Systems Biology of Cancer: A Challenging Expedition for Clinical and Quantitative Biologists

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression

An Integrated Computational Pipeline to Construct Patient-Specific Cancer Models

Precision oncology largely involves tumor genomics to guide therapy protocols. Yet, it is well known that many commonly mutated genes cannot be easily targeted. Even when genes can be targeted, resistance to therapy is quite common. A rising field with promising results is that of mathematical biology, where in silico models are often used for the discovery of general principles and novel hypotheses that can guide the development of new treatments. A major goal is that eventually in silico models will accurately predict clinically relevant endpoints and find optimal control interventions to stop (or reverse) disease progression. Thus, it is vital to develop an ecosystem of researchers to optimize the model creation and analysis pipeline. The modeling pipeline posited within this dissertation (dubbed the Synergistic Model Acquisition and Target Analysis (SMATA) pipeline) includes equation learning (to develop topological network communications and functions), attractor analysis, application of phenotype control theory, and simulation of suggested targets. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. As such, we apply these strategies to one of the most detrimental cancers, Pancreatic Ductal Adenocarcinoma (PDAC). While any cancer diagnosis is life-altering, pancreatic cancer is among the most discouraging to receive because of its extreme difficulty to overcome. Using our pipeline, we were able to corroborate previous publications and even make some new promising discoveries

In silico clinical trials through AI and statistical model checking

A Virtual Patient (VP) is a computational model accounting for individualised (patho-) physiology and Pharmaco-Kinetics/Dynamics of relevant drugs. Availability of VPs is among the enabling technology for In Silico Clinical Trials. Here we shortly outline the state of the art as for VP generation and summarise our recent work on Artificial Intelligence (AI) and Statistical Model Checking based generation of VPs

Modeling formalisms in systems biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)

On the origins and rarity of locally but not globally identifiable parameters in biological modeling

Structural identifiability determines the possibility of estimating the parameters of a model by observing its output in an ideal experiment. If a parameter is structurally locally identifiable, but not globally (SLING), its true value cannot be uniquely inferred because several equivalent solutions exist. In biological modeling it is sometimes assumed that local identifiability entails global identifiability, which is convenient because local identifiability tests are typically less computationally demanding than global tests. However, this assumption has never been investigated beyond demonstrating the existence of counter-examples. To clarify this matter, in this paper we began by asking how often a structurally locally identifiable parameter is not globally identifiable in systems biology. To answer this question empirically we assembled a collection of 102 mathematical models from the literature, with a total of 763 parameters. We analysed their identifiability, determining that approximately 5% of the parameters are SLING. Next we investigated how the SLING parameters arise, tracing their origin to particular features of the model equations. Finally, we investigated the possibility of obtaining false estimates. Some of the solutions that are mathematically equivalent to the true one involved parameters and/or initial conditions with negative values, which are not biologically meaningful. In other cases the true solution and the equivalent one were in the same range. These results provide insight about a previously unexplored hypothesis, and suggest that in most (albeit not all) systems biology applications it suffices to test for structural local identifiability.MCIN/AEI/ 10.13039/50110001103300004837 | Ref. PID2020-113992RA-I00MCIN/AEI/ 10.13039/50110001103300004837 | Ref. RYC-2019-027537-IXunta de Galicia | Ref. ED431F 2021/00

Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution

Cancer develops via the progressive accumulation of somatic mutations, which subvert the normal operation of the gene regulatory network of the cell. However, little is known about the order in which mutations are acquired in successful clones. A particular sequence of mutations may confer an early selective advantage to a clone by increasing survival or proliferation, or lead to negative selection by triggering cell death. The space of allowed sequences of mutations is therefore constrained by the gene regulatory network. Here, we introduce a methodology for the systematic exploration of the effect of every possible sequence of oncogenic mutations in a cancer cell modelled as a qualitative network. Our method uses attractor identification using binary decision diagrams and can be applied to both synchronous and asynchronous systems. We demonstrate our method using a recently developed model of ER-negative breast cancer. We show that there are differing levels of constraint in the order of mutations for different combinations of oncogenes, and that the effects of ErbB2/HER2 over-expression depend on the preceding mutations

Modelling timing in blood cancers

Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis. This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit