Open Access
Dasika, Madhukar S
Graduate Program:
Chemical Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
April 04, 2007
Committee Members:
  • Costas D Maranas, Committee Chair
  • Reka Z Albert, Committee Member
  • Patrick C Cirino, Committee Member
  • Antonios Armaou, Committee Member
  • gene regulatory networks
  • signaling networks
  • Optimization
  • synthetic biology
<P>Nature has created a wide range of diversity in the form of microorganisms with sometimes less than 200 genes to highly complex biological entities such as humans. All these biological systems are driven by an underlying complex cascade of biochemical reactions. These reactions are often represented as “networks”; these networks come in different flavors such as Metabolic, Signaling and Gene Regulatory. It is now clear that development of systematic procedures to analyze biological networks will lead to advances in biotechnology and therapeutics. To this end, in this thesis we develop computational approaches which can serve as powerful tools in various stages of biological research and discovery. Specifically, this thesis is composed of four parts that address inference, topological analysis and redesign of biological networks. While complementary these approaches are not necessarily interlinked. </P> <P>The first part of this thesis focuses on development of a mathematical programming based framework to extract the underlying layer of complex component interaction networks using high-throughput biological data. Specifically, a mixed-integer linear programming based modeling and solution framework for inferring time delay in gene regulatory networks is developed. Solution of the model with real microarray data indicates that (i) The model predicts considerable number of interactions with a non zero value of time delay suggesting that time delay is prominent in gene regulation and (ii) Predicts a network that is more sparse and less sensitive to random fluctuations in gene expression, when time delay is accounted for.</P> <P>Subsequently, optimization based frameworks are introduced for elucidating the input-output structure and redesigning large-scale cell signaling networks for pinpointing targeted disruptions leading to the silencing of undesirable outputs in the context of therapeutic interventions. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exists two distinct types of outputs in the signaling network that either can be elicited by many different input combinations (i.e., degenerate) or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades.</P> <P>Next, we focus our attention to the development of computational tools construct and validate biological networks. Specifically, a discrete event based mechanistic simulation platform DEMSIM was developed for testing and validating putative regulatory interactions. The proposed framework models the main processes in gene expression, which are transcription, translation and decay processes, as stand-alone modules while superimposing the regulatory circuitry to obtain an accurate time evolution of the system. The stochasticity inherent to gene expression and regulation processes is captured using Monte Carlo based sampling. Overall, the results demonstrate the simulation framework’s ability to make accurate predictions about system behavior in response to perturbations and distinguish between different plausible regulatory mechanisms postulated to explain observed gene expression profiles. </P> <P>Finally, we explore how optimization based approaches can be used to construct synthetic genetic networks that exhibit a specific function. Specifically, we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ODE’s but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. The optimization framework is applied on a variety of applications ranging from the design of circuits that exhibit a specific time course response to circuits that discriminate between the presence, absence and level of external stimuli (e.g., genetic decoder). The results for the demonstrate the ability of the framework to (i) generate the complete list of circuit designs of varying complexity that exhibit the desired response; (ii) rectify a non-functional biological circuit and restore functionality by modifying an existing component and/or identifying additional components to append to the circuit. </P>