Third q-bio Summer School: Other Topics in Biological Modeling

Back to The Third q-bio School.

In this lecture series, we will cover other topics related to quantitative biology research.

Lecture 1

Scope

Modeling Cancer Development, Part I Experimental models

Lecturer

James Freyer, LANL

Abstract

A solid tumor in a human is arguably one of the most unique, complex and chaotic biological systems in existence. Contributing greatly to this complexity is the highly heterogeneous tumor microenvironment, which has both spatial and temporal variations in an unaccountably large number of parameters (extracellular chemistry, cellular physiology, metabolism, gene expression and protein composition, to name a few). Unfortunately, this unique microenvironment has numerous adverse effects on the response of a tumor to essentially every therapy that has been devised to date. Thus, improving our understanding of this extremely complex biological system will have benefits for cancer therapy as well as for basic biology. An increasingly important tool in this field is the use of model systems, both experimental and theoretical. This lecture will start with a basic description of the tumor microenvironment, including mechanisms behind the heterogeneity, recent advances in assaying the microenvironment, and impacts on cancer therapy. This will be followed by a description of three-dimensional (3-D) experimental tumor models, focusing on the multicellular spheroid that we use in our laboratory. Two examples of our recent experimental work with spheroids will demonstrate how this system can be used to answer basic questions on the regulation of the cell cycle and protein expression. A new application for spheroids will be presented, along with a new experimental model system we have under development. The lecture will conclude with a description of theoretical models used to describe tumor growth and the tumor microenvironment, including an introduction to a multiscale model developed at Los Alamos that will be presented in much more detail in a subsequent lecture in this series.

Lecture 2

Scope

Protein dynamics

Lecturer

Hans Frauenfelder

Abstract

Proteins are involved in essentially every biological reaction. In texts they are usually shown just as X-ray diffraction reveals them, namely rigid, without hydration shell and bulk solvent. Proteins are, however, dynamic systems that continuously fluctuate and the fluctuations are essential for functions. The lecture will discuss two concepts that are the basis for understanding the dynamics, namely the existence of an energy landscape and the influence of the hydration shell and the bulk solvent.

Lecture 3

Scope

How to Model HIV Infection

Lecturer

Alan Perelson

Abstract

Mathematical modeling of HIV infection has lead to asignificant dvances in our knowledge about HIV and its treatment. This lecture will provide a tutorial about how one goes from data to developing models that have practical utility. Modeling will involve ordinary differential equations. The basic biology of HIV will be reviewed as well as the action of drugs used in therapy. If you have questions about HIV/AIDS this would be good venue to ask them.

References

Perelson, A. S. and Nelson, P. (1999). Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev. 41, 3-44.

Callaway, D. S. and Perelson, A. S. (2002). HIV-1 infection and low steady-state viral loads. Bull. Math. Biol. 64, 29–64.

Perelson, A. S. (2002). Modelling viral and immune system dynamics. Nature Rev. Immunol. 2, 28-36.

Rong, L., Feng, Z. and Perelson, A. S. (2007). Mathematical analysis of age-structured HIV-1 dynamics with combination therapy. SIAM J. Appl. Math. 67, 731-756.

Lecture 4

Scope

Modeling Cancer Development

Lecturer

Yi Jiang

Abstract

Lecture 5

Scope

Cell-Based Modeling Approach: GGH and CompuCell3D

Lecturer

James Glazier

Lecture 6

Scope

Cancer Systems Biology

Lecturer

[Vito Guaranta]

Abstract

Systems Biology (SB) can be viewed as a system of linked coordinates that slides alongBiological Scales. SB practitioners still tend to work primarily at one particular biological scale, but their distinctive trait is a worry about connecting, or integrating, with scale levels above and below. There are misconceptions about Systems Biology, e.g., that it isa mindless accumulation of data by some high-throughput means, no hypothesis necessary prior to experimentation, or, more dangerous, that large amounts of data automatically provide important answers. One truth about SB is that, for now, it can be comfortably ignored by “Conventional Biology”. If Cancer Systems Biology entails a system of linked coordinates that one can slide along the Biological Scales of Cancer, then what are the Biological Scales of Cancer? They span the whole of life, from genes to populations. In modern cancer research, there is a general disconnect between scales, even between disciplines that focus on one scale, e.g., cancer epidemiology andcancer biology. This has produced an enormous loss of information, because it dismisses the concept of emergent properties, e.g., cells perform functions like proliferation or motility that cannot be assigned to elements of a lower scale, molecules or genes, for instance. However, these functions are possible because of genes and molecules. In addition a fundamental misconception is that emergent properties are something mysterious and cannot be addressed quantitatively. There is an unmet need to define cancer progression in terms of emergent properties as one moves from one scale to the other. A key activity in Cancer SB is collecting large datasets. Major barriersto producing large datasets have been mostly removed for genetic or proteomic data. However, they still exist at higher scale. Particularly noteworthy is the dearth of data at the cell biology level, which hinders the mapping of genotype to phenotype. By necessity, large datasets require modeling for interpretation, from statistical to mathematical to computational, since they defy intuition. While large datasets are not a prerequisite to begin modeling cancer, eventually they are an absolute need. A major challenge in cancer SB remains data collection, interpretation, integration and incorporation into models at the cellular scale.

Lecture 7

Scope

Integration of Experimentation with Modeling

Lecturer

Alissa Weaver

Abstract

This lecture gives an introduction to integration of experimentation with mathematical and computational modeling at all stages. The lecture will include the following parts: 1) Rationale for integration; 2) Stages of integration: i: premodel: framing the question; ii: generation of assumptions; iii: parameterization; iv: validation/testing of model predictions, both general and specific; v: model development/modification; 3) Philosophy on best use of data-model integration. Concrete examples of types of experimental integration (by stage) and the usefulness will be discussed.

Lecture 8

Scope

Mathematical Modeling of Epidemiology / Writing Custom Term Papers

Lecturer

Mac Hyman