Faculty Description


  • PROFESSOR
    Joint appointment: Professor, Section of Molecular Biology, Division of Biological Sciences
  • Ph. D., M.I.T., 1990
  • http://matisse.ucsd.edu
Contact
  • Office Location:  
    Office: UH 7246
    Lab location: NSB 2104-08
  • Office Phone:  858-534-7263
    Lab Phone: 858-534-5817
  • Email:  thwa@ucsd.edu
  • Administrative Contact:  Nancy Steinmetz
Research Statement
  • We use a combination of theoretical and experimental approaches to elucidate the organizational principles of living systems. The goal is to quantitatively characterize the physiological behaviors and understand how they arise in terms of the underlying molecular interactions. The model organism our lab focuses on is the bacterium E. coli, although we also study other organisms in collaboration with other labs. Please visit our lab webpage for further information.
Awards & News
  • Honors and Awards
  • - UCSD Physicist Terry Hwa elected to Fellowship in the American Academy of Microbiology

    Fellows of the Academy are elected annually through a highly selective, peer-review process, based on their records of scientific achievement and original contributions that have advanced microbiology. There are over 2,000 Fellows representing all subspecialties of microbiology, including basic and applied research, teaching, public health, industry, and government service. Dr. Hwa's lab is interested in attaining a quantitative, predictive understanding of links between molecular interactions and physiological responses in bacteria, focusing mostly on E. coli. They use a complementary two-pronged approach. In a bottom-up approach, his lab performs quantitative characterization of combinatorial transcriptional control and post-transcriptional controls to construct quantitative models of gene regulation based on molecular properties. In a top-down approach, they characterize phenomenological laws governing bacterial growth, metabolism, and gene expression. The phenomenological laws can be exploited to provide precise quantitative predictions between physiological perturbations and responses; they can also be used to guide the elucidation of molecular signaling mechanisms.

    - Burroughs-Wellcome's Innovation Award in Functional Genomics, 2000-2003 Guggenheim Fellowship, 1999-2000.

    - Beckman Young Investigator Award, 1997-2000.

    - Office of Naval Research Young Investigator Award, 1995-1998.

    - A. P. Sloan Foundation Research Fellowship, 1994-1999.

    - Overseas Chinese Physics Association's Outstanding Young Researcher Award, 1993.

    - American Physical Society's LeRoy Apker Award, 1986
  • Simple Math Sheds New Light on a Long-Studied Biological Process
  • One of the most basic and intensively studied processes in biology--one which has been detailed in biology textbooks for decades--has gained a new level of understanding, thanks to the application of simple math to a problem that scientists never before thought could benefit from mathematics.

    The scientists who made the discovery, published in this week's advance online publication of Nature, found that the process bacteria use to quickly adapt to metabolize preferred energy sources such as glucose--a process called "catabolite repression"--is controlled not just by glucose, as had long been known and taught, but just as much by other essential nutrients, such as nitrogen and sulfur, available to bacteria in their growth medium.

    "This is one of the most studied processes in molecular biology; it's in every textbook," says Terence Hwa, a professor of physics and biology at UC San Diego, who headed the team of scientists. "We showed that this process doesn't work the way most people thought it did for the past several decades, and its purpose is different from what had generally been assumed."

    The basic phenomenon, Hwa says, is analogous to a balanced diet: To reduce an individual's sugar uptake, common wisdom is to reduce the availability of sugar. This strategy backfires on bacteria because they would increase their appetite for sugars -- the process of catabolite repression would direct the bacteria to increase the production of their sugar uptake system to counteract the scarcity of sugar in the environment. However, by figuring out that catabolite repression actually works by sensing the difference between the influx of sugar and that of other essential nutrients such as nitrogen, it is possible to drastically lower the bacteria's appetite for sugar by simply rationing the supply of nitrogen.

    Hwa and his team arrived at their surprising finding by employing a new approach called "quantitative biology," in which scientists quantify biological data and discover mathematical patterns, which in turn guide them to develop predictive models of the underlying processes.

    "This mode of research, an iterative dialogue between data quantitation and model building, has driven the progress of physics for the past several centuries, starting with Kepler's discovery of the law of planetary motion," explains Hwa. "However, it was long thought that biology is so laden with historical accidents which render the application of quantitative deduction intractable."

    The significance of the study, according to Hwa, is that it demonstrates that the physicists' quantitative approach can also effectively probe and elucidate biological processes, even a classic problem that has been heavily scrutinized.

    "Molecular biology gives us a collection of parts and interactions," says Hwa. "But how do you make sense of those interactions? You need to examine them in their physiological context. Quantitative patterns in physiological responses, together with mathematical analysis, provide important clues that can reveal the functions of molecular components and interactions, and in this case, also pinpoint the existence of previously unknown interactions."

    "It is remarkable that after so many years of studying these cells there are more fascinating things to be discovered by simple experiments and theory," says Krastan B. Blagoev, a program director in the National Science Foundation's Division of Physics, which jointly funded the research with the agency's Molecular and Cellular Biology Division.

    Hwa and his team of physicists and biologists at UC San Diego are among the world's leaders in quantitative biology, which is gaining an upsurge of interest and importance in the life sciences. According to a recent National Academy of Sciences report, advances in quantitative biology are a necessary ingredient to ensure our nation continues to make future progress in medicine, genetics and other life science disciplines. By quantifying the complex behavior of living organisms, for example, researchers can develop reliable models that could allow them to more accurately predict processes like drug interactions before untested pharmaceuticals are used in human clinical trials. UC San Diego is in the middle of a major expansion in quantitative biology, with plans to hire 15 to 20 faculty members in this new discipline in different departments over a three-year period.

    In their study, the UC San Diego scientists collaborated with colleagues at Peking University in China, the University of Marburg in Germany and the Indiana University of School of Medicine--an international research team formed six years ago with the help of a grant from the Human Frontier Science Program, headquartered in Strasbourg, France.

    Biologists have long known that when glucose is the primary carbon source for cells, bacteria such as E. coli repress genes that allow the organism to metabolize other kinds of sugars. This catabolite repression effect is controlled by a small molecule known as "cyclic adenosine monophosphate"--or cAMP.

    "Previously, it was thought that glucose uptake sets the cAMP level in the cell," says Hwa. "But we discovered that in reality, it's the difference between carbon uptake and the uptake of other essential nutrients such as nitrogen. So the picture now is very different."

    The UC San Diego scientists unraveled this relationship by measuring the level of cAMP and the level of enzymes that break down sugar molecules in bacterial cells against the growth rates of the bacteria, while subjecting these cells to limiting supplies of carbon, nitrogen and other compounds.

    "When we plotted our results, our jaws dropped," recalls Hwa. "The levels of the sugar uptake and utilization enzymes lined up remarkably into two crossing lines when plotted with the corresponding growth rates, with the enzyme level increasing upon carbon limitation and decreasing upon nitrogen and sulfur limitation. The enzyme levels followed the simple mathematical rules like a machine." "From the overall pattern, it is clear that there's nothing special about glucose," he adds. "Now we know this process is not about the preference of glucose over other carbon compounds, but rather the fine coordination of carbon uptake in the cell with other minor, but essential nutrient elements such as nitrogen and sulfur."

    Hwa points out that the physiological insights derived from simple mathematical relations guided them to figuring out both the strategy and molecular mechanisms their bacteria employ to coordinate carbon metabolism with those of other elements. Such knowledge may be very valuable to the fermentation industry, where metabolic engineers strive to rewire the genetic programs of industrial microorganisms to increase their yield of desirable products, such as insulin for biomedical applications and ethanol for bioenergy.

    Hwa further speculates that by similarly quantifying how the human metabolic control system deals with different types of nutrient limitations, one may envision novel strategies to combat diseases such as obesity, which involves an imbalance of macronutrient composition, or even cancer, which requires a full suite of nutrient elements to fuel its rapid growth.

    While quantitative biology papers are often filled with complicated mathematical formulas and involved heavy number crunching by computers, Hwa says the mathematics used in this discovery was surprisingly simple.

    "We just used line plots," he says. "Our entire study involves just three linear equations. They're the kind of things my 10-year-old daughter should be able to do. Quantitative biology doesn't have to be fancy." Like their mathematical approach, Hwa says his team's experiments were simple enough most of them could have been done 50 years ago. In fact, one prominent scientist was on the right track to discovering the same thing nearly 40 years ago. The Nobel-Prizewinning French scientist Jacques Monod, who was the first to study the effects of catabolite repression quantitatively during World War II 70 years ago and whose study led eventually to the birth of molecular biology 20 years later, wrote a paper published months after his death in 1976 that questioned the standard understanding of catabolite repression--a publication that had been long forgotten until Hwa mentioned his team's results recently to some colleagues from France.

    "Monod knew that something was not quite right with the standard picture of cyclic AMP," says Hwa, who was directed to that 1976 paper. "He knew that nitrogen was having an effect on the input and he knew that somehow it was very important."

    Hwa says he and his team are now applying the same quantitative approaches to learn more about the response of bacteria to antibiotics and how cells transition from one state to another. "This kind of quantitative, physiological approach is really underutilized in biology," he adds. "Because it's so easy to manipulate molecules, biologists as well as biophysicists tend to jump immediately to a molecular view, often decoupled from the physiological context. Certainly the parts list is important and we could not have gotten to the bottom of our study without all of the molecular work that had been done before. But that in of itself is not enough, because the very same parts can be put to work in different ways to make systems with very different functions."

    Other authors of the paper were UC San Diego scientists Conghui You, Hiroyuki Okano, Sheng Hui, Zhongge Zhang, Minsu Kim and Carl Gunderson; Yi-Ping Wang of Peking University in China; Peter Lenz of the University of Marburg in Germany; and Dalai Yan of the Indiana University School of Medicine in Indianapolis.

  • Physics and Quantitative Biology: identifying a simple genetic circuit for stripes
  • Many living things have stripes, but the developmental processes that create these and other patterns are complex and difficult to untangle.

    Now a team of scientists has designed a simple genetic circuit that creates a striped pattern that they can control by tweaking a single gene.

    With multiple starting points, bacteria guided by a simple genetic circuit can create intricate patterns.
    "The essential components can be buried in a complex physiological context," said Terence Hwa, a professor of physics at the University of California, San Diego, and one of the leaders of the study published October 14 in Science. "Natural systems make all kinds of wonderful patterns, but the problem is you never know what's really controlling it."

    With genes taken from one species of bacterium and inserted into another, Hwa and colleagues from the University of Hong Kong assembled a genetic loop from two linked modules that senses how crowded a group of cells has become and responds by controlling their movements.

    One of the modules secretes a chemical signal called acyl-homoserine lactone (AHL). As the bacterial colony grows, AHL floods the accumulating cells, causing them to tumble in place rather than swim. Stuck in the agar of their dish, they pile up.

    Because AHL doesn't diffuse very far, a few cells escape and swim away to begin the process again.

    Left to grow overnight, the cells create a target-like pattern of concentric rings of crowded and dispersed bacterial cells. By tweaking just one gene that limits how fast and far cells can swim, the researchers were able to control the number of rings the bacteria made. They can also manipulate the pattern by modifying how long AHL lasts before it degrades.


    A colony of bacteria with a "synthetic" genetic circuit develops a pattern of strips over 24 hours.
    Although individual bacteria are single cells, as colonies they can act like a multicellular organism, sending and receiving signals to coordinate the growth and other functions of the colony. That means fundamental rules that govern the development of these patterns could well apply to critical steps in the development of other organisms.

    To uncover these fundamental rules, Hwa and colleagues characterized the performance of their synthetic genetic circuit in two ways.

    First, they precisely measured both the activity of individual genes in the circuit throughout the tumble-and-swim cycle. Then they derived a mathematical equation that describes the probability of cells flipping between swim and tumble motions.

    Additional equations describe other aspects of the system, such as the dynamics of the synthesis, diffusion and deactivation of one of the cell-to-cell chemical signal AHL.

    This three-pronged approach of "wet-lab" experiments, precise measurements of the results, and mathematical modeling of the system, characterize the emerging discipline of quantitative biology, Hwa said. "This is a prototype, a model of the kind of biology we want to do."

  • Understanding drug-resistant bacteria
  • UC San Diego scientists explore role of varying drug levels in speeding bacterial emergence.

    Strains of bacteria able to resist multiple antibiotics pose a growing threat to public health, yet the means by which resistance quickly emerges aren't well understood.

    Scientists led by physics professor Terence Hwa at the University of California, San Diego, thought that the variety of environments in which bacteria encounter antibiotic drugs could play an important role. They have developed a mathematical model, published in the June 18 early online edition of the Proceedings of the National Academy of Sciences, that demonstrates how that would work.

    Drug levels can vary widely between different organs and tissues in the human body, or between different individuals in a hospital. To account for that, their model considers a matrix of "compartments" with differing concentrations of a drug.

    The bacteria in their model can move randomly from one compartment to the next. Their survival and rates of proliferation depend on the concentration of antibiotic within each compartment. And mutations that allow the bacteria to survive and thrive in environments with slightly higher concentrations were allowed to emerge randomly as well.

    The system, designed to represent the varying environment of the human body, showed that drug-resistant mutants could evade competition by invading parts of the body, compartments in the model, with slightly higher drug concentrations where other bacteria fail to thrive.

    When the process is repeated, the bacterial population can quickly adapt to components with much higher drug concentrations, with adaptation rates that would be very unlikely impossible in a uniform environment.

    Although Hwa's team created this model to study the evolution of antibiotic resistance, its formulation quite general. It could be applied to any example of adaptive expansion of an organism's range, a general feature of biological systems that has allowed living things to populate every corner of the Earth, they write.

    "Our mathematical model quantifies hypotheses, makes falsifiable predictions and suggests experiments on this vast subject in which many words have been said but few quantitative statements can be found," Hwa said. "The next step is quantitative experimentations which are being carried out in our lab and elsewhere."

    The iteration of quantitative prediction, experimental characterization and model refinement characterizes quantitative biology, an emerging discipline that aims to fundamentally change biological research from discipline that is descriptive in nature to one that is quantitative and predictive. prerequisite for engineering and synthesis that promise to be the fruit of this century of biology.

    Co-authors include postdoc Rutger Hermsen and graduate student Barret Deris, both members of Hwa's research group. This work was supported by the Center for Theoretical Biological Physics (National Science Foundation) and the National Cancer Institute's Physical Science-Oncology program. Deris holds a National Science Foundation Research Fellowship.

  • Growth Laws Call Shots
  • Synthetic Biology: Linear relationships explain cell response during fermentation

    Cells obey simple "growth laws" that describe linear relationships between cell growth and protein expression, Terence T. Hwa and colleagues at the University of California, San Diego, report (Science, DOI: 10.1126/science.1192588). Their findings could ease the ability to predict cell growth in synthetic biology experiments, fermentation processes, and other areas.

    "Hwa and colleagues have used an integrated computational and experimental approach to show that protein expression influences cell growth and vice versa," says James J. Collins, who studies synthetic biology at Boston University. "That's important because efforts in synthetic biology assume that synthetic circuits and other constructs are in most cases isolated and independent of other actions in cells."

    The researchers changed the state of Escherichia coli cells by inhibiting their ribosomes, which tends to suppress protein expression, or by varying their nutrient levels. They found that when the growth rate increases by boosting nutrients, the ribosome content of cells likewise increases linearly, increasing protein expression. Another linear relationship they found was that "when you slow down the ribosome, the cell makes more ribosome and less of other proteins," Hwa notes.

    On the basis of these growth laws, they divided the bacterium's proteome into three broad categories: ribosomal, metabolic, and housekeeping. Housekeeping proteins, which account for about 50% of the proteome, don't change with growth state, but the cell varies in the other two categories, depending on growth conditions.

    "The rule is extremely simple," Hwa says. "If you have poor nutrients, then you devote more resources to the metabolic portion. If your ribosome is having trouble translating, you devote more resources to the ribosomes.

    The findings also help explain why engineered pathways in bacteria slow down cell growth: Because "unnecessary" proteins produced by engineered genes reduce the production of both metabolic and ribosomal portions of the proteome. "We predicted that the growth rate would drop linearly with the amount of these unnecessary proteins in the cell," Hwa says. They found that growth slowed down just as predicted when engineered E. coli expressed ß-galactosidase in large quantities.

    "The authors have shown that the cell cycle itself, and the general growth state of the cell, plays a big role in the output of your circuit," Collins says.

    Chemical & Engineering News
    ISSN 0009-2347
    Copyright © 2013 American Chemical Society

Selected Publications