CAAM/STAT 499, BioMath Foundations Seminar, Fall 2003

Modeling and Simulation of Genetic Regulatory Systems

When & Where: Wednesdays 5-6 PM in HB 227

Resources: Human

Blair Christian,
Steve Cox,
Mark Embree,
Rudy Guerra,
Brad Peercy,

Resources: 2 monographs, a review paper, and a hypertext

[1] M. Ptashne and A. Gann, Genes & Signals, Cold Spring Harbor Laboratory Press, 2002.
[2] P. Baldi and G.W. Hatfield, DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Cambridge University Press, 2002.
[3] H. De Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology, Vol. 9, No. 1, 2002, pp. 67-103.
[4] Biology Hypertext.

There are a large number of beautiful/intriguing/maddening introductory/expository texts. Some favorites are
[A] E. Schroedinger, What is Life?
[B] A. Kornberg, For the Love of Enzymes: The Odyssey of a Biochemist.
[C] J.D. Watson (and G.S. Stent), The Double Helix, Norton Critical Edition.
[D] R. Dawkins, The Selfish Gene.
[E] S. Kauffman, The Origins of Order.


September 3: [1] Chapter 1, Lessons From Bacteria: The Lac Operon and Bacteriophage lambda. Also hit chapter 7 of [4] and here . You may also wish to preview the circuit diagram of McAdams & Shapiro .

September 10: [3, sec. 4], Boolean Networks. Origins: S. Kauffman, The Large Scale Structure and Dynamics of Gene Control Circuits, J. Theoretical Biology 44, 1974, pp. 167-190. Tutorial and Phage Example and an Inference Paper

September 17: [3 sec.6], Nonlinear Ordinary Differential Equations. We follow Chapter 9 in Computational Cell Biology, Fall, Maryland, Wagnerand Tyson, eds., Springer, 2002.

September 24: [3 sec. 10], Stochastic Simulation. Its application to chemical reactions stems from work of Gillespie. We have coded two of his examples. This code produced this plot and this code produced this plot. See Arkin et al. for its application to Phage lambda. StochSim is a general purpose biochemical simulator.

October 1: [2] Chapter 4, Gene Expression Profiling Experiments. Also, check out Chapters 3 & 4 of Microarray Gene Expression Data Analysis: A beginners guide (ISBN 1-4051-0682-4, Blackwell Publishing)

October 8: [2] Chapters 5 and 6, Statistical Analysis of Array Data

October 15: Guest lecture by Craig Bush.
There is a long standing interest in our lab concerning the glucocorticoid steroid hormone's role as an anti-proliferative chemotherapeutic option (1). Molecular biology has confirmed early speculation that the steroid's mechanism of action primarily involves transcriptional regulation (2). That is, the glucocorticoid ligand behaves in a concentration dependent manner as a switch; envoking a yet largely ill-defined genomic signalling cascade (3,4). The hormone's efficacy is fleeting and physicians must constantly consider the risks of side-effects (5). It is our hypothesis that with the technical advances currently being made in functional genomics that signaling networks can be computed using probabilistic networks a priori (6). To this end, we will generate very dense time course data using microfluidics to measure both transcriptional rates and relative abundance of synchronized murine lymphoma cells. We will then use prior data to supplement the generation of causal networks displaying the temporal and spatial inter-dependencies of genes making up the transcriptome (7). From there, it is our belief that drug discovery will largely be guided in the future by so-called pathway maps, using causal networks to help define figures of merit when studying a theoretical drug's pharmacology.
References (Enter PMID number into Pubmed.)
(1) Chen et al. PMID: 12795418
(2) Adcock. PMID: 11448148
(3) Yudt and Cidlowski PMID: 1214532
(4) Gottlicher et al. PMID: 9660166
(5) Schacke et al. PMID: 12441176
(6) de Jong. PMID: 11911796
(7) Huang PMID: 10475062

October 22: Jordan Almes and Ed Castillo
The Basics of Boolean Models and an Application to Phage Lambda
In boolean models of gene networks, genes are either on or off at a given time step. The state of a gene X at the next time step is determined by a boolean logic function whose input is the current state of the n genes who influence gene X. Often, n is very small compared to the total number of genes in the system. We shall present the basics of implementing a boolean model and an application to Phage Lambda. If time permits, the reverse engineering of boolean networks as proposed by H. Lahdesmaki et al will also be discussed.

October 29: Slava Fofanov
Basics of phylogenetic analysis
Molecular phylogenetics is a study of evolutionary relationships among organisms or genes. As with previous VIGRE talks, this presentation will mainly focus on the basic methods to infer phylogeny. In particular, we will cover parwise sequence alignment algorithms (Needleman-Wunch) and multiple sequence alignment heuristics (ClustalW alogirhm). We will then focus on the phylogenetic tree construction methods (UPGMA) and the interpretation of the trees. Finally, we will see how these methods were used in my latest project in inferring phylogenies of proteins involved in NFKB pathway, a very important immune-response pathway.

November 5: All not going to A&M
Expose what is, and is not, working at all levels of our organization. We shall review PFUGwork Seminar-work and outreach work. We shall muse on when and where to hold party and what we would like to do next term.

November 12: Reverse Engineering
During our Wednesday seminars we have discussed various methods that may be used to model gene networks, including Boolean models, differential equation models, and Stochastic Models. Such models are useful as tools to describe how genes in a network regulate each other through their transcription products and can be formulated with biological knowledge of gene network connectivity and kinetics. We would like to address the problem of inferring gene network connectivity and kinetic from experimental data, such as that obtainable from gene microarrays. Dr. Mark Embree will provide an introduction of the basic problem, Eddie Castillo will speak on reverse engineering Boolean networks, Nick Henderson and Megan Abadie will speak on differential equation models, and Robert Mallery will speak on structured matrices.
Here is a PC trailer.

November 19: Integrative Model of Synaptic Plasticity Involving the CREB Gene Cascade in a Two-cell Circuit
PFUG Members: Megan Abadie, Amanda Geck, Jane Hartsfield, Zack Kilpatrick, Joanna Papakonstantinou, Dr. Brad Peercy, and Dr. Paul Smolen
Abstract: While mathematical models of gene regulatory networks are a hot topic in mathematical biology, few models integrate multi-cell systems and their individual gene networks. Our biochemical model of the memory stabilization process includes the dynamics of a two-cell neural circuit and each cell's individual gene network. This CREB gene network increases the effectiveness of the ways cells signal across each synapse. We represent the combination of cell circuits and gene networks by linking their ODE systems.
Recommended Reading: CAAM 415 page
Activity Dependent CREB Phosphorylation: Convergence of a fast, sensitive calmodulin kinase pathway and a slow, less sensitive mitogen-activated protein kinase pathway written by Gang-Yi Wu, Karl Deisseroth, and Richard W. Tsein. Proc. Nat'l Acad. Sci vol. 98.5, 2808-2813 (2001).
Synapse site


1 credit: Participate in Wednesday seminar meetings.
2 credit: Take an active role in a PFUG and contribute to a report and/or oral presentation.