeply counter-intuitive. Our faculty
have been leaders in this field. Examples of key contributions include:
• New theory showing how the behavior of a cell is affected by variation in the
molecules that make up the cell. Johan Paulsson is the acknowledged world leader in
developing sound mathematical foundations for the study of the causes and
consequences of variation. Publications include:
Hilfinger A, Chen M, Paulsson J. Using temporal correlations and full distributions to separate
intrinsic and extrinsic fluctuations in biological systems. Phys Rev Lett. 2012 Dec
14;109(24):248104.
Huh D, Paulsson J. Random partitioning of molecules at cell division. Proc Natl Acad Sci U S A.
2011 Sep 6;108(36):15004-9.
Hilfinger A, Paulsson J. Separating intrinsic from extrinsic fluctuations in dynamic biological
systems. Proc Natl Acad Sci U S A. 2011 Jul 19;108(29):12167-72.
Huh D, Paulsson J. Non-genetic heterogeneity from stochastic partitioning at cell division. Nat
Genet. 2011 Feb;43(2):95-100.
Pedraza JM, Paulsson J. Effects of molecular memory and bursting on fluctuations in gene
expression. Science. 2008 Jan 18;319(5861):339-43.
• New experiments showing the role of variation in bacterial biofilms — a growing
medical threat — antibiotic resistance, cancer, drug responsiveness, and the use of
drug combinations. The Paulsson, Sorger and Kishony labs are leaders in these areas.
Publications include:
Norman TM, Lord ND, Paulsson J, Losick R. Memory and modularity in cell-fate decision making.
Nature. 2013 Nov 28;503(7477):481-6.
8
Lau BT, Malkus P, Paulsson J. New quantitative methods for measuring plasmid loss rates reveal
unexpected stability. Plasmid. 2013 Nov;70(3):353-61.
Fallahi-Sichani M, Honarnejad S, Heiser LM, Gray JW, Sorger PK. Metrics other than potency
reveal systematic variation in responses to cancer drugs. Nat Chem Biol. 2013 Nov;9(11):708-14.
Flusberg DA, Sorger PK. Modulating cell-to-cell variability and sensitivity to death ligands by codrugging. Phys Biol. 2013 Jun;10(3):035002.
Spencer SL, Gaudet S, Albeck JG, Burke JM, Sorger PK. Non-genetic origins of cell-to-cell
variability in TRAIL-induced apoptosis. Nature. 2009 May 21;459
Lieberman TD, Flett KB, Yelin I, Martin TR, McAdam AJ, Priebe GP, Kishony R. Genetic variation
of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective
pressures. Nat Genet. 2014 Jan;46(1):82-7
Toprak E, Veres A, Michel JB, Chait R, Hartl DL, Kishony R. Evolutionary paths to antibiotic
resistance under dynamically sustained drug selection. Nat Genet. 2011 Dec 18;44(1):101-5.
Lieberman TD, Michel JB, Aingaran M, Potter-Bynoe G, Roux D, Davis MR Jr, Skurnik D, Leiby N,
LiPuma JJ, Goldberg JB, McAdam AJ, Priebe GP, Kishony R. Parallel bacterial evolution within
multiple patients identifies candidate pathogenicity genes. Nat Genet. 2011 Nov
13;43(12):1275-80.
9
A new level of understanding of the mitochondrion, the powerhouse of the cell
The mitochondrion is responsible for generating chemical energy for the cell. It is a
special compartment within the cell with an unusual chemical environment, constructed
from a double membrane and a large set of special proteins. Mutations in
mitochondrial proteins cause a wide range of genetic diseases that have acute effects in
children but also are thought to play a role in cancer, diabetes, and neurodegenerative
disease. The Mootha lab is a world leader in using computational techniques to identify
mitochondrial proteins and thereby identify the genetic mutations responsible for
disease. Publications include:
Calvo SE, Compton AG, Hershman SG, Lim SC, Lieber DS, Tucker EJ, Laskowski A, Garone C, Liu S,
Jaffe DB, Christodoulou J, Fletcher JM, Bruno DL, Goldblatt J, Dimauro S, Thorburn DR, Mootha
VK. Molecular diagnosis of infantile mitochondrial disease with targeted next-generation
sequencing. Sci Transl Med. 2012 Jan 25;4(118):118ra10.
Ronchi D, Garone C, Bordoni A, Gutierrez Rios P, Calvo SE, Ripolone M, Ranieri M, Rizzuti M, Villa
L, Magri F, Corti S, Bresolin N, Mootha VK, Moggio M, DiMauro S, Comi GP, Sciacco M. Nextgeneration sequencing reveals DGUOK mutations in adult patients with mitochondrial DNA
multiple deletions. Brain. 2012 Nov;135(Pt 11):3404-15.
Tucker EJ, Hershman SG, Köhrer C, Belcher-Timme CA, Patel J, Goldberger OA, Christodoulou J,
Silberstein JM, McKenzie M, Ryan MT, Compton AG, Jaffe JD, Carr SA, Calvo SE, RajBhandary UL,
Thorburn DR, Mootha VK. Mutations in MTFMT underlie a human disorder of formylation
causing impaired mitochondrial translation. Cell Metab. 2011 Sep 7;14(3):428-34.
Garone C, Rubio JC, Calvo SE, Naini A, Tanji K, Dimauro S, Mootha VK, Hirano M. MPV17
Mutations Causing Adult-Onset Multisystemic Disorder With Multiple Mitochondrial DNA
Deletions. Arch Neurol. 2012 Dec;69(12):1648-51. doi: 10.1001/archneurol.2012.405.
Sancak Y, Markhard AL, Kitami T, Kovács-Bogdán E, Kamer KJ, Udeshi ND, Carr SA, Chaudhuri D,
Clapham DE, Li AA, Calvo SE, Goldberger O, Mootha VK. EMRE is an essential component of the
mitochondrial calcium uniporter complex. Science. 2013 Dec 13;342(6164):1379-82.
Baughman JM, Perocchi F, Girgis HS, Plovanich M, Belcher-Timme CA, Sancak Y, Bao XR,
Strittmatter L, Goldberger O, Bogorad RL, Koteliansky V, Mootha VK. Integrative genomics
identifies MCU as an essential component of the mitochondrial calcium uniporter. Nature. 2011
Jun 19;476(7360):341-5.
10
Methods for predicting protein structure from DNA sequence information, solving a
decades-old problem of great importance
The so-called "protein folding problem" has challenged researchers for decades. In
principle the sequence of a protein includes all of the information required to predict its
structure. Yet, because of the promiscuity of interactions between the building blocks
of the protein, the amino acids, the number of possible structures that could result from
a single sequence is so large that it has been impossible to search through them to find
the correct one. The advent of large-scale genomic sequencing has provided many
clues to protein sequence, by giving us sets of sequences of families of proteins in
different organisms, that presumably all fold in the same way. Even so, it has been
extremely challenging to correct structures from incorrect ones. The Marks lab has
used a new approach based on global statistical methods drawn from physics to solve
this problem for medium-sized proteins in large families. This is a huge step forward in
what has been a very intractable problem. It has been particularly important in the
study of proteins that are hard to study by other means. Key publications:
Hopf TA, Colwell LJ, Sheridan R, Rost B, Sander C, Marks DS. Three-dimensional structures of
membrane proteins from genomic sequencing. Cell. 2012 Jun 22;149(7):1607-21
Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C. Protein 3D structure
computed from evolutionary sequence variation. PLoS One. 2011;6(12):e28766.
Morcos F, Pagnani A, Lunt B, Bertolino A, Marks DS, Sander C, Zecchina R, Onuchic JN, Hwa T,
Weigt M. Direct-coupling analysis of residue coevolution captures native contacts across many
protein families. Proc Natl Acad Sci U S A. 2011 Dec 6;108(49):E1293-301.
11
New ways to detect diseases such as anemia, colon cancer, kidney injury, diabetes and
tuberculosis, as well as engineered cells that record antibiotic treatments
Synthetic biology, imaging and computational analysis have together made it possible to
create new biomarkers of disease that make disease diagnoses faster and more
accurate. Examples include:
Kotula JW, Kerns SJ, Shaket LA, Siraj L, Collins JJ, Way JC, Silver PA. Programmable bacteria
detect and record an environmental signal in the mammalian gut. Proc Natl Acad Sci U S A.
2014 (in press)
Liong M, Hoang AN, Chung J, Gural N, Ford CB, Min C, Shah RR, Ahmad R, Fernandez-Suarez M,
Fortune SM, Toner M, Lee H, Weissleder R. Magnetic barcode assay for genetic detection of
pathogens. Nat Commun. 2013;4:1752
Wood DK, Soriano A, Mahadevan L, Higgins JM, Bhatia SN. A biophysical indicator of
vaso-occlusive risk in sickle cell disease. Sci Transl Med. 2012 Feb 29;4(123):123ra26.
Higgins JM, Mahadevan L. Physiological and pathological population dynamics of circulating
human red blood cells. Proc Natl Acad Sci U S A. 2010 Nov 23;107(47):20587-92.
Miller MA, Askevold B, Yang KS, Kohler RH, Weissleder R. Platinum Compounds for HighResolution In Vivo Cancer Imaging. ChemMedChem. 2014 Feb 6. doi: 10.1002/cmdc.201300502.
Peterson VM, Castro CM, Chung J, Miller NC, Ullal AV, Castano MD, Penson RT, Lee H, Birrer MJ,
Weissleder R. Ascites analysis by a microfluidic chip allows tumor-cell profiling. Proc Natl Acad
Sci U S A. 2013 Dec 17;110(51):E4978-86
Yang L, Besschetnova TY, Brooks CR, Shah JV, Bonventre JV. Epithelial cell cycle arrest in G2/M
mediates kidney fibrosis after injury. Nat Med. 2010 May;16(5):535-43
Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, Carr SA, Thadhani R, Gerszten RE,
Mootha VK. Metabolic profiling of the human response to a glucose challenge reveals distinct
axes of insulin sensitivity. Mol Syst Biol. 2008;4:214.
12
A world-leading effort in applying new theoretical principles to biological
systems
Our Department is recognized as a major nexus for theoreticians aiming to develop
novel theory and computational tools that apply to real-world biological problems, not
simply to toy models. In addition to the examples throughout the notes above, we
highlight the following publications:
Mirzaev I, Gunawardena J. Laplacian dynamics on general graphs. Bull Math Biol. 2013
Nov;75(11):2118-49.
Dexter JP, Gunawardena J. Dimerization and bifunctionality confer robustness to the isocitrate
dehydrogenase regulatory system in Escherichia coli. J Biol Chem. 2013 Feb 22;288(8):5770-8.
Xu Y, Gunawardena J. Realistic enzymology for post-translational modification: zero-order
ultrasensitivity revisited. J Theor Biol. 2012 Oct 21;311:139-52.
Gunawardena J. A linear framework for time-scale separation in nonlinear biochemical systems.
PLoS One. 2012;7(5):e36321. doi: 10.1371/journal.pone.0036321.
Gnad F, Estrada J, Gunawardena J. Proteus: a web-based, context-specific modelling tool for
molecular networks. Bioinformatics. 2012 May 1;28(9):1284-6.
Rowland MA, Fontana W, Deeds EJ. Crosstalk and competition in signaling networks. Biophys J.
2012 Dec 5;103(11):2389-98.
Deeds EJ, Bachman JA, Fontana W. Optimizing ring assembly reveals the strength of weak
interactions. Proc Natl Acad Sci U S A. 2012 Feb 14;109(7):2348-53.
Harmer R, Danos V, Feret J, Krivine J, Fontana W. Intrinsic information carriers in combinatorial
dynamical systems. Chaos. 2010 Sep;20(3):037108.
Kolokotrones T, Van Savage, Deeds EJ, Fontana W. Curvature in metabolic scaling. Nature. 2010
Apr 1;464(7289):753-6.
Goentoro L, Shoval O, Kirschner MW, Alon U. The incoherent feedforward loop can provide foldchange detection in gene regulation. Mol Cell. 2009 Dec 11;36(5):894-9.
Feret J, Danos V, Krivine J, Harmer R, Fontana W. Internal coarse-graining of molecular systems.
Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6453-8.
13
Ways to understand drug action at a new level, contributing to the founding of
Harvard Medical School's Program in Therapeutic Science
Our Department was instrumental in developing the intellectual framework for the
Systems Pharmacology Initiative (led by Peter Sorger) that aims to use mathematical
and computational approaches to better understand and predict the action of drugs.
The Systems Pharmacology Initiative is now a major component of the School's Program
in Therapeutic Science. Efforts in the Kirschner lab have led to new computational
methods for better predicting drug combinations. Encouraging progress has already
been made, as reported in the publications below.
Niepel M, Hafner M, Pace EA, Chung M, Chai DH, Zhou L, Schoeberl B, Sorger PK. Profiles of
basal and stimulated receptor signaling networks predict drug response in breast cancer lines.
Sci Signal. 2013 Sep 24;6(294):ra84.
Tang Y, Xie T, Florian S, Moerke N, Shamu C, Benes C, Mitchison TJ. Differential determinants of
cancer cell insensitivity to antimitotic drugs discriminated by a one-step cell imaging assay. J
Biomol Screen. 2013 Oct;18(9):1062-71.
Fallahi-Sichani M, Honarnejad S, Heiser LM, Gray JW, Sorger PK. Metrics other than potency
reveal systematic variation in responses to cancer drugs. Nat Chem Biol. 2013 Nov;9(11):708-14.
Flusberg DA, Roux J, Spencer SL, Sorger PK. Cells surviving fractional killing by TRAIL exhibit
transient but sustainable resistance and inflammatory phenotypes. Mol Biol Cell. 2013
Jul;24(14):2186-200.
Kleiman LB, Maiwald T, Conzelmann H, Lauffenburger DA, Sorger PK. Rapid phospho-turnover by
receptor tyrosine kinases impacts downstream signaling and drug binding. Mol Cell. 2011 Sep
2;43(5):723-37.
Orth JD, Kohler RH, Foijer F, Sorger PK, Weissleder R, Mitchison TJ. Analysis of mitosis and
antimitotic drug responses in tumors by in vivo microscopy and single-cell pharmacodynamics.
Cancer Res. 2011 Jul 1;71(13):4608-16.
Bollenbach T, Kishony R. Resolution of gene regulatory conflicts caused by combinations of
antibiotics. Mol Cell. 2011 May 20;42(4):413-25.
Torella JP, Chait R, Kishony R. Optimal drug synergy in antimicrobial treatments. PLoS Comput
Biol. 2010 Jun 3;6(6):e1000796.
Gohil VM, Offner N, Walker JA, Sheth SA, Fossale E, Gusella JF, MacDonald ME, Neri C, Mootha
VK. Meclizine is neuroprotective in models of Huntington's disease. Hum Mol Genet. 2011 Jan
15;20(2):294-300.
Gujol TS, Peshkin L, Kirschner MW Exploiting polypharmacology for drug target deconvolution.
Proceedings of the National Academy of Sciences. 2014 Epublication March 19, 2014
14