Networks and Systems Biology

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
FIS;UPF
This course provides an introduction to dynamical systems and network analysis used in contemporary systems biology. The vast majority of cellular processes do not rely on the operation of a single component of the cell's machinery (e.g. a single gene or protein), but on the interplay between multiple components working together as a system. The goal of this course is to describe how this emerging behavior arises from the interaction between multiple cellular components, in the form of gene and protein circuits and networks. Given the presence of feedback loops in these circuits, their behavior cannot be predicted intuitively by tracking the state of the network components along their interaction paths. Due to these limitations, mathematical modeling is necessary to establish the range of possible behaviors that a cellular network can have, and the effect of perturbations (genetic or biochemical) on these behaviors. This course presents an overview of emerging phenomena that arise from cellular circuits and networks, emphasizing their mathematical description.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K4 - Integrate the concepts offered by the most widely used programming languages in the field of Life Sciences to model and optimize data structures and build efficient algorithms, relating them to each other and to their application cases.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

    • Skills

  • S1 - Integrate omics and clinical data to gain a greater understanding and a better analysis of biological phenomena.
  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S5 - Disseminate information, ideas, problems and solutions from bioinformatics and computational biology to a general audience.
  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

    • Competences

  • C2 - Identify the complexity of the economic and social phenomena typical of the welfare society and relate welfare to globalization, sustainability and climate change in order to use technique, technology, economy and sustainability in a balanced and compatible way.
  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C4 - Work as a member of an interdisciplinary team, either as an additional member or performing managerial tasks, in order to contribute to the development of projects (including business or research) with pragmatism and a sense of responsibility and ethical principles, assuming commitments taking into account the available resources.

    • Objectives

    1. Model biological information in mathematical language for further analysis and processing.
      Related competences: K2, K3, S1, S3,
    2. Understand and develop algorithms with computer languages.
      Related competences: K2, K3, K4, S2, S7,
    3. Critical thinking and problem solving skills
      Related competences: K1, K2, K3, K7, S5, S8, C2, C3, C4,

    Contents

    1. Cell biology by the numbers
      Introduction to systems biology. Back-of-the-envelope calculations in biology.
    2. Dynamical systems modelling of cellular regulation processes
      Introduction to dynamical systems theory. Gene expression and protein synthesis. Michaelis-Menten and Hill Equations.
    3. Network motifs in biology
      The negative feedback loop: robustness and homeostasis.
      The feedforward motif: pulse generation and adaptation.
      The positive feedback loop: bistability and memory.
    4. Biochemical oscillators
      Linear stability analysis. Design principles of biochemical oscillators: delayed negative feedback and amplified negative feedback.
    5. Noise in biological systems
      Transcriptional noise. Master equation. The chemical Langevin equation. The Gillespie Algorithm.
    6. Biological networks
      Introduction to network theory. Network topology. Random graphs. Percolation.
      Network inference from dynamical data.

    Activities

    Activity Evaluation act



    Final exam


    Objectives: 1 2 3
    Week: 18 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Midterm exam


    Objectives: 1 2 3
    Week: 8 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h


    Teaching methodology

    Lectures will be mainly of expository type. There will be also problem-based sessions and exercise sessions using Python.

    Evaluation methodology

    For the evaluation of the subject, the grade of the partial exam (P), the grade of the final exam (F) and participation to the problem-based learning sessions (PBL) will be taken into account through the following formula:

    Grade=max(0.3*P+0.6*F+0.1*PBL;0.1*PBL+0.9*F)

    A student is considered to have taken the subject if he/she takes the final exam and handed in all the practicals. If the student has taken the subject but has failed, then the student may take the re-evaluation exam (R) and in this case the grade for the subject will be the maximum between R and 0.1*PBL+0.9*R.

    Bibliography

    Basic

    Complementary

    Web links