Team:Heidelberg/HEARTBEAT database

HEARTBEAT: Design of rational promoter sequences

Introduction

In parallel to the biochemical method RA-PCR our team worked on a computational approach for the rational design of promoter libraries. This strategy focused on spatial dependencies between pairs of binding motives and how the distance of a particular binding motive affects transcriptional activity. Similar to existing methods which predict spatial preferences of transcription factor binding sites (TFBS) by detecting statistically overrepresented motives [] we used [http://genome.dkfz-heidelberg.de/| Promotersweep] [] to analyse and process the information of over 4000 human promoter sequences. The data of a total of 29966 TF-binding sites was then stored in a MySQL database (DB) which we termed [Team:Heidelberg/HEARTBEAT_gui| HEARTBEAT] (Heidelberg Artificial Transcription factor Binding site Engineering and Assembly Tool, link). Based on frequency distributions (see results) for SREBP and VDR derived from HEARTBEAT, we were able to design 25 promoter sequences with different arrangement of TF-binding sites (see Promoter design).

VDR- as well as SREBP-responsive promoter constructs were first cloned into a reporter plasmid (BBa_K203100) and consequently transfected into MCF-7 and HeLa cells, respectively. The final screening was accomplished by TECAN and FACS measurements (results). Sentece about first results. According to our results, we think that our way of combining rational generation and experimental validation of synthetic constructs, provides a novel and effective strategy in synthetic biology.

To enable the construction of HEARTBEAT-based rational promoter sequences which comprises sequence assembly with TF-binding sites of choice, adding spacer sequences, checking for restriction sites and unintentional TFBS etc., we developed the HEARTBEAT graphical user interface (GUI link). Furthermore, we developed a computer model based on fuzzy logic, which is able to simulate the activity of the designed rational and random synthetic promoter sequences. In a reverse approach considering the output, the model helps the user in optimizing the input sequence. Altogether HEARTBEAT which comprises data analysis (HEARTBEAT-DB), a graphical user interface (HEARTBEAT-GUI) and network modeling (HEARTBEAT fuzzy network (FN)) provides a powerful and promising instrument for the synthetic biology community.

Background / Motivation

We present two different approaches for promoter design resulting in three different types of synthetic promoters: randomly assembled constitutive and inducible promoters as well as rationally designed promoters. As an additional type of promoters those occurring in nature can be integrated into vector systems. These heterogeneous cocktail of promoters can be combined for a precise regulation of pathways. This represents the power of our entire HEARTBEAT project. Synthesized promoters can be then used e.g. as a combinatorial gene therapy, i.e. several promoters that are of different types and/or have different strength will be applied as treatment agents. Therefore, a model that not only simulates single promoter activity and following gene expression but also accurately predicts gene expression from combined promoter sequences is indispensable.

We constructed a Fuzzy Logic model to provide a formal mathematical framework for prediction of combined activity of multiple promoters upon several stimuli and to gain insight into the mechanisms that generate diverse expression levels.

A Short Introduction into Fuzzy Logic Modeling

Fuzzy Logic is a rule-based approximate artificial reasoning method developed by [http://en.wikipedia.org/wiki/Lofti_Zadeh| Lotfi Zadeh] in 1965. Its motivation is the observation that humans often think and communicate in a vague way, and yet can make precise decisions [10]. It has been widely used in engineering and Artificial Intelligence approaches such as Fuzzy Controllers and Fuzzy Expert Systems. Fuzzy Logic has also been used for the modeling of biological pathways [11] and very recently to analyze gene regulatory networks [12]. Key advantages of Fuzzy logic-based approaches are (i) the ability to construct models based on prior knowledge of the system and experimental data and (ii) encode intermediate states for inputs and outputs, thus improving other logic-approaches that can only deal with ON/OFF states such as Boolean models [13] and (iii) simulations can be derived from both qualitative and quantitative data, both of which can be cast into the form of IF-THEN rules. Thus, FL constitutes a powerful approach for the understanding of heterogeneous datasets.

A Model combining XXX (2 nice words for prediction of HB DB) with experimental data

In our project, the complete set of rules will capture the behavior of each promoter in a Multiple-Input Single-Output (MISO) Fuzzy Logic model. Combining the MISO models in a network of all promoters will constitute the final Multiple-Input Multiple-Output (MIMO) model allowing for the simulation and prediction of combined activation of pahways regulated by our promoters. A key advantage of this methodology towards understanding the exclusive pathway activation of our promoters of interest is the possibility to study not only the individual activity of each promoter but also the combined activity, as the signal progresses from one MISO to another.

Achievements

Model description

blah

References

[1] Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99-104 (2004).

[2] Hu, Z., Killion, P. J. & Iyer, V. R. Genetic reconstruction of a functional transcriptional regulatory network. Nature Genet. 39, 683-687 (2007).

[3] Gertz, J., Siggia E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457. 215-218 (2009)

[4] Roider, H. G. et al. Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics 23, 134-141 (2006)

[5] ref to come

[6] Andianantoandro, E. et al. Synthetic biology: new engineering rules for an emerging discipline. Mol Sys Biol (2006)

[7] Alberts, B. et al. Molecular Biology of the Cell, 5th edition. Garland Science, 2008, Chapter 6

[8] Vardhanabhuti, S., Wang, J. & Hannenhalli, S. Position and distance specificity are important determinants of cis-regulatory motifs in addition to evolutionary conservation. Nucl Acid Res 35, 3203-3213 (2007).

[9] Yokoyama, K. D., Ohler, U. & Wray, G. A. Measuring spatial preferences at fine-scale resolution identifies known and novel cis-regulatory element candidates and functional motif-pair relationships. Nucl Acid Res, 1-21 (2009)

[10] Nelles, O. Nonlinear System Identification. Springer, 2000.

[11] Bosl, W. J. BMC systems biology 1, 13 (2007).

[12] Mathematical modeling of the lambda switch:a fuzzy logic approach.

[13] B. B. Aldridge, J. Saez-Rodriguez, J. L. Muhlich et al., PLoS computational biology 5 (4), e1000340 (2009).