Quantitative Molecular Biology

[Marguerite Litscher]

Thisis an introductory laboratory-based course designed to teach basic biological laboratory skills used in exploring the quantitative nature of biological systems and the computational reasoning required for performing research in computational biology. Over the course of the semester, students will perform various experiments and computationally analyze the results of these experiments. Students will also use computation to design experiments based on the data they collect. During this course students will be using traditional, well-developed techniques as well as automated lab equipment to answer scientific questions: How should different sources of DNA in a specimen be identified? What changes do cells undergo during apoptosis? Understanding the results of these experiments will require students to think critically about the data they generate, the appropriate controls required to support their conclusions, and the biological context within which these results were obtained. During this course students will gain experience in many aspects of scientific research, including: designing and executing protocols for traditional and automated experiments, computational processing and analysis of collected results and communicating results to peers and colleagues.

Quantitative biology is an umbrella term encompassing the use of mathematical, statistical or computational techniques to study life and living organisms. The central theme and goal of quantitative biology is the creation of predictive models based on fundamental principles governing living systems.[1][2]

Are you curious about studying how life works at a molecular level? Are you interested in solving biological problems using quantitative molecular-based techniques? The biochemistry and molecular biology major may be of interest to you!

You will begin your studies with foundation courses in biology, chemistry, physics and mathematics. At the upper-division level, you will take a four-course sequence in advanced biological topics and an intensive laboratory course designed to introduce you to the methods and procedures used in studying biochemical processes. You will supplement your courses in biology with sequences in organic chemistry. All of your advanced coursework is designed to give you maximal opportunities to increase your skill and familiarity with research laboratory procedures.

Modern biology research increasingly relies on quantitative tools to make precise measurements of cell state. This course provides an introduction to the experimental techniques and computational methods that enable the quantitative study of biological systems. We start with an intro to programming using Python and we employ the learned skills to analyze proteomics and sequencing data for studying gene networks within and across species, modeling biochemical reactions to study the dynamics of gene and protein networks, and extracting information about the spatial organization of biological systems using fluorescence imaging.

The Cell and Molecular Biology program provides a core curriculum in those subjects that are of importance to all students in the fields of cell and molecular biology, complemented by specific courses in each student's area of concentration. Specializations include: molecular, genetic and quantitative biology approaches in the study of normal and disease states; the structure-function relationships of nucleic acids, proteins, carbohydrates, and lipids; genome and proteome regulation, organization, and expression; cell membranes, motility, and cytoskeletal dynamics; development and differentiation; receptors and signal transduction; organelle trafficking and modulation; the development and function of the nervous system; and regulatory events in the immune system. The program features a laboratory rotation system that allows students to research in three or more laboratories of their choice before selecting an advisor. Required CMB coursework includes a semester-long course in which students choose two-week in-depth modules taught by faculty experts in those areas, a grant-writing course, a course that focuses on core techniques used in cell and molecular biology research, and a seminar class in which more senior students give research talks to first- and second-year students. Peer and faculty mentoring sessions, extensive professional development and wellness resources, and a strong commitment to diversity, equity and inclusion are also integral aspects of the CMB program.

Mission: Cell and Molecular Biology (CMB) is an interdisciplinary Ph.D. program at Duke University that empowers diverse students to become rigorous, responsible, independent scientists, equipped with the technical, operational and professional skills needed to thrive in the modern biomedical workforce. CMB achieves this objective through a safe, equitable, ethical and inclusive training environment, an integrated, trainee-centered curriculum and research experience, and a robust program of faculty mentoring, skills development, career advancement and institutional support.

The diversity of metazoan life forms that we experience today arose as multicellular systems continually sampled new phenotypes that withstood ever changing selective pressures. This phenotypic diversification is driven by variations in the underlying regulatory network that instructs cells to form multicellular patterns and structures. Here, we computationally construct the phenotypic diversity that may be accessible through quantitative tuning of the regulatory network that drives multicellular patterning during C. elegans vulval development. We show that significant phenotypic diversity may be sampled through quantitative variations without overhauling the core regulatory network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically deduce the quantitative molecular changes that may have transpired during the evolution of the Caenorhabditis genus.

Copyright: 2009 Giurumescu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the Institute for Collaborative Biotechnologies Grant DAAD 19-03-D-0004 from the U.S. Army Research Office (to A.R.A.), the Center for Biological Circuit Design at Caltech, and the Jacobs Institute for Molecular Engineering for Medicine. P.W.S. is an investigator with the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The fundamental question then is how much phenotypic variation is possible by quantitative perturbations in network performance without wholesale changes to network topology. On the one hand, we may expect that the wild-type multicellular phenotype may be highly robust to quantitative variations. Indeed, computational analysis of the Drosophila segment polarity network demonstrated the robustness of the wild-type multicellular pattern to significant parameter changes [7]. This robustness may be a more pervasive property of developmental regulatory networks that allows their modular utilization in different multicellular geometries and developmental contexts [8]. On the other hand, for a given multicellular system, some degree of fragility in the regulatory network is essential for evolutionary diversification. New multicellular phenotypes must be accessible through modifications to the underlying regulatory network, providing avenues for sampling new phenotypes that may be more beneficial under different selective pressures.

The anchor cell (AC) stimulates the vulva precursor cells Pn.p with LIN-3 in a graded manner. These cells laterally interact with their neighbors through the LIN-12 pathway. The crosstalk between LIN-3 and LIN-12 signaling results in the wild-type pattern of differentiation 332123. In the wild-type organism, the 1 vulval lineage generates progeny that forms the orifice and connects to the uterus, while the 2 vulval lineage generates progeny that form the vulval lips and connect to the body epidermis. The daughters of the 3 cells fuse to the surrounding syncytium and do not contribute to the vulval tissue.

We sought to better understand how much phenotypic diversity a developmental regulatory network can produce through quantitative changes without altering the network architecture. To conduct this analysis, we started with our previously reported mathematical model of the regulatory network that controls vulval development in C. elegans [21]. This model uses ordinary differential equations to track the activity of two key signals in each precursor cell: MAP kinase and the lateral Notch signal (details are provided in Materials and Methods). The levels of these two signals are then used to predict the fate of each cell. The model consists of eight dimensionless parameters whose values influence the pattern of fate choices (Figure 2A). To determine the phenotypes that are accessible through quantitative modulation of the network, we allowed each parameter to vary across a broad range of physiological values (Materials and Methods). For each combination of parameter values, the multicellular phenotype was computed. In this manner, the multidimensional parameter space was divided into sub-regions associated with specific multicellular phenotypes (Figure 2B).

(A) Model parameters. The model has eight dimensionless parameters associated with the various molecular interactions known to contribute to the specification of vulval precursor cells (see also Materials and Methods). (B) Schematic of the phenotype phase diagram. This diagram portrays a simplified, three-dimensional version of the 8-dimensional phenotype phase diagram. Each axis represents a model parameter. Each point in parameter space yields a specific multicellular phenotype, such as the wild-type (332123, black). (C) The total number of predicted phenotypes eventually saturates as the volume of the parameter space is expanded. (D) Distribution of parameter space occupancy (PSO). A histogram depicting the number of phenotypes (bars) occupying different fractions of the parameter space. This histogram is compared to a log-normal distribution (filled circles). The arrows indicate the PSO values of some experimentally observed phenotypes.

3a8082e126