FST TCS 2007 Satellite Workshop
BioInformatics and Systems Biology

There has been a great upsurge of interest in Computational Biology, Bio-Informatics and Systems Biology in various computer science research groups in the country. This workshop aims at generating further interest among researchers and students in the problems being examined in the area. The presentations in the workshop will examine the problems posed by biology from the computing and mathematical analysis viewpoint.

Special Event: Felicitation of Sachin Maheshwari

IARCS will felicitate Prof. Sachin Maheshwari on the occasion of his 60^th birthday.

Schedule

• Sridhar Hannenhalli

  Deciphering Gene Regulatory Networks by in silico approaches

  Biological processes are controlled at various levels in the cell and while these mechanisms are poorly understood, transcriptional control is widely recognized as an important component and a better understanding of which will provide an efficient means for the therapeutic intervention in disease processes. We have been focusing on various computational problems pertaining to transcriptional regulation, namely, (1) representation and identification of transcription factor binding sites, (2) PolII promoter prediction, (3) Predicting interaction among transcription factors, (4) Transcriptional modeling, i.e. identifying arrangements of TFs that co-regulate a set of transcripts. I will present a brief overview of the computational approaches and challenges as well as a number of applications including transcriptional regulation in memory storage, heart failure, and osteoarthritis.
  
  

• S. Cenk Sahinalp

  taveRNA: A Combinatorial Toolkit for Predicting the Independent and Joint Structure of RNA Sequences

  Recent discovery of many families of non-coding RNAs (ncRNAs), with a variety of functions, has led to resurgent interest in the computational prediction of secondary and tertiary structures, both those formed by individual RNA sequences and those formed by a pair of interacting RNAs (e.g. a coding mRNA and a regulatory ncRNA). In this talk, we introduce taveRNA, Simon Frasier University's software suite for prediction of RNA structure and interaction.

  The taveRNA package currently includes three software tools:

  (i) alteRNA predicts the secondary structure of individual RNA sequences using an alternative to the popular approach of free energy minimization alteRNA performs structure prediction by minimizing a linear combination of the total free energy of the structure and its overall energy density.

  (ii) inteRNA predicts the joint secondary structure of two interacting RNA molecules by minimizing their total free energy. inteRNA comes in a number of flavors, including both a vanilla internal/external stack pair maximizer and a more complex loop optimizer.

  (iii) pRuNA scans a set of ncRNAs to identify those that can potentially form stable interactions with a given mRNA sequence. pRuNA, when combined with inteRNA, provides a powerful method for searching for ncRNAs that regulate a given mRNA.
  
  

• Sanjay Jain

  The dynamics of large scale biological networks and system level properties of E. coli cells

  The availability of genetic and other data of bio-molecular interactions on a large scale holds out the promise of understanding system level properties of living organisms from first principles. However, there are several obstacles in bridging the gap between microscopic information at the molecular level and system level phenomena at the organism level. First, our knowledge at the molecular level is only partial. Second, there is a paucity of theoretical and conceptual tools to predict complex system level phenomena that depend upon several microscopic components acting in concert, starting from the partial information that we currently possess at the molecular scale.

  In this talk I will describe some recent work that uses information in publicly available databases of genetic and metabolic networks of the bacterium E. coli to study the dynamics of these networks. Two computational techniques of systems biology will be reviewed: (a) the boolean dynamics of genetic regulatory networks, and (b) the flux balance analysis of metabolic networks. These two techniques are examples of methods that are helping in bridging the above mentioned gap. In conjunction with graph theory, when applied to the E. coli genetic and metabolic networks, these techniques help us to understand how, as a complex system, the cell is not only robust to perturbations of its genetic state, but is also at the same time flexible in responding to changes in its external environment. This work also brings out some of the unique design features of biological networks related to their control, modularity and crosstalk.
  
  

• Francois Fages

  Formal verification and inference of biochemical reaction models

  To a large extent, the biological properties of biochemical systems known from experiments can be formalized in temporal logics, both qualitatively and quantitatively. Such a formal specification of the behaviors of the system, under various conditions, opens the way to the use of automated reasoning tools, not only for validating models and their refinements (e.g., by model-checking technologies) but also for inferring parameter values and reaction rules from temporal properties. We report our experience in the design of the Biochemical Abstract Machine environment BIOCHAM and its use on models of signal transduction and of the cell cycle.
  
  
• P.S. Thiagarajan

  A Parameter Estimation Technique for Bio-Pathway Models

  The construction of dynamic models of bio-signaling pathways has drawn much attention recently. A serious barrier to making progress in this area are the unknown rate constants governing the various bio-chemical reactions. The point being, a signaling pathway can be viewed as a network of bio-chemical reactions and in practice, the values of many of the rate parameters governing these reactions will be unknown. Hence a major challenge is to develop algorithmic techniques for estimating the values of the unknown parameters of large intra-cellular bio-chemical networks.

  In the present talk, after surveying the background, we will present a decompositional approach that exploits the structure of a large pathway model to break it into smaller components. The parameter estimation problem can then be solved independently for the smaller components. This leads to significant improvements in computational efficiency.

Sponsors

This workshop is made possible due to the generous sponsorship of Microsoft Research India.

Organizers

• Neelima Gupta, Delhi University
• Sanjiva Prasad, IIT Delhi