Paulsson Lab

Paulsson Lab

Department of Systems Biology 

Harvard Medical School

Department of Systems Biology 

Harvard Medical School

The main topic of our research is stochasticity and control in gene expression, and our most common experimental systems are bacteria and their plasmids. Some of the specific projects we are currently developing are listed below.

Research

Negative feedback is found all over biology and is crucial to suppress fluctuations and keep systems away from undesired states. However, because it always acts indirectly, either after a delay or through an intermediate molecular species, negative feedback could also increase fluctuations. It is in fact mathematically impossible for any control loop to completely overcome the problems associated with indirectness. This places general lower bounds on variances and creates frustration trade-offs where suppressing one noise source instead amplifies another, whether in transcriptional autorepression or the control of body

Plasmids as Model Systems for Fluctuations and Feedback

Genes confront selection at multiple conflicting levels. Selection on groups favors cooperation among group members, while selection within groups favors selfish elements that exploit the common good. Our experimental model will be the competition between plasmids inside bacteria, which exemplify the selection on symbiotes and parasites in general but have fully determined gene function. We will use detailed information on single cell plasmid and molecule

Stochasticity and Multilevel Selection on Self-replicators

Many analytical results so far on stochasticity in chemical reactions are based on a Master Equation approach to a linearized system. We are analyzing higher order consequences of such approaches and trying to extend them such that they can be used in a fluctuation-dissipation formulation in various cases of biochemical interest

Theoretical Basis of Stochastic Chemical Reactions

As cells grow and divide, cellular components and chemical species in the cells are also partitioned and multiplied. Considering the noise of such partitioning and multiplying components, two major questions we are focusing on are: how does the noise of the mother cell affect the noise of daughter cells and how does noise change as the cell grows. A combination of the answers would give the full description of noise in a cyclostationary process.

Fluctuations in Growing and Dividing Systems

Single Molecule Counting Methods

Gene expression and many other biological processes can be greatly randomized when synthesis or degradation occurs in ‘bursts’ of many molecules. But each birth and death of a macromolecule can also involve several small steps, creating a kinetic memory between individual events. Based on generalized stochastic models, we discuss how this could have the opposite effect of bursting or make fluctuations easier to average out – reducing variances without concentration-dependent control. We obtain analytical expressions that encompass this possibility as well as any distribution of timing intervals. Surprisingly, we also find that most single-cell data fits equally well to models with and without kinetic memory, and discriminating between them may require time-series with single-molecule resolution.

Finding the step-size of biochemical fluctuations

numbers, as well as the results of competition experiments, to validate and extend our analytical result on the probabilities of fixation for selfish and altruistic strategies as a function of metabolic burdens, population sizes and conjugation rates. This will simultaneously provide insight into the fitness effects of different control mechanisms and a direct, experimental measurement of the effect of multilevel selection on the evolution of self-replicators.

Different models for the origin of fluctuations in biochemical systems are consistent with current experiments. Distinguishing between them requires being able to precisely determine the number of molecules inside single cells. Furthermore, to determine the consequences of noise on control systems requires the simultaneous measurement of different chemical species in the same cell.  Observing its effects on the evolution of plasmids requires being able to measure the numbers different types of

temperature. The aim of this project is to achieve a more quantitative understanding of negative feedback acting under biological constraints, illustrated by replication control of bacterial plasmids. Bacterial plasmids are simple, tractable, and well characterized, and use negative feedback to suppress fluctuations and reduce extinction rates. We will develop methods to accurately count plasmids copy number in single cells, and to separate between different origins of fluctuations.

We will then test and illustrate the mathematically predicted trade-offs and limits, and finally go back to refine the theory.

plasmids inside a single bacteria. We are developing new methods, based on biological, chemical and optical properties to accurately measure plasmid, mRNA and protein numbers inside single bacterial cells. We have so far developed an accurate method for determining plasmid copy numbers based on conditional replication.