Mathematical and computational analysis of intracelluar dynamics

Chapter 1 Introduction Discoveries in the fields of molecular and cellular biology over the past few decades have shed light at the genetic and molecular level on the key intracellular

Chapter 1

Introduction

Discoveries in the fields of molecular and cellular biology over the past few decades have shed light at the genetic and molecular level on the key intracellular components involved in myriad biological processes spanning from fundamental gene expression

to major cellular physiology such as proliferation, differentiation, migration and death If a dogma were to describe the cellular approach of doing work, it would be

“every component is a middleman” or in other words, no intracellular component works independently By interacting among themselves through biochemical and/or biophysical mechanisms, intracellular components work in specific teams to achieve the stipulated goal(s) In general, these interactions generate rich and non-intuitive systems behaviors, often called emergent properties, which cannot be predicted from the properties of each intracellular component For instance, a living cell is an emergent property, which results from the dynamic interactions among the components that made up the cell that cannot be inferred by studying in isolation the properties of DNA, proteins, metabolites etc As a result, understanding of any biological process will be inadequate even with complete understanding of the involved intracellular components alone, so called reductionism approach This main shortcoming of reductionism is addressed in systems biology approach At the core of this approach, intracellular components involved in the biological process under studied are considered concurrently in which the dynamics or time-courses of the components due to their interactions are analyzed Nonetheless, the reductionism

approach is complementary to systems biology approach because the former is useful

in the discovery of intracellular components

A key primary goal of systems biology is to understand, explain and predict counter-intuitive biological phenomena by considering interactions among cellular components that are involved in a particular biological process as a whole As this approach is often associated with complexity that arises from the analyses of interactions among components that are often nonlinear and coupled, the resultant interaction dynamics confound our innate processing ability Therefore, mathematical and computational models that approximate the studied biological systems are constructed In the models, mathematical expressions are used to describe the interactions after which they are solved either analytically or for most cases, numerically by computer simulations to obtain the intracellular dynamics In addition, as will be illustrated in this thesis, modeling can be useful to solve biological problems where an experimentation approach is impractical because of existing technical limitations Nevertheless, as complexity of the interactions scales exponentially with total number of components in the model, solving for the model dynamics becomes computationally intractable for relatively large systems

In this thesis, systems biology approach is used to explain observed cell physiologies and make novel predictions by analyzing intracellular dynamics based

on the known interactions among intracellular components using mathematical and computational models Examples of intracellular dynamics include the change with time of the quantity, biochemical activity, secondary structures, localization and etc of

level understanding of the biological process and elucidating of the systems behaviors can then be obtained from the insights of such analyses The dynamics of two intracellular processes are analyzed in Part I and Part II of the thesis, as outlined below

The first system is the p53-AKT genetic network regulating cell survival and death that constitutes a connected network of cancer-relevant genes (Part I: Chapters 2

to 5) The key tumor suppressor protein, p53, and the oncoprotein, AKT, are involved

in a cross talk that could be at the core of a cell’s control machinery for switching between survival and death This cross talk is a combination of reciprocally antagonistic pathways emanating from p53 and AKT that also involves another tumor suppressor gene, PTEN, and another oncogene, MDM2 This mutual antagonism is the more significant when one observes that p53 and AKT have opposite effects on apoptosis, a form of programmed cell death; a review of the relevant pathways regulating the AKT network as well as the regulation of apoptosis by the p53-AKT network is given in Chapter 2 Therefore, a critical study of the p53-p53-AKT pathway can lead to the understanding of how cells regulate the switch between cell survival and death Various p53-AKT models, which are developed in Chapter 3 using the known pathway interactions, are shown to possess the potential to exhibit bistability, a phenomenon in which two stable steady states of the system coexist for the same set of control parameter values (e.g extent of DNA damage); the two states correspond to pro-survival and pro-death cellular states This work has been published in Wee and Aguda (2006), as attached in Appendix A-1

Interestingly, intracellular protein levels of p53 and MDM2 have been observed in single-cell experiments of specific cell types and in mouse organs to oscillate in response to ionizing radiation (IR) (reviewed in Chapter 4) The putative cause of the oscillations, the p53-MDM2 negative feedback loop, is embedded in the p53-AKT network studied in the preceding chapters The biological roles and consequences of such oscillations in the control of the cell survival and death switch are studied in Chapter 5 Besides reproducing p53-MDM2 oscillations that matched experimental observations, the model predicts several biological consequences of cells manifesting p53 oscillations The most significant prediction is that p53 oscillations sensitize cells towards apoptosis upon IR This work has been submitted for publication (Wee and Aguda, 2008b)

The biological system studied in Part I is a typical genetic network in which proteins interact among themselves through various regulatory feedback loops Fascinatingly, the process of gene transcription, which is ubiquitous in any genetic network, is in itself no less dynamic than the genetic network it belongs to In particular, due to the coupling of intron splicing to pre-mRNA elongation during the process of gene transcription, the processing of pre-mRNA to mature mRNA is dynamic As a result, pre-mRNA secondary structures are dynamic during transcription, and the consequences on the efficiency of pharmacological molecules to remove genetic mutations in the dystrophin pre-mRNA, a potential therapeutic strategy for Duchenne muscular dystrophy, is the subject of study in Part II of the thesis

The second system studies the consequences of co-transcriptional coupling of

pre-mRNA elongation with intron splicing (Part II: Chapters 6 and 7) Specifically,

Chapter 6 examines the consequences of co-transcriptional coupling between

transcription and splicing on AON-mediated exclusion of specific exons in the dystrophin pre-mRNA to correct the genetic mutations involved, which is a potential therapy for Duchenne muscular dystrophy (DMD); AONs or antisense-oligonucleotides are synthetic single-stranded molecules that are complementary to a specific sequence in the target mRNA Because DMD is linked to multiple genetic mutations involving many different exons of the large dystrophin gene, AONs to induce exon removal have to be customized for the individual patient according to the mutation involved Hence, a systematic way to design effective AON target sites is seriously in need for personalized therapy To do so however, major factors affecting AON efficiency must be determined

AON can only bind to a target site when the nucleotides within the target site

do not form complementary base-pairings with other nucleotides within the

pre-mRNA As splicing is co-transcriptional, AONs must compete with splicing factors

to bind to their target sites during transcription However, co-transcriptional folding

of the pre-mRNA might alter the secondary structure of these sites, which in turns

changes the co-transcriptional binding accessibilities of the AON target sites To test the hypothesis that co-transcriptional binding accessibilities of target sites is a key

factor determining AON efficiency, a model that takes into account of the

implications of co-transcriptional effects is developed in Chapter 7 to approximate

the transcriptional process around the vicinity of the target exon The model is motivated by the spirit of systems biology as it considers the implications of the

coupling between transcript elongations and processing that requires interactions among myriad cellular components, i.e., transcription is not a standalone process

Analyses show that dynamics of co-transcriptional binding accessibilities of AON

target sites could statistically correlate with efficacy and efficiency of 94% of previously reported AONs The use of computational modeling here overcomes the difficulty in tracking the transcriptional process in the laboratory This work has been

published in Wee et al (2007, 2008a), as attached in Appendix A-1